Administración de riesgos financieros ARF.

Course syllabus. Fall 2025. Currently under review.

Abstract

A course syllabus is an academic document containing relevant information for students. This document serves as your ultimate guide to successfully completing this course. Please read it regularly and carefully. Within this document, you will find a comprehensive description of the course goals, rules, objectives, recommendations, grading details, a detailed schedule, activities, resources, references, and learning outcomes. The objective of this document is to provide you with complete certainty regarding what to expect from this course, starting from day 1 until the final exam. Based on my experience, reading this syllabus is positively correlated with students’ performance.

Back to Quantitative Finance with

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1 Welcome.

Welcome to the course! My name is Martín. I hope this message finds you and your loved ones in good health.

1.1 Learning formats.

This syllabus applies to all delivery modes: face-to-face, blended, or fully online.

Distance learning refers to educational formats where students and instructors are not physically in the same space. Based on my experience, these courses can be just as effective as traditional in-person classes. While you may have personal preferences, I encourage you to approach all formats with flexibility. Employers increasingly value adaptability, especially the ability to thrive under changing circumstances and embrace new ways of learning and working.

My first experience with distance education was in 1999, and I have been engaged with it ever since, both as a postgraduate student and as a professor. My research network includes colleagues in Chile, Ireland, the UK, Italy, and Spain. Although we meet in person only once a year, we collaborate constantly through virtual means.

Knowing how to work effectively in online environments is just as important as doing so face-to-face. I hope my experience with various teaching formats will support your success in this course. Welcome again!

1.2 About me.

My full and updated curriculum vitae, is here.

I am currently a professor of finance and economics at UDEM. I regularly collaborate with the University of Manchester through the Alliance Manchester Business School and with SOAS University of London via the Centre for Financial and Management Studies (CeFiMS).

Areas of expertise. Finance, Economics, Statistics, Data Science.

Research interests. Empirical asset pricing; beta and SDF pricing models and tests; financial econometrics; GMM estimation and inference; portfolio allocation models and performance; computational finance; data science applications in business.

Education:

• Postdoc in Finance – University of Manchester.
  
• PhD in Quantitative Finance – University of the Basque Country (Doctor Europaeus distinction from the European University Association).
• Four MSc degrees in: Statistical Learning, Modern Applied Statistics, Quantitative Finance, and Finance.
• Three University Expert diplomas in: Statistical Learning, Applied Statistical Methods, and Advanced Statistics – from EGADE and UNED.
  
• BS in Economics – Tecnológico de Monterrey.

I have also completed more than 20 professional training programs in various areas, including data science, sustainable finance, migration, and innovation. These programs were offered by prestigious institutions such as Strathclyde Business School, University of Bath, Duke University, RISIS Research Infrastructure for Science and Innovation Policy Studies, Università della Svizzera Italiana, AIT Austrian Institute of Technology, The Alan Turing Institute, Universidad Complutense de Madrid, Manchester Institute of Innovation Research, among others.

Research. I am a researcher in the area of quantitative finance. I have held a couple of full-time research positions as a pre-doctoral Marie Curie research fellow supported by the Sixth European Community Framework Programme, and a post-doctoral research fellow position both at The University of Manchester, Alliance Manchester Business School, and the Centre for the Analysis of Investment Risk. My research has been published in 3-star journals according to The Chartered Association of Business Schools, including Journal of Empirical Finance, Quantitative Finance, and Journal of Financial and Quantitative Analysis (research assistance). My research has been presented in numerous research seminars in the UK, Spain, Mexico, Brazil, Sweden and Ireland. My research has also been presented in prestigious international conferences including the Spanish Association of Finance Forum; Eastern Finance Association (USA); World Congress of the Econometric Society (Italy); French Finance Association; and Econometric Research in Finance among others. I collaborate as reviewer and editor for several academic journals in the areas of finance.

Teaching. I have been a lecturer in economics, finance and data science for under and postgraduate levels at different universities in Mexico and the UK for the last 25 years. Also, I have supervised more than 100 dissertations at under and postgraduate academic programs of schools including the London School of Business & Finance; University of London, SOAS University of London; The University of Manchester; Universidad Complutense de Madrid, UDEM, among others. Also, I have experience in continuous education, consulting, and executive training in the area of finance.

Beyond academics, I have a passion for art, especially music and painting. I enjoy playing my Yamaha digital piano and have performed in concerts across Mexico and Europe. I have served as a keyboardist, pianist, and orchestra director, including more than a decade of musical productions with Tecnológico de Monterrey (Monterrey Campus).

1.3 Teaching philosophy.

I aim to teach the kind of course I would be eager to take as a student. My perspective has been shaped by more than 25 years of experience as both a student and researcher at several universities in Mexico and Europe. These experiences have given me deep insights not only in my field of expertise but also in diverse educational approaches and learning techniques across different academic cultures. This dual exposure has taught me the importance of combining theory with practice. Relying only on theory can limit the applicability of knowledge, while focusing only on practice can weaken the understanding of core principles. A thoughtful mix of both is essential for meaningful learning.

In my courses, I place strong emphasis on the use of data and computer programming to develop practical skills and a research-oriented approach to solve business problems. This emphasis is not simply a personal preference, but a reflection of the growing demands of the job market. I understand that not all students enjoy learning computer programming, but it has become an essential skill across disciplines, and the university is committed to helping students develop it as part of their professional training. I seek to integrate current technologies and innovation tools so that students are prepared not only for today’s challenges but also for the ones they will face in the near future.

The use of AI tools is not prohibited in my courses, as I believe it is both impractical and counterproductive to ban them in today’s academic and professional environment. Students are expected to become familiar with these tools and learn how to use them effectively. However, this does not mean their use should be indiscriminate or unquestioned. In my experience, AI does not typically provide correct or meaningful answers to the kind of exam questions I design, which require specific conceptual understanding and critical thinking. Nonetheless, these tools can be useful for learning, exploring ideas, and improving the quality of projects when used responsibly and thoughtfully.

I also recognize that students themselves bring varied attitudes and circumstances to their learning. I believe that most students are genuinely interested in the subject matter, although I understand that not everyone comes to class under the same circumstances. Some may not enjoy the topic, others may be navigating difficult personal situations, and some may simply be struggling to stay engaged. I try to be mindful of this while maintaining high academic standards. I care about students’ mental health and well-being, and I do not believe that learning should come at the expense of anyone’s stability or health.

I do not believe there is such a thing as a “bad student.” I believe all students have potential and ability, though they may face different challenges and arrive with different levels of preparation or support. My job is not to judge students by their difficulties, but to meet them where they are and help them move forward. At the same time, I also believe that earning good grades or performing well in a course is not the same as being a good person, and that the latter is far more important. Education is a powerful tool for personal and professional growth, but it should also cultivate empathy, integrity, and other higher values.

That said, learning is a shared responsibility. My role is to provide structure, guidance, and support, but students are expected to attend class, follow the rules, and engage actively with the material. They need to prepare, participate, and take ownership of their learning. I provide detailed plans for every class so students know exactly what to read, deliver, and when assignments are due. I design assessments not only to evaluate performance but also to reinforce understanding. I value clarity and fairness in grading, and I offer feedback as a tool to support improvement and critical thinking. While I maintain fixed course standards that all students are expected to meet, I provide the necessary support to help them reach these requirements. Clear communication is essential for the success of the course. I encourage students to contact me with academic questions or other concerns that may affect their learning. I am here to support their progress and to help them stay on track.

This collaborative approach works best within a structured framework. Our course operates within a clear set of rules and regulations established in the syllabus and official university documents. While I value communication and acknowledge personal and emotional challenges, I am also committed to applying these rules consistently and fairly. These guidelines exist to ensure transparency, fairness, and academic integrity. We are not expected to change or adapt the rules based on individual circumstances, and I believe it is important to uphold this principle for the benefit of the entire class.

I strive to maintain a professional teaching approach while remaining approachable and supportive. I love my profession, I enjoy what I do, and I am always willing to help students learn and develop relevant skills. I do not try to entertain or to be friends, but I do aim to maintain a respectful and cordial relationship that supports our ability to meet the course objectives in a constructive and professional manner.

Within this professional framework, I welcome and respect the diversity of student backgrounds, experiences, and perspectives. This diversity enriches the learning environment and helps prepare us for working in a global and interconnected world. At the same time, I believe it is important to distinguish between valuing diverse viewpoints and maintaining academic rigor. While a range of perspectives is encouraged in class discussions, this does not mean that all answers are equally valid from a technical or disciplinary standpoint. Sound reasoning, evidence, and established knowledge remain essential. Education should promote open dialogue, but it must also be grounded in facts, analytical thinking, and a shared commitment to learning. I also encourage students to be aware of the broader social and environmental context of our actions. In my view, education should not only build knowledge and skills but also reinforce values of responsibility, including care for the environment we all share.

I believe that professors and students are not competitors; we are collaborators working toward a shared goal: learning. When a disagreement arises, I don’t see it as a conflict. Instead, I treat it as an opportunity for dialogue. Sometimes I may be wrong, and I try to correct my mistakes openly and promptly. Other times, I may need to explain to the student, using clear reasoning, subject knowledge, or university regulations, why their understanding might not align with course expectations. I understand that my role comes with institutional authority, and I take seriously the responsibility to use that authority fairly. I try to create an environment where students feel heard, even when we disagree, and where they can trust that decisions are based on principles, not personal judgment. Disagreements don’t have to become confrontations. With open communication and mutual respect, they can become part of the learning process itself.

As I reflect on these principles, I’m mindful that higher education serves multiple purposes and represents different things to different people. Particularly in fields like business, finance, and economics, education is not only an academic pursuit but also a strategic investment. Whether tuition is paid by the student, their family, or a sponsoring institution, there is an expectation of value and return. As students and instructors, we have a shared responsibility to treat this process with the seriousness it deserves. That said, it’s also important to remember that education, while valuable, is not the only, or the most important, aspect of our lives as individuals. Our health, our relationships, and our character and values matter even more. I hope my students leave the classroom not only with stronger skills but with a deeper sense of what really matters.

2 Overview.

This section includes the course objectives, mechanics, and important information about academic integrity.

2.1 ARF description.

Financial risk management involves the identification, quantification, and mitigation of financial risks. In this course, our focus is on credit and market risk models and their practical implementation using R and Python.

Credit risk refers to the potential loss arising from a borrower’s failure to make payments on their debt obligations. Managing credit risk involves measuring the probability of default and the associated potential losses to minimize financial losses. While traditional courses often concentrate on studying firms, this course also incorporates the analysis of credit risk at the individual and country levels, providing a broader perspective and enabling the application of financial and economic analysis. On the other hand, market risk refers to the potential for financial losses arising from adverse movements in market prices, such as the prices of stocks, bonds, currencies, commodities, and other financial instruments. This risk is inherent in the dynamic nature of financial markets, where prices are influenced by various factors such as economic indicators, geopolitical events, interest rates, and overall market sentiment.

The main textbooks recommended for this course are: Hull (2015), which covers influential credit risk models widely used in the financial industry; Drake and Fabozzi (2010), Crouhy, Galai, and Mark (2014) and Brealey et al. (2020), which provide comprehensive analyses of various sources of risk in financial markets; Lozano (2024a) and Lozano (2024b), which bridges the gap between theoretical model analysis and practical computational modeling using R. Complementing these resources, Hull (2020) and James et al. (2021) are recommended as additional reference books.

Listen to this Spotify podcast to learn more about financial risk.

Please refer to the course calendar provided at the end of this syllabus for further details about the topics, activities, and required readings: Section 7.

2.2 Objectives.

The main objectives of this course are threefold:

1. Understanding course topics. The course aims to ensure that you have a solid understanding of the topics covered. This includes gaining knowledge of various models and techniques in the areas of financial economics and quantitative finance. You will delve into the concepts, theories, and principles underlying these subjects.

2. Applying quantitative techniques. The course emphasizes the application of quantitative techniques and models. You will learn how to implement these models by translating the original paper-based formulations into functional computer code. Through practical exercises and assignments, you will gain hands-on experience in using computational tools to analyze financial data, generate insights, and interpret results.

3. Developing financial competences. The course is designed to help you develop the necessary competences to excel as a financial professional, practitioner, or junior researcher. By engaging in the learning activities and utilizing the available resources, you will enhance your professional skills in financial analysis, modeling, and reporting. The course aims to equip you with the practical abilities and knowledge required to thrive in the field of finance.

Overall, the course provides a comprehensive learning experience that combines theoretical understanding with practical application. It aims to prepare you for a successful career in finance by fostering your proficiency as a financial professional and enabling you to contribute as a practitioner or junior researcher.

To achieve the objectives of the course, you are required to undertake various tasks throughout the semester. These tasks can be summarized as follows:

1. Reading. You are expected to engage in extensive reading to grasp the concepts and theories covered in the course. This involves studying the recommended textbooks, academic papers, and other relevant resources provided. Reading is fundamental for understanding the subject matter.

2. Hard work. Success in the course will require dedicated effort and hard work. You need to actively participate in lectures, complete assignments and projects, and actively engage with the course material. It is important to invest sufficient time and energy into mastering the concepts and techniques taught.

3. Seeking assistance. Should you encounter difficulties or have questions, it is encouraged to reach me out for assistance <martin.lozano@udem.edu>. I can help you to clarify concepts, resolve doubts, and ensure a deeper understanding of the material. I am available to support your learning journey.

It is worth noting that the provided pseudocode below is a simplified representation used for illustration purposes. While it may not directly run in R, it serves as an example to demonstrate the learning process. The actual learning process will be more intricate and involve a comprehensive exploration of the course topics, hands-on exercises, and practical applications.

# Initial condition.
learn <- FALSE
# Initial inputs assigned to study the topic.
inputs <- c(read, time, effort, assistance, others)
# Learning process.
while (learn == FALSE) {
  understand <- study(inputs) # Evaluate study function.
  if (understand == FALSE) {
    print("Add more inputs and try again.")
    inputs <- inputs + 1
    }
  else { # Understanding is the way to learn.
    learn == TRUE
    print ("Well done!")
  }
  }

Remember, persistence and determination are key to the learning process. Persistence refers to the quality or ability to continue striving towards a goal or completing a task despite facing difficulties, obstacles, or setbacks. It involves determination, perseverance, and the willingness to keep working towards a desired outcome, even when faced with challenges or discouragement. Persistence often requires a strong sense of motivation, resilience, and the ability to maintain focus and effort over an extended period of time. It is a valuable trait that can contribute to personal and professional success in various areas of life. By dedicating yourself to reading, working hard, and seeking assistance when needed, you will enhance your understanding and excel in the course.

2.3 Mechanics.

The course operates on the assumption that you will approach it with enthusiasm and a positive attitude, demonstrating a willingness to actively engage with the material and meet my expectations as professor. The following expectations are outlined:

1. Active participation. You are expected to be proactive and engaged throughout the semester. This includes completing the learning activities, assignments, and readings in a timely manner. Your active participation will contribute to a deeper understanding of the subject matter.

2. Understanding and application. The goal is for you to comprehend the course material and its practical applications to the best of your ability. This entails studying the subject matter thoroughly, grasping the underlying concepts, and being able to apply them in relevant contexts.

3. Course calendar. The course calendar, provided at the end of the syllabus Section 7, serves as a roadmap for the entire semester. It outlines the lecture topics, required readings, and upcoming activities, assignments, and exams. It is important to familiarize yourself with the course calendar and adhere to its timeline to ensure a smooth progression throughout the semester.

4. Time management. Given that some tasks may require more than a week to complete, it is crucial to manage your time efficiently. By planning ahead and allocating sufficient time to each task, you will be able to fulfill your course responsibilities effectively.

In this course, it is expected that you approach the required material proactively and conscientiously. This means dedicating time to studying the material in advance and engaging in independent practice. By doing so, you will create the necessary conditions to actively contribute to class discussions by providing valuable comments and asking thought-provoking questions.

Conscious and proactive study habits are important for several reasons. First, it allows you to stay on track with the course material and prevents you from falling behind. It ensures that you are familiar with the topics being covered and have a solid foundation to build upon during class. Moreover, engaging in advance study and practice enables you to take full advantage of the learning opportunities provided in the course. By actively exploring the material on your own, you can deepen your understanding, reinforce key concepts, and gain additional insights. This will enhance your learning experience and maximize the potential knowledge you can gain from the course.

On the other hand, if you choose not to engage in conscious study and practice, or you simply cannot given your current situation, you may find yourself struggling to keep up with the course requirements and may experience frustration. Falling behind can lead to a loss of interest, lower grades, and potentially even failure. Then, in order to mitigate the risk of failing the course and to optimize your learning outcomes, it is strongly recommended that you commit to consciously studying the required material in advance and engaging in independent practice as much as necessary. By doing so, you will be better prepared, actively participate in class, and increase your chances of success in the course.

In this video, students from The University of Melbourne share great tips and strategies about how they get the most out of university lectures.

In accordance with some of The Sustainable Development Goals by the United Nations, see UN (2015), all the compulsory readings and activity submissions are available in electronic format. Other complementary materials and activities may be incorporated or altered during the semester, depending on relevant news or events that do not currently exist or are hard to anticipate at the beginning of the semester. If that is the case, I will inform you in advance. As a student, you should be confident that all the course materials and activities are perfectly suitable for an undergraduate student enrolled in a prestigious world-class university. In other words, there will not be an intellectual challenge that you cannot overcome with the appropriate amount of enthusiasm, time, work, determination, and, if necessary, assistance. This course is designed in such a way that you can pass and learn as long as you invest the right amount of time and work.

2.4 Academic integrity.

Individuals with integrity demonstrate a high level of moral character and ethics, choosing to do what is right even when faced with difficult decisions or temptations. They are trustworthy, reliable, and have a strong sense of accountability. Integrity is an essential quality in personal relationships, professional settings, and society as a whole, as it fosters trust, respect, and fairness.

We pledge to uphold the highest standards of honesty and integrity, both for ourselves and our peers. Violations of academic integrity, including plagiarism and cheating, are strictly prohibited and may result in consequences such as failure of assignments, failure of the course, and additional disciplinary actions as per current regulations. Plagiarism is defined as presenting another person’s thoughts or words as one’s own, without proper acknowledgment or attribution. This includes copying from textbooks, other sources, including the Internet, generative AI tools, or any material without giving due credit to the original source.

Please watch the following video about plagiarism taken from York St. John University about understanding plagiarism.

I strongly advise against constructing a piece of work by simply cutting and pasting or copying material written by others into something you submit as your own. Regardless of any pressure you may feel to complete an assignment, it is important to never succumb to the temptation of taking a shortcut and using someone else’s material inappropriately. This includes ChatGPT and other generative AI tools as this is a form of plagiarism with potentially serious consequences.

See the following video to illustrate this point:

Ethical behavior is implicit in the course mechanics and rules, and it will be explicit in several topics throughout the course material. Following the indications of this syllabus is a simple way and a clear example regarding how we can effectively pursue ethical behavior. Ethics concerns are inherent in business, economics and finance activities because the professionals in these fields frequently manage resources to achieve a range of objectives, not exclusively maximize profits. Pursuing ethical behavior also helps us to build solid institutions, which is consistent with the United Nations 17 sustainable development goals, UN (2015). Managing own or third-party resources entails a high degree of responsibility because people often face the alternative to apply unethical strategies to achieve their own interests. A proper discussion of the ethical aspect in the decision-taking process including conflict of interests is necessary to increase the awareness of young professionals like you. In the end, following an ethical code as a business practice can contribute to strengthening or building your own reputation – one of the most significant assets you have, or are currently building.

Take a look at this video about integrity.

Integrity is particularly crucial in the realm of finance professionals due to the following reasons:

1. Trust and credibility. Finance professionals handle sensitive financial information, make critical financial decisions, and often act as stewards of other people’s money. Integrity is essential to establish and maintain trust and credibility with clients, investors, and stakeholders. Clients need to trust that their financial advisor or accountant will act in their best interests, provide accurate information, and handle their funds responsibly.

2. Ethical responsibility. Finance professionals have a significant ethical responsibility to act in an ethical and responsible manner. They must adhere to ethical codes of conduct, such as those set by professional organizations, and follow legal and regulatory requirements. Acting with integrity ensures that financial professionals prioritize ethical behavior, avoid conflicts of interest, and make decisions that are in the best interest of their clients or organizations.

3. Confidentiality and privacy. Finance professionals often have access to confidential financial information, including personal and sensitive data. Integrity is crucial in maintaining the privacy and confidentiality of this information. Professionals must handle financial data responsibly, protect client information, and maintain the highest standards of confidentiality and data security.

4. Risk management. Financial decisions and transactions involve risks, and integrity plays a critical role in managing these risks. Acting with integrity helps finance professionals accurately assess and communicate risks to clients or stakeholders. It involves being honest about the potential risks involved in investments, disclosing relevant information, and ensuring transparency in financial reporting.

5. Compliance and regulatory requirements. Finance professionals must adhere to various legal and regulatory requirements in their work. Integrity is essential in complying with these regulations, reporting accurate financial information, and avoiding fraudulent or deceptive practices. Acting with integrity helps professionals maintain compliance with laws such as anti-money laundering regulations, financial reporting standards, and tax laws.

6. Professional reputation. The finance industry relies heavily on reputation and credibility. Finance professionals with a reputation for integrity are more likely to attract clients, secure partnerships, and build long-term relationships. Conversely, a lack of integrity can quickly damage a professional’s reputation and have long-lasting negative consequences.

7. Industry ethics and public trust. The finance industry plays a significant role in the economy and society at large. Maintaining integrity within the industry is crucial for public trust and confidence in the financial system. Finance professionals with integrity contribute to the overall reputation and trustworthiness of the industry, helping to foster a healthy and reliable financial environment.

In summary, integrity is vital for finance professionals to establish trust, uphold ethical standards, protect confidentiality, manage risks, comply with regulations, maintain a strong professional reputation, and contribute to the overall integrity of the finance industry.

2.5 Sustainable finance.

I am certified as ‘carbon literate’ by the UN Climate Change Conference and Coventry University. The certification represents a robust understanding of the climate context and a commitment to recognizing ways to adjust our behavior to reduce our carbon footprint, as well as influencing our social and professional circles. My view is that one way of achieving a positive impact on the environment and society is to learn more about sustainable finance.

According to the European Commission, sustainable finance refers to the process of taking environmental, social and governance (ESG) considerations into account when making investment decisions in the financial sector, leading to more long-term investments in sustainable economic activities and projects. Environmental considerations might include climate change mitigation and adaptation, as well as the environment more broadly, for instance the preservation of biodiversity, pollution prevention and the circular economy. Social considerations could refer to issues of inequality, inclusiveness, labour relations, investment in human capital and communities, as well as human rights issues. The governance of public and private institutions – including management structures, employee relations and executive remuneration – plays a fundamental role in ensuring the inclusion of social and environmental considerations in the decision-making process.

In this course, you will be exposed to an introduction about sustainable finance, climate change and green economy. In particular, you will acquaint yourself with the basic skills and tools for applying the sustainable finance mechanisms to a real-world policy or business context.

Learning materials are taken from The One UN Climate Change Learning Partnership UN CC:Learn, which is a joint initiative of more than 30 multilateral organizations helping countries to achieve climate change action both through general climate literacy and applied skills development. UN CC:Learn provides strategic advice and quality learning resources to help people, governments and businesses to understand, adapt, and build resilience to climate change.

See the earth’s global average surface temperature in 2021 tied with 2018 as the sixth warmest year on record, according to an analysis by NASA:

Learning about sustainable finance is important as it enables individuals and organizations to make informed financial decisions that consider environmental and social factors, manage risks, comply with regulations, meet investor demand, and contribute to a more sustainable and responsible financial system.

3 Data science.

Data science is the study of extracting generalizable knowledge from data. Being a data scientist requires an integrated skill set that encompasses operations research, statistics, and computer science, along with a solid understanding of formulating problems in specific fields to achieve effective solutions. This course aims to introduce you to this rapidly growing field and equip you with its basic principles, tools, and general mindset within the context of business. Ideally, you will learn to apply financial and economic concepts, models, techniques, and tools to analyze various aspects of data science practice. This includes data collection and integration, exploratory data analysis, descriptive and predictive modeling, visualization, evaluation, and effective communication. For a comprehensive introduction to the application of data science in finance, I recommend referring to Hull (2020).

Listen to this podcast broadcasted on December 2023 to learn more about my view on this topic.

In this course, the goal is not to become a data scientist, but rather to lay the foundation for further specialization in this field through postgraduate studies. Nowadays, many undergraduate students recognize the need for basic knowledge in data science and machine learning to thrive in a world where these areas have an increasing impact on job opportunities. In the past, computer literacy was essential for all executives. Today, executives are expected to be comfortable managing large data sets and collaborating with data science professionals to drive innovation and enhance productivity.

Learning these computational skills aligns with the purpose of developing STEAM (Science, Technology, Engineering, Arts, and Mathematics) skills during your undergraduate studies in business. For more information on STEAM skills, you can refer to Boon Ng et al. (2019). In my opinion, learning opportunities for undergraduate students should include authentic tasks that are grounded in real-world business contexts. Authentic tasks typically involve ill-defined problems, complex or multi-step questions, multiple approaches to problem-solving, and sub-tasks that integrate across disciplines. This course incorporates some of these STEAM principles and ideas into various learning activities.

Data science has a strong connection with finance and economics. In this course, we will explore the integration of data science concepts and tools within the context of digital humanities. Digital humanities is an interdisciplinary field that encompasses research, teaching, and innovation at the intersection of computing and various humanities disciplines, including economics and finance. It is inherently methodological and encompasses the investigation, analysis, synthesis, and presentation of information in electronic form. Digital humanities studies the impact of these digital media on the disciplines in which they are utilized and examines the contributions of these disciplines to our understanding of computing. If you are interested in delving deeper into the concept of digital humanities and exploring the ongoing vibrant discussions in this field, I recommend referring to Klein and Gold (2019). Additionally, to gain insights into data science and data ethics informed by the principles of intersectional feminism, which aligns with the fifth United Nations Sustainable Development Goal on gender equality UN (2015), you may find D’Ignazio and Klein (2020) to be a valuable resource.

To learn more about the 17 United Nations Sustainable Development Goals:

3.1 .

R is a programming language and free software environment designed for statistical computing and graphics. It is supported by the R Foundation for Statistical Computing and widely utilized by statisticians and data miners for developing statistical software and performing data analysis. Python, on the other hand, is an interpreted, interactive, and object-oriented programming language. It incorporates features such as modules, exceptions, dynamic typing, high-level dynamic data types, and supports multiple programming paradigms including procedural and functional programming.

Getting started with R and Python has become increasingly accessible due to the abundance of free resources available on the Internet. This includes Artificial Intelligence (AI) tools and specialized AI programming assistants. All it takes is dedicating the right amount of time and effort to learn these languages. The perceived difficulty of computer programming is often a significant barrier for some individuals, but once they realize that it is not as challenging as they initially thought, their progress improves significantly.

This course integrates data science, data analysis, and computational finance using R, R Core Team (2024) and Python as the primary tools. As a result, you will have the opportunity to learn or further enhance your coding skills, which will enable you to apply economic and finance models in practical scenarios. It is important to note that this course is not primarily focused on computer science, and therefore we have limited time to cover mandatory finance-related material. To overcome this limitation, you will engage in hands-on assignments, collaborate with your peers, use AI specialized tools, and utilize online resources such as Swirl lessons and/or DataCamp courses to learn R and Python.

There are various approaches to learning basic data science and developing the ability to transform information into valuable business intelligence. Learning to code is one method that I highly recommend. Coding allows you to train your brain to think more efficiently and productively, enabling you to tackle complex problems and generate innovative solutions. In today’s finance job market, there is a growing demand for candidates with knowledge in the field of data science or computational finance. This is primarily because such expertise enhances creative problem-solving skills and proficiency in data analysis.

Listen to Steve Jobs:

I understand that learning programming may seem challenging and frustrating at first for some people. If this is your case, I want to assure you that it is not as difficult as it may initially appear. In fact, it can be an incredibly rewarding experience that opens up a whole new world of possibilities. By learning programming languages like R and Python, you will gain access to free and open-source software that is supported by a generous online community. This community is always ready to assist and provide guidance, making your learning journey much easier. Additionally, you will discover the incredible advancements in scientific document production that come with these tools, offering limitless possibilities compared to commercial software.

For a university student new to programming, using AI-based assistants for R and Python coding can significantly boost productivity and enhance the learning experience. AI assistants provide real-time code suggestions, autocomplete features, and immediate feedback, which can help beginners write code more efficiently and with fewer errors. These tools can also guide students through best practices and advanced concepts, accelerating their learning curve. Additionally, AI-powered error detection and debugging can be invaluable for new programmers, helping them identify and fix mistakes quickly, thereby deepening their understanding of coding principles. The ability to refactor code efficiently and maintain consistency across projects can further instill good coding habits and standards, making the transition from learning to real-world application smoother.

However, there are potential drawbacks to consider. Over-reliance on AI assistants might hinder the development of essential problem-solving skills and a deep understanding of programming concepts. Students might become too dependent on automated suggestions and fail to engage deeply with the code they are writing. Moreover, AI suggestions are not always accurate or contextually appropriate, and inexperienced programmers might struggle to discern which recommendations to follow. It’s important to note that AI tools tend to be more effective when users already possess some programming skills, as they can better evaluate the AI’s suggestions and integrate them appropriately. Additionally, the learning curve associated with integrating and using AI tools effectively, along with potential costs for advanced features, might be challenging for students with limited resources. Balancing the use of AI assistants with traditional learning methods is crucial to develop both practical skills and a robust understanding of programming.

3.2 ChatGPT.

The use of ChatGPT and other virtual assistants or generative AI tools in course activities is not prohibited for two main reasons. First, it would be impossible to ban them due to their easy access and the fact that they are present and integrated into a significant number of applications. Second, it would be equivalent to banning one of the technologies that is most transforming the way people work, and the job market would expect graduates to be familiar with and skilled in using such tools.

However, just because their use is not prohibited in the course does not mean it should be indiscriminate or used for everything without questioning. Based on my experience, and that of students who have taken the course, using ChatGPT does not guarantee a good grade. The main reason is that such tools, according to their own documentation, can make mistakes, especially when the task we ask is tied to a specific database, particular conditions dictated by the course materials, and specific methodologies and procedures of the course. Using these tools might give the false impression that they can infallibly solve class questions and activities, but that is not the case for the reasons mentioned.

My recommendation is that these tools can be very useful for studying, learning, and improving the status of projects and assignments, but using them as a substitute for the student’s own reasoning is not advisable and will most certainly be reflected in low grades.

3.3 Commercial alternatives.

Throughout your undergraduate studies, you will be expected to acquire proficiency in various commercial software programs, such as Microsoft Excel, SPSS, STATA, E-Views, and many others. I strongly encourage you to develop your skills in these programs, particularly if their usage is required by your professor. However, it is crucial to recognize that these programs are owned by private firms and primarily focused on generating value for their shareholders. Consequently, there is no guarantee that the associated file formats will remain accessible or even exist in the future, which can adversely impact reproducibility.

While I will never discourage you from learning commercial software programs like the ones mentioned above, I also want to emphasize the alternative option of learning and utilizing user-oriented computer languages, such as R or Python, for conducting rigorous data analysis in the fields of economics and finance. These languages are supported and continuously enhanced by a large and active scientific community, providing a plethora of online resources to support eager beginners like yourself.

Commercial software products like the ones mentioned above are undoubtedly important in the job market. However, it is crucial to recognize that the primary interaction with these programs relies on using a mouse to navigate through pre-defined, limited, and inflexible menus. This type of user interaction is often fleeting and unrecorded, resulting in many of the choices made during a quantitative analysis going undocumented. This lack of documentation poses significant challenges as it becomes difficult to trace the steps taken during an analysis and hampers the ability to replicate or extend the analysis in different contexts. In contrast, coding enables us to conduct and produce reproducible research. Learning how to code is akin to writing a cooking recipe, where each time you execute the code, the desired outcome is achieved. If you want to change the flavor from vanilla to chocolate, you do not need to start the entire process from scratch. Instead, you can simply modify the flavor parameter from vanilla to chocolate, execute the code, and voilà!

Commercial software products often come with high licensing fees and rely on opaque ‘black boxes’ — systems or processes where the internal workings are hidden or unknown — to generate a range of results. These black boxes pose a problem as they provide little insight into the underlying assumptions and procedures used to produce the final outcomes. Users may be left with a false impression that they can perform data analysis without fully understanding the intricacies involved. While this convenience may have its place in specific and limited scenarios, it hinders the exploration and customization necessary for innovative and improved applications.

In contrast, embracing languages like R and Python provides a versatile alternative to point-and-click programs. With these languages, you can write scripts to program algorithms for economic and financial analysis and visualization. By delving into the details of the computation, you gain a deeper understanding of the process and unlock possibilities for customization and innovation.

In light of this, I encourage you to embrace the shift from clicking to scripting. Look at this video to find out:

While chefs may need to invest in ovens, kitchen items, and ingredients, in the fields of economics and finance, many of our inputs, such as data and technology, are freely available. R and Python, being open-source software, come at no cost. By acquiring coding skills, you gain the ability to share, expand, reproduce, and innovate, ultimately generating original empirical results that serve as crucial inputs for research outputs, including your dissertation or other professional projects.

3.4 Cloud IDE.

A web-based Integrated Development Environment (IDE) is an online platform that provides developers with a suite of tools for writing, editing, debugging, and testing code directly within a web browser. Unlike traditional desktop IDEs, web-based IDEs require no local installation and can be accessed from any device with internet connectivity. These platforms often support multiple programming languages and come equipped with features like syntax highlighting, code completion, version control integration, and collaborative editing.

Some examples of cloud IDE:

Deepnote is a cloud-based data notebook that offers a new kind of collaboration, compatible with Jupyter. With Deepnote, you can easily work on your data science projects in real-time and in one centralized location with your colleagues.

Google Colab, short for Google Colaboratory, is a free, cloud-based platform that allows users to write, run, and share Python code within a Jupyter notebook environment. It is particularly popular for data science, machine learning, and deep learning tasks, as it provides easy access to powerful computing resources, including GPUs and TPUs, without requiring any setup. Users can import and work with datasets from various sources, integrate with Google Drive for storage, and use a wide range of Python libraries. Colab facilitates collaboration by allowing multiple users to work on the same notebook simultaneously and share their work easily. It is widely used in both educational and research settings for prototyping, experimentation, and collaborative projects.

DataLab from DataCamp is an interactive coding environment designed for learning and practicing data science and analytics skills. It is part of DataCamp’s educational platform, offering users access to a rich set of tools and resources for working with data. In DataLab, users can write and execute code in various programming languages such as Python, R, and SQL, with access to popular data science libraries and frameworks. The environment is integrated with DataCamp’s extensive collection of courses, allowing learners to apply their knowledge through hands-on exercises and projects. DataLab also features real-time coding collaboration, enabling multiple users to work on the same project simultaneously, fostering teamwork and peer learning. Additionally, it includes an integrated AI assistance tool that provides real-time feedback, code suggestions, and debugging help, enhancing the learning experience and making it easier for users to overcome coding challenges. This combination of features makes DataLab a powerful and user-friendly platform for developing data science skills.

In this course you will be asked to complete graded activities in DataLab. However, in case it fails you can use other web based or desktop IDE alternatives.

I find DataCamp a very good alternative to learn data science. Normally, people have to pay for a DataCamp account to learn data science, and some firms have to pay for this kind of training to help their employees to learn R or Python. Current fees for a DataCamp premium individual account is about 33.25 USD per month, about 200 USD for the semester. However, as my student, you have free individual access for full access to all DataCamp courses and resources including DataCamp’s DataLab for the whole semester as long as this firm keeps its promise to make this access free for my students. In exchange, DataCamp ask for a mention on social media, please find all the resources and instructions on these communication guidelines. Are you able to provide this?

3.5 Desktop IDE.

A desktop Integrated Development Environment (IDE) is a software application installed locally on a computer that provides a comprehensive set of tools for software development, including code editing, debugging, and testing functionalities. Desktop IDEs typically offer robust features such as syntax highlighting, code completion, refactoring tools, and integrated version control. They are optimized for performance and often provide extensive customization options and plugin support to tailor the development environment to specific needs. Working with a cloud-based alternative, instead of a desktop IDE is similar to working with Google Docs instead of MS Word. We are all familiar with both local and cloud-based work environments and understand the importance of data privacy and regular file backups.

Some examples of cloud IDE:

RStudio is an integrated development environment (IDE) specifically designed for the R programming language, which is widely used for statistical computing, data analysis, and visualization. RStudio provides a comprehensive set of tools that enhance productivity, including a source editor with syntax highlighting, code completion, and smart indentation. It features an interactive console for running R code, a powerful debugger, and tools for plotting and managing packages. RStudio also includes integrated support for version control systems like Git, making it easier to manage and collaborate on projects. Additionally, it supports the development of R Markdown documents, Shiny web applications, and interactive dashboards, allowing users to create and share dynamic and reproducible reports. RStudio is also Quarto-friendly, enabling the creation of multi-format documents and reports. Furthermore, it supports the use of Python, allowing users to seamlessly integrate Python code and libraries into their workflows, making RStudio a versatile and powerful platform for data science and analytical tasks.

The main difference between R and RStudio is that R is the core programming language, while RStudio serves as the user-friendly integrated development environment (IDE) for developing data science projects. When working with R, you will need to download and install both R and RStudio. However, you will primarily use RStudio as your interface for coding in R. Behind the scenes, your computer will utilize the R program to execute the calculations. Moreover, RStudio is free and offers the convenience of seamlessly integrating multiple programming languages, such as R and Python, within a single data science project. This feature is particularly useful when collaborating with a team proficient in different programming languages.

In this course, you will be asked to complete graded activities (homework assignments and exams) using the cloud based alternatives, specifically DataCamp’s DataLab. However, you are required to install RStudio in your own computer.

If you have not yet installed the necessary programs on your computer, please download R, Python, and RStudio from the following websites: https://www.r-project.org/, https://www.python.org/downloads/, and https://posit.co/downloads/, respectively. In the reference list at the end of this document, you will find some helpful YouTube installation guides that explain the step-by-step process of downloading and installing these programs from scratch.

Please note that there are other alternatives to RStudio like Anaconda and Visual Studio Code. If you were to ask, my personal favorite is RStudio. In fact, I created this syllabus using RStudio with R markdown and Quarto, and it is hosted in GitHub pages.

3.6 Relevance.

You may be aware that just a few years ago, economic agents with privileged access to information had a clear comparative advantage in business and decision-making. However, thanks to technology, information and data are now widely accessible, eradicating the possibility of gaining a competitive edge solely through information access. With data availability no longer being a distinguishing factor, knowledge has emerged as a critical aspect in business. In the present day, it is not merely about having access to information; rather, it is about understanding how to leverage the increasing volume of data to create value in business. Manipulating and transforming data into valuable business tools and informed decisions has become an essential skill for all business professionals.

I understand learning programming languages could represent a source of uncertainty and stress for some of my students. This is why I have developed and gathered a vast amount of varied and free resources to learn R and Python in the reference section of this syllabus. In fact, you have more free resources that you need in the semester. It is true that you will have to learn a few things on your own, and it is true you will have to investigate to learn some other things. You are expected to learn how to learn as well and as quickly as you can because in the job market you need to constantly learn and apply new knowledge, and solve problems that currently do not exist. A competitive graduate is not the one who learns what was taught in class, a competitive graduate is the one who also manages to learn how to learn.

It is important to remember that university is a time for learning and exploring new things. Learning new languages like R and Python is just one aspect of this educational journey. While it may require some time and effort initially, the benefits and knowledge gained far outweigh the investment. These skills will not only enhance your academic experience but also provide you with valuable tools for future endeavors. It is common to face temporary frustrations during the learning process, but I encourage you not to let them dampen your enthusiasm and hinder your overall learning experience at university. Embrace the challenges, seek help when needed, and remember that the rewards of mastering programming languages are well worth the initial difficulties.

In my opinion, English serves as the predominant language for conducting research and engaging in business endeavors. Mathematics and statistics act as languages that enable us to comprehend the workings of nature. Computer languages, on the other hand, facilitate direct communication with computers, enabling us to conduct statistical experiments within the business context. Considering that computers are an integral part of our lives, it is essential to learn how to communicate with them not only at a basic user level but also at a programmer level. As aspiring professionals, it is crucial to distinguish ourselves from our peers and prepare for the changing conditions of the job market, particularly in the field of financial economics. In my perspective, it is vital for individuals to strive for proficiency in these three forms of interaction with our environment, irrespective of their specific professional business expertise: English, mathematics, and coding.

4 Resources.

Academic resources encompass a wide range of materials, tools, and sources of information specifically designed to support and enhance learning, research, and scholarly pursuits in an academic setting. These resources play a crucial role in facilitating knowledge acquisition, promoting critical thinking, and advancing scholarly activities within the academic community.

Here is a list and description of the available resources to help you learn the subject. My advice is to utilize as many resources as possible, as they will enable you to grasp the subject matter and develop the necessary professional competences.

Professor. I have extensive experience as an academic and researcher, along with numerous postgraduate studies. I am willing to assist you in better understanding the course topics. If you need any help, feel free to contact me <martin.lozano@udem.edu> and follow my advice.

Please watch the video about professors:

Groups. Exams and homework assignments (see Section 5) are group-based, so your team will be an important part of your academic support throughout the course.

Groups policies.

1. Groups are formed with no more than four students. Group activities can be completed by one, two, three, or four students. Group formation is done by the students at the beginning of the semester.
2. A group member may switch groups as long as the members of both the group they are leaving and the group they wish to join agree and express their consent in writing; an email will suffice. Additionally, I must authorize the change to ensure the number of members per group does not exceed the limit. All requests to change groups must be submitted no later than one week before the next partial or final exam. Requests received after this deadline will not be considered or rejected.
3. It is not mandatory, but I recommend that your team establish clear rules and put in writing the reasons that could justify a penalty in the coevaluation (peer evaluation). These types of agreements should be managed within the team; I do not intervene in these matters because I consider them internal, and I believe each team can work in its own way. I do not change coevaluation marks.
4. If a group member performs poorly, the rest of the group may use coevaluation to ensure the individual grade is more accurately assigned. Coevaluation is a crucial component of group work. A coevaluation score of zero assigned to a student will directly result in a zero as their individual grade for that activity. Therefore, students are strongly encouraged to participate actively and contribute meaningfully to their group’s work.

Class sessions. During our class sessions, we will explain and discuss specific topics, address questions, review your study progress, and occasionally conduct brief activities. Please keep in mind that the time available during class sessions is limited, and we may not be able to cover every topic in full detail. To ensure comprehensive coverage, we will utilize other resources and learning activities. It is expected that you attend on time, actively participate, and engage in our discussions. I recommend using your computer during the sessions as you may need to access an IDE, view the PDF textbook, or refer to your own homework.

For Zoom sessions, you have the option to keep your camera either on or off, as we will often be sharing the screen during class, or you will be doing an activity. To provide convenience, unless otherwise specified, class Zoom sessions will be recorded and made accessible to you. Please note that occasional internet service disruptions during class sessions are a risk we all face. The Zoom link to join the class can be found in Blackboard and in Section 7.

Tardiness policies:

1. In face-to-face classes, three tardies count as one absence. Tardies accumulate over the course of the semester. A tardy is recorded if the student arrives immediately after attendance is taken and remains for the rest of the class. If a student is marked as present or tardy, this can be changed to an absence if they leave the classroom or do not stay until the end of the class.
2. If the class is online, there is no tardiness policy. Attendance will be based on the Zoom activity log, which shows how many minutes each student remained connected during the session. Because there is no tardiness policy in online classes, each session will result in either an attendance or an absence being recorded. If a student is not connected for the entire class, or at least for the vast majority of it, the session will be marked as an absence. To ensure accurate tracking, it is essential that you join each class using your own Zoom login credentials. This is the only way to verify your identity and correctly attribute your attendance.
3. In face-to-face classes, we usually hold a few Zoom sessions during the semester (see Section 7 for details). During these sessions, the tardy policy is the same as in online classes.
4. In face-to-face classes, while some sessions may be conducted online as part of the course design, I allow my students to attend the designated face-to-face sessions via Zoom once per semester as an exception. If this applies to you, please notify me by email. If it happens a second time, the student may still attend via Zoom, but I will record an absence in accordance with current university regulations.
5. With respect to the maximum absences policy, we will adhere to the current university regulations.

Please watch the following video for a valuable recommendation from Prof. Fleisch, which is applicable to both online and face-to-face sessions:

Review sessions. We have review sessions for assignments and exams, which take place before the homework assignment submission deadline and prior to the exams. These sessions provide a great opportunity to ask questions and enhance your chances of achieving higher marks in both assignments and exams. I usually avoid to discuss new material during these review sessions.

See some recommendations about review sessions:

Discussion forums. There are three discussion forums available for us to interact and engage with one another. Each forum corresponds to a different part of the course: the first, second, and third partials. The benefit of the discussion forums is that everyone can track the progression of discussions and actively participate. The logical flow of ideas remains recorded, allowing you to receive my feedback and comments. Since our class sessions have limited time, we may occasionally utilize the discussion forums.

Email. You are welcome to contact me via email at any time: <martin.lozano@udem.edu>. I also send group emails containing important information, so ensure that my email address is not marked as spam. If there is ever a delay in my response to an email or any other request, please feel free to insist and kindly remind me.

Meetings. Whether it is face-to-face or virtual learning, we can schedule 30-minute Zoom online meetings if you require additional assistance or have any other issues you would like to discuss with me. These meetings can be arranged either individually or in groups. Simply send an email to <martin.lozano@udem.edu> to inquire about my availability. I ask that meetings be arranged by email so that I have written evidence of the arrangement and the necessary time to check my schedule before confirming available hours. If my initial availability does not work for you, feel free to let me know, I can offer alternative times until we find a mutually convenient slot. If you need more time, we can arrange for additional meetings or extend the duration as needed. Here is the Zoom link for meetings: https://us02web.zoom.us/j/9209945512. The student or the group may request that the Zoom meeting be recorded, and I can share the recording with them for future reference. For the class session Zoom link, refer to the course calendar: Section 7.

DataCamp DataCamp is an online learning platform focused on data science and analytics. It offers interactive courses in Python, R, SQL, and other tools commonly used in data analysis, machine learning, and statistics. Users learn by writing and running code directly in the browser, with instant feedback and guided exercises. You have free and full access as my student this semester.

DataCamp policies.

1. We rely on DataLab, which is part of DataCamp, for completing homework assignments and exams (see Section 5). These platforms are not only essential for completing tasks and exams but also serve as the official means for submitting and turning them in. However, these platforms are not immune to occasional downtime. For this reason you must have RStudio installed on your local computer, which will allow you to have a backup to continue your work in case of an emergency.
2. All homework assignments and exams submissions, must be sent directly within the DataLab environment in the form of Jupyter notebooks (.ipynb). Although DataLab technically allows uploads in formats such as PDF or MS Word, these formats are not acceptable for evaluated homework or exams in this course. You are required to complete your work directly in the provided Jupyter notebook environment to ensure consistency and proper evaluation of your submissions.
3. In my experience, my students create and work with various documents and folders within DataLab. For convenience, I have created a folder called “submissions” in your group workspace, where you, as students, will place the documents I need to evaluate as assignments and exams. My instruction is not to rename this folder, and to use it so that I can easily identify the documents I need to assess and grade.
4. DataLab and Jupyter Notebooks may be new tools for some students, so it is important to pay close attention when submitting exams and assignments. All activities must be submitted in the correct workspace, which is identified by the group name and the correct folder called “submissions”. Submitting an activity in the wrong location, even if the work is completed, will be treated as not delivered. No exceptions will be made for submissions placed in incorrect folders or workspaces.
5. After completing your final exam \(E_F\), you will no longer have editor access to DataLab. Therefore, I suggest that you create a backup of any documents you wish to keep.

Book. The book (or books) is one of the main pillars of this course. In my experience, learning primarily occurs through reading and then reinforcing the concepts through practical application, although there are various other activities included in this course. The authors of the book are esteemed experts in their respective fields: John C. Hull from University of Toronto; Frank Fabozzi from Johns Hopkins Carey Business School; Richard A. Brealey from London Business School; Stewart C. Myers from Sloan School of Management, Massachusetts Institute of Technology; Franklin Allen from Imperial College London; Rob J. Hyndman and George Athanasopoulos from Monash University; ad Eugene F. Brigham and Joel F. Houston from University of Florida. These books are not only suitable for intermediate and advanced undergraduate degrees but also for first-year master’s degrees. I prefer using the original versions of the books rather than translations because, in my experience, the translations may not always match the quality of the original English version and, in some cases, may not be available.

Tutorials. I have created a series of specialized online tutorials that provide step-by-step instructions on implementing various topics and estimating financial and economic models using data. These tutorials aim to help you bridge the gap between the theoretical concepts presented in the textbook and the practical implementation in R code. This approach, often referred to as literate programming, allows for a seamless transition. You can access my tutorials in my GitHub public repository https://github.com/mlozanoqf, or more easily via GitHub Pages: Quantitative Finance with R.

Others. All learning resources described above represent a good resource for your own study of the course material. There are plenty of Internet resources that you will have to use, from databases, YouTube videos, GitHub public repositories, specialized programming blogs, AI tools, books, electronic books, etc. See the resource list at the end of this document for further details. You are encouraged to read articles, reports and news on your own to enhance and expand your understanding about how theoretical concepts relate with current real-life events. The Economist, The Financial Times, The Guardian, The Wall Street Journal, MarketWatch, Reuters, Bloomberg, Bureau of Economic Analysis, Banxico, Project Syndicate, The New York Times, El Financiero (México), El Confidencial (Spain), OECD, are a good way to grasp contemporary insights related with this course. Other references to support your learning process include economic and financial reports from private banks such as Banamex and BBVA, and think tanks websites such as The Mexico Institute, México cómo vamos, CIDAC, IMCO, COMEXI, among many others. My advice in this respect is rather simple: the more you read the more you learn.

Further support.

In case you have any concern, any question about the course contents, or if you are having trouble understanding the course material, you have to contact me as soon as possible. This is your own responsibility starting from day 1. We can arrange an online meeting, or we can solve your questions or concerns by email, whatever is best. In case you are having a poor academic performance and you are genuinely interested to improve, my best advice is to contact me during the lecture period, not after the last session of the semester, and we can discuss specific strategies that can potentially help you to get higher marks and reduce the risk of failing the course. The point here is that you have to know that I can help you to improve your academic performance during the semester only if you are truly interested. If you would like to improve your marks at a later stage, or after the final exam, then I am afraid I can do nothing for you, but I can do a lot during the semester. Please email me in case you would like to arrange an appointment, my full contact details are at the beginning of this syllabus. The email is definitely the best way to initially approach me.

In case you get a low mark in one activity or you get difficulties at some specific topic you should take immediate actions in order to quickly revert this. I am not planning to relax the marking criterion so what you have to do is to improve your own quality standards in order to pass given my marking standards and my expectations about your academic performance. You are free to contact me in case you need assistance about specific strategies to improve your academic performance.

I do not recommend you to get disappointed, angry or sad if you get a low mark. There is no need for that because getting one low mark is not determinant to fail the course. Please see the evaluation method to verify how the final grade is computed and you will be amazed in a positive way. Also, I do not recommend you to get frustrated if you receive an unexpected low mark or an unexpected negative feedback about your work or your answers. The mechanics here are very simple: in order to improve, understand and learn, you need to know what you did well, what you did wrong, and try again until you do well without getting desperate or frustrated in the process. In short, avoid negative feelings as these might lead to further frustration. Nobody wants to hurt you, we all want you to learn in a favorable environment. You have overcome challenges before, so avoid the dark side . On the contrary, you should rather work harder to meet the course standards. We are not in conflict, in fact we are collaborating. According to my experience, students who sadly fail this course ignore or forget these recommendations.

As a student, you may have different responsibilities. You are probably working, you might have family commitments, other courses, unexpected workload, troubles, and other diverse duties. All these may affect your academic performance at some time. My view here is that you are expected to do well in all aspects of your life and you will have to manage your time effectively and be productive. I hope you can allocate your time in such a way that you can pass this course and do well in the rest of your personal activities. Sometimes the workload is so intense that you have to evaluate whether you need to drop an activity to do well in the rest and keep you healthy, physically and mentally. If you find yourself overwhelmed by your personal troubles, workload and responsibilities, please ask for help, the university has professionals that can help you with this. If you have personal problems I can hear you and if I am unable to guide you properly, we can ask for professional help. Keep this in mind, we all care about your health, and health is far more important than a job, a course, and the university.

This is a video from The University of Arizona Global Campus about How to Manage College Stress.

We all know that good grades do not necessarily make you a good person or a good professional. One could have difficulties at school but have such a good professional network, or an impressive ability to do business, or an impressive entrepreneurship spirit. However, grades are still quite useful to assess how well you are at meeting some academic standards and how well you manage to understand the relevant topics in your area of expertise. It is more important to be a good person than a good professional, and the graded activities are specifically designed to partially evaluate your technical abilities as a professional. Then, we all assume that you are a good person, and the course activities will help us to evaluate some of the required skills and competences as professionals. Having said that, I hope you can achieve high grades in this course.

In sum, I expect the best academic performance you can achieve, not the average, and definitely not the minimum. This should not be a surprise since you are studying at one of the most prestigious private universities in the country (we belong to a business school with AACSB and AMBA accreditation). If you succeed at delivering your best performance in this course, and I believe you can, then you might be in a better position to eventually tackle business problems including the most interesting and valuable ones which includes those that do not exist yet. I am sure you have done some extraordinary things in the past, you have overcome very hard challenges, so take this course just as another opportunity to unleash your full potential and show me how committed you are with your academic professional training.

I strongly believe you can learn anything just as this video from Khan Academy indicates:

Most of my previous recommendations in this subsection are for those who are having difficulties with the course. If you are doing fine, then good for you , try to enjoy the learning process as much as you can. My commitment is that you will have all the support and resources you need to pass the course during the semester; you only have to take them or ask for them during the lecture period, not after the last course session, and follow all my recommendations in this syllabus.

5 Activities.

Learning activities are structured tasks or exercises designed to facilitate the acquisition of knowledge, skills, and understanding in an educational context. They are intended to promote active engagement, deepen understanding, and support the achievement of specific learning objectives.

The learning activities are classified into graded, non-graded, and extra marks categories.

• Graded activities are two assignments \(H_1\), \(H_2\), two partial exams \(E_1\), \(E_2\) and one final exam \(E_F\), which account for 100% of your final grade and all of them are group activities.
• Extra marks are awarded as a bonus to your next partial exams or final exam, so completing extra marks activities increases the maximum mark possible. These are optional and individual activities.
• Non-graded activities are relevant for your learning but are not included in the grading process. Therefore, failure to complete them will not result in any mark deduction. These are optional and individual activities.

Activities policies.

1. Exams and homework assignments are submitted in English.
2. Exams and homework assignments are group activities. See Group policies for more details.
3. In this course there is no late submission policy at all.
4. All homework assignments and exams submissions, must be sent directly within the DataLab environment in the form of Jupyter notebooks (.ipynb). See DataCamp policies for more details.
5. The final exam \(E_F\) is the last activity of the semester. No activities are planned after the final exam to increase marks or pass the course.
6. The final exam \(E_F\) date may need to be adjusted for administrative reasons in the case of students who are graduate candidates.
7. Students may lose the right to take their final exam due to excessive absences, according to current university regulations. I will notify affected students by email if they exceed the allowed number of absences. See Tardiness policies for more details.

5.1 Final grade.

1. Groups \(G\) receive marks for all graded activities \(H_1\), \(H_2\), \(E_1\), \(E_2\), and \(E_F\) assigned by me (the professor): \(G_{H1}, G_{H2}, G_{E1}, G_{E2}, G_{EF}.\)

2. Students complete individual activities and earn extra marks \(X\) applicable for partial exams and final exam only: \(X_{E1}, X_{E2}, X_{EF}.\) Then, extra marks are added to the group marks: \[ \begin{align} GX_{E1} &= G_{E1} + X_{E1}, \\ GX_{E2} &= G_{E2} + X_{E2}, \\ GX_{EF} &= G_{EF} + X_{EF}. \end{align} \]

3. Students receive coevaluations from teammates for homeworks and partial exams (peer assessment): \(C_{H1}, C_{H2}, C_{E1}, C_{E2}\). For example, \(C_{H1}\) is the simple average of the rest of your teammates who submit their coevaluation: \(C_{H1} = \frac{1}{N} \sum_{i=1}^{N} C_{H1,i}\). Coevaluation is the last step to get the individual marks for the graded activities \(H_1\), \(H_2\), \(E_1\) and \(E_2\):

\[ \begin{aligned} \text{If } G_{H1} \geq 70 \text{ and } C_{H1} \geq 70, &\quad H_1 = (0.7 \times G_{H1}) + (0.3 \times C_{H1}). \\ \text{Else}, &\quad H_1 = \min(G_{H1}, C_{H1}). \end{aligned} \] \[ \begin{aligned} \text{If } G_{H2} \geq 70 \text{ and } C_{H2} \geq 70, &\quad H_2 = (0.7 \times G_{H2}) + (0.3 \times C_{H2}). \\ \text{Else}, &\quad H_2 = \min(G_{H2}, C_{H2}). \end{aligned} \]

\[ \begin{aligned} \text{If } GX_{E1} \geq 70 \text{ and } C_{E1} \geq 70, &\quad E_1 = (0.7 \times GX_{E1}) + (0.3 \times C_{E1}). \\ \text{Else}, &\quad E_1 = \min(GX_{E1}, C_{E1}). \end{aligned} \]

\[ \begin{aligned} \text{If } GX_{E2} \geq 70 \text{ and } C_{E2} \geq 70, &\quad E_2 = (0.7 \times GX_{E2}) + (0.3 \times C_{E2}). \\ \text{Else}, &\quad E_2 = \min(GX_{E2}, C_{E2}). \end{aligned} \] Of most frustration to students is receiving the same mark as their fellow non-contributing group members despite producing much of the group’s work. In order to avoid this free-rider problem you will have to answer two coevaluations, one for the first part of the course and one for the second part. The first coevaluation is used to calculate the individual marks of \(H_1\) and \(E_1\); the second coevaluation is used to calculate the individual marks of \(H_2\) and \(E_2\). coevaluation is so important that one student may fail simply because of his or her low contribution in the group. Sometimes students face mitigating circumstances, if that is the case you will have to discuss with your group because their marks may have a significant negative impact on your mark.

The coevaluation is as an effective tool to incentive or penalize the group members to work well and on time. As a professor, I am not always aware of who is working well within a group, but the coevaluation can help us to be fair and assign marks based on academic merits. I do not reveal specific details about how you co-evaluate your colleagues. So, your coevaluation details will remain anonymous. I cannot change the coevaluation, this is a mark assigned by your colleagues based on your performance and contribution. Then, there are many incentives aligned so the group should work well, otherwise the chances to get a low mark are high.

In this video, students from The University of Melbourne share their thoughts on how to effectively work in teams.

Coevaluations are completed using a Google Form. I set up an example here: https://forms.gle/Rzd6Chv89X5HR4rWA. Feel free to access the link and fill out the form to get familiar about the process. The real link will be available in Blackboard.

In particular, you will have two Google Forms web links in Blackboard to complete your coevaluation, one for \(H_1\) and \(E_1\), and one for \(H_2\) and \(E_2\). You will receive a copy of your answers by email just as in any other Google form. A typical issue is that students are not able to open it, but that is because you need to log in using the university email address. There is coevaluation for \(H_3\) since it is non-graded, and \(E_F\) since classes are over by then.

4. Final grade \(F\) is calculated using a convenient weighted average:

\[ \begin{aligned} F &= 0.4\times[0.7\mathrm{max}(E_1, E_2) + 0.3\mathrm{min}(E_1, E_2)] \\ &+ 0.3\times[0.7\mathrm{max}(H_1, H_2) + 0.3\mathrm{min}(H_1, H_2)] \\ & + 0.3\times E_F. \end{aligned} \]

Where \(E_F = G_{EF} + X_{EF}\) because no coevaluation is applied to \(E_F\).

This criterion is significantly better compared with the traditional average as the higher exam and assignment marks weigh more than twice the lower marks (70% versus 30%).

Unfortunately, some students who do badly in their first exam and/or their first homework assignment believe that everything is lost and they should drop the class. My view is that this is not an accurate view as the grading above allows you to have a very bad exam and/or homework assignment and still be in a good position to pass the course. For example, imagine that for some reason you have \(H_1=35\), but you manage to improve and get \(H_2=85\). In any other course you will have an average of \(\frac{35+85}{2}=60\) . However, in my course we compute weighted averages for both homework assignments and partial exams, so your weighted average is \((35\times 0.3) + (85\times 0.7)=70\) .

The difference between the weighted versus regular average is illustrated below:

The effect of weighted averages over the final grade \(F\) is quite significant. Here is a very extreme example to illustrate the effect of the weighted average. See the difference between a final grade of 56 versus 70.

Weight	Activity	Mark	Points in this course	Points in other courses
40%	E1	0	0 × 0.3 × 0.4 = 0	0 × 0.5 × 0.4 = 0
	E2	100	100× 0.7 × 0.4 = 28	100× 0.5 × 0.4 = 20
30%	H1	0	0 × 0.3 × 0.3 = 0	0 × 0.5 × 0.3 = 0
	H2	100	100× 0.7 × 0.3 = 21	100× 0.5 × 0.3 = 15
30%	EF	70	70 × 0.3 = 21	70 × 0.3 = 21
100%	F		70	56

In any case, my sincere advice is to keep the standard as high as possible in order to minimize the risk of achieving low grades.

5.2 Exams & Homework assignments.

All topics covered in this course will be evaluated in the exams. To achieve an outstanding final grade, I strongly recommend taking detailed notes throughout the semester. Completing the assignments is also excellent preparation for the exams, so I encourage you to start working on them as early as possible. You’re always welcome to share your progress with me, I’ll be happy to provide feedback to help you improve.

Please take a look at this video about taking notes:

This one is good as well. In this video, students from The University of Melbourne talk about using digital tools to take notes and stay focused.

When? The exams will follow the schedule in the course calendar (Section 7), aligned with the university’s official dates. Exams \(E_1\) and \(E_2\) will be 1.5-hour in-class activities, and \(E_F\) will last 2 hours. In 3-hour class sessions, \(E_1\) and \(E_2\) will take place during the first part, followed by a review of the answers in the second. Homework assignments are due at 10:00 a.m. on the specified dates in the course calendar, with no late submissions accepted under any circumstances: missing, incorrect, empty, or corrupted files will result in a zero for the group. This policy is strict and non-negotiable, and it’s important to be aware, as every semester some students lose marks due to preventable oversights. Homework \(H_1\), \(H_2\), and \(H_3\) are due shortly before \(E_1\), \(E_2\), and \(E_F\), respectively. Low assignment grades are usually not because the tasks are too difficult, but because groups begin working on them just a few days before the deadline; planning ahead is strongly advised.

Please consider the following recommendations about exams:

What are typical questions? While past partial exams may be available—since I allow students to keep their copies, it’s important to keep in mind that the course content evolves and the questions change each semester. Still, reviewing older exams can help you understand the general format and types of questions. Exams often include tasks that require using R and/or Python, typically focused on analyzing and interpreting data or results. To help you prepare, we will have review sessions before each graded exams and homeworks assignments, and if more time is needed, we can continue discussions through the forum. Homework assignments will include applied, research-oriented questions that require writing code in R and/or Python, as well as learning new concepts and conducting independent research. However, you won’t be alone as you have access to many resources outlined in the syllabus, including my support through Zoom sessions, DataLab discussions, meetings and email.

What are the mechanics? Exam instructions will be provided as a .ipynb Jupyter Notebook file, made available on Blackboard five minutes before the start of the class session, as scheduled in the course calendar. Once available, you must download the file and upload it to your group’s DataLab workbook, where you and your teammates will collaborate online to complete the exam within the given time frame. There’s no need to submit the file separately as I have access to your DataLab and will make an electronic copy for grading. Be sure to finish before the deadline, as late work is not accepted. A few days later, you’ll receive your group’s grade along with comments. I may also silently monitor your progress during the exam via your notebook. The process for homework assignments is similar: once the instructions are posted on Blackboard, upload them to your group’s DataLab workbook, complete the task online, and ensure everything is finalized before the deadline. I will make a copy for review and grading, and afterward, you’ll receive your group’s mark and feedback.

Are we going to review the exam and homework assignment answers? Yes, after each exam, we will use the following class session (held via Zoom) to go over the questions and discuss correct approaches. This applies to \(E_1\) and \(E_2\), but not to \(E_F\) since classes end before the final exam results can be reviewed. Exam questions are open-ended, and there are often multiple correct ways to answer them, so we will explore a variety of valid responses and common mistakes. Some students may be used to questions with only one right answer, but that is not usually the case in this course. If more time is needed for review, we can continue the discussion on the forum, in DataLab, or through a meeting upon request. Reviewing your mistakes is an important way to learn and prepare for the final exam. For homework assignments, students receive immediate feedback during their presentations, and additional comments are available upon request.

What if we fail to understand our own mistakes? Regardless of the activity, if you have any difficulty understanding your mistakes, it is expected that you reach out to me for clarification. In such cases, I may request you to attempt the questions again before we can discuss your specific mistakes in a meeting.

Do all group members receive the same mark? Not necessarily. Individual marks are adjusted based on coevaluation results. For example, even if a group receives a perfect score on the first homework \(G_{H1} = 100\), a student with a coevaluation score of \(C_{H1} = 0\) would receive an individual mark of \(H_1 = 0\). Conversely, if the group mark is \(G_{H1} = 90\) and a student receives a coevaluation of \(C_{H1} = 100\), their individual mark adjusts to \(H_1 = 93\). In short, your final mark reflects both the group’s performance and your contribution. Additionally, exam marks may increase through participation in extra credit activities.

Where? Physical presence in the classroom is not required for taking the exams. You can complete the exams from any location of your choice, as they are conducted, answered, and submitted entirely in electronic format. Just ensure that you and the rest of your group have a stable Internet connection. I can create Zoom rooms for your group to answer the exam if you wish. I will be available during the exams through Zoom, and while joining the Zoom session is not mandatory during exams, please be aware that I will be accessible in case you need any assistance. The Zoom link for the exams is the same as the one assigned to the course. It is important to note that for 3-hour class sessions, the first part of the session is dedicated to taking the exam. During the second part of the class, you will need to log in to the Zoom session to review and discuss the answers for the \(E_1\) and \(E_2\) exams. Logging in is required to register your attendance for this session.

How many questions? For both \(E_1\) and \(E_2\), there will be a total of 4 questions, out of which you are required to answer 3. This allows you the opportunity to choose the 3 questions that you feel most confident about. As for \(E_F\), there will be 5 questions, and you are expected to answer 4. All questions carry equal weight. It is important to note that if you answer all questions may result in a significant mark deduction, you only have to answer 3 out of 4 for partial exams and 4 out of 5 for the final exam. However, if you take the exam on a different date for any reason, your \(E_1\) or \(E_2\) will consist of 3 questions instead of 4. In this case, you will not have the advantage of leaving one exam question unanswered. The same principle applies to the \(E_F\). The \(E_F\) covers all the topics and activities covered in the course. To ensure convenience, one question from \(E_1\) and one question from \(E_2\) will be included in \(E_F\) with minor modifications. This means that if you have prepared well for \(E_1\) and \(E_2\), you should be able to answer at least 50% of the \(E_F\) correctly.

Can we open the textbook during the exam? Yes, you are allowed to use the Internet and all course materials during the exams. The purpose of the exams is to assess your reasoning, coding, and analytical skills rather than your ability to memorize concepts or perform Internet searches. The exam questions are designed in a way that the answers cannot be found online, in the textbook, or in a test bank. Even AI tools frequently fail to provide full correct answers because most of the time questions are very specific and data oriented. While the questions are based on the course material, they are typically new and original. However, it is important to note that you are expected to answer the exam on your own, with the assistance of your own group, which should consist of no more than 4 students.

5.3 Rubrics.

This section explains how your work will be evaluated in this course. You’ll find the grading rubric for oral presentations, the criteria used to assess your exams, and definitions of common instruction words used in assignments. Review these guidelines carefully to understand what’s expected and how to prepare your submissions effectively.

Homework assignments, oral presentation rubric.

Each of the 5 criteria is worth 20 points, for a total of 100 points. The final assignment group grade is based entirely on the oral presentation. Written submissions must be delivered on time but are not directly graded. There is no late submission policy: submissions must be uploaded by the deadline through the group workspace in DataLab. Timely submission through the correct platform is a necessary condition for the assignment to be evaluated. In some cases, specific questions or components of an assignment may be evaluated under different or additional criteria. When this applies, it will be clearly stated in the assignment instructions.

No evidence 0% - 40%.	Emerging 60%.	Competent 80%.	Strong 100%.
1. Understanding of problem, objectives, and concepts: 20 points.
No explanation of the problem or objectives; no visible grasp of the core concepts or task.	Some understanding is evident, but unclear or incomplete; vague or inaccurate use of concepts.	Generally clear explanation of the problem and objectives; concepts are mostly correct and relevant.	Clear, accurate, and thoughtful explanation of the problem; concepts are correctly used and well integrated.
2. Methodology and implementation (including code): 20 points.
No method presented or completely misunderstood; implementation or code not explained.	Method is mentioned but poorly explained or justified; code seems copied or unclear.	Method is mostly clear with some justification; implementation and code are explained with partial understanding.	Methodology is clearly justified and correctly applied; code is well explained and understood.
3. Analysis and interpretation of results: 20 points.
No results shown or results are misinterpreted; no logical conclusions.	Some results presented, but interpretation is shallow, confusing, or partially incorrect.	Results are reasonably interpreted; conclusions are mostly clear and linked to the objectives.	Results are clearly analyzed and interpreted; conclusions are logical, insightful, and relevant.
4. Communication and group presentation quality: 20 points.
Presentation is read aloud or disorganized; very poor clarity or group coordination.	Delivery is hesitant or unclear; some members dominate or contribute little; moderate coherence.	Presentation is mostly clear, structured, and coordinated; some minor delivery issues.	Presentation is confident, well-articulated, and engaging; group is coordinated and professional.
5. Responsible use of tools and independent thinking: 20 points.
Clear signs of overreliance on AI tools; no personal understanding or critical thinking.	AI tools used, but with limited understanding or personal contribution.	AI tools used appropriately; students show some independent thinking and reflection.	Responsible and critical use of AI; students clearly demonstrate understanding and learning in their own words.

How exams will be evaluated.

The following criteria will be used to assess your answers in both partial and final exams. They reflect the expectations regarding content, clarity, methodology, language, format, and academic integrity. Please review each item carefully and use them to guide how you prepare and structure your responses. Failing to meet these criteria may result in mark deductions or invalidation of your submission.

1. Relevance to the question requirements. Each exam question includes specific instructions regarding the nature of the response required. For example, the question may specify whether the answer must involve coding, explanation, interpretation, or another form of analysis. It is essential that you follow these instructions carefully. If the question requests an interpretation of results, providing only raw code or calculations without interpretation will be considered an incomplete or incorrect response. Likewise, if a question asks for a written explanation and the answer consists only of equations or numerical results, marks will be deducted. Ignoring the type of response expected may result in low or no credit.

2. Correctness and methodological accuracy. Your answers must demonstrate the correct application of theoretical concepts, formulas, and analytical methods introduced during the course. This includes implementing statistical or econometric procedures accurately, using proper assumptions, and applying models in appropriate contexts. Answers that include methodological mistakes, incorrect formulas, misapplied techniques, or unsupported reasoning will be penalized. Both accuracy in the process and correctness in the final outcome are important. In addition, formulas and equations should be properly formatted using Markdown, which is easily supported in Jupyter Notebooks. Clear mathematical notation helps ensure that your reasoning is understandable and professionally presented.

3. Interpretation and analytical insight. In addition to obtaining correct numerical or coded results, you must show that you understand their meaning. This involves interpreting figures, tables, estimation outputs, or theoretical implications correctly. A good answer explains what the results show, what they imply, and how they relate to the broader question. Superficial or incorrect interpretations, or answers that merely report numbers without context or insight, will receive lower marks.

4. Use of English language. All exams must be written in English. Responses submitted in other languages will not be evaluated. In addition to using English, it is important that your writing is grammatically correct and syntactically clear. Responses with poor grammar, fragmented sentences, or language errors that obscure meaning may receive fewer marks, even if the technical content is sound.

5. Clarity and organization. Your answer should be well structured and logically organized. It should be easy to read and understand. This includes writing in complete sentences, presenting ideas in a coherent sequence, and clearly labeling key parts of the response, such as steps in a procedure or final conclusions. Avoid excessive jargon, unclear phrasing, or disorganized content. Answers that are confusing or hard to follow may receive lower marks, even if they contain correct elements.

6. Completeness of the answer. An answer should address all parts of the question. Partial answers that omit key steps, relevant assumptions, or critical arguments will be penalized. For instance, if a question includes multiple subparts and you respond to only one, or if you present a final result without explaining how you obtained it, your answer will be considered incomplete. You are expected to provide full, coherent, and well-developed responses.

7. Code functionality and readability (when applicable). If a question requires coding, the code must be functional and should run without errors unless stated otherwise. In addition, the code should be readable, efficient, and annotated when necessary. Simply copying code from previous material without adapting it to the specific question is not acceptable. Points may be deducted if the code is incorrect, does not produce the expected output, is difficult to follow, or lacks basic explanations.

8. Use of course materials and concepts. Strong answers demonstrate your ability to connect your work to concepts, models, and tools discussed in the course. When appropriate, reference material from lectures, textbook figures or tables, or class examples to support your response. Doing so shows that you understand the broader context of the question. Answers that rely only on general intuition or personal opinion without grounding in course content are unlikely to receive high marks.

9. Exam validity: Group, time, and platform requirements. Exams must be completed according to the official conditions set by the instructor. In particular, all exams are to be taken in groups unless individual participation has been explicitly authorized in advance. You must submit the exam during the designated time period and through the required format. In this course, that means submitting your group response using the DataLab platform on DataCamp. Failure to meet these requirements may result in the submission being considered invalid.

10. Academic integrity and similarity between groups. Although the exams are completed in groups and students are not physically monitored, academic integrity is strictly enforced. If two or more groups submit answers that are significantly similar, whether due to collaboration between groups, the use of AI tools that generate identical content, or other forms of misconduct, this will be treated as a serious academic offense. In such cases, a substantial mark deduction will apply and/or the matter may be referred to the university for formal investigation of plagiarism.

Here, I define a few common verbs used in assignment and exam instructions.

1. Define. State the precise meaning of a term or concept. Your response should be concise, accurate, and free from ambiguity, often including a formal or widely accepted definition.
   
2. Describe. Provide a detailed account of the characteristics or features of a concept, process, or phenomenon. Focus on “what it is” and include relevant details, but avoid analysis or interpretation unless specified.
   
3. Explain. Clarify the “why” or “how” of a concept or phenomenon. Provide reasoning, causes, or mechanisms to ensure the topic is fully understood, often using examples or logical arguments to support your answer.
   
4. Replicate. (e.g., replicate a figure or table). Reproduce a figure, table, or result from provided data or information using appropriate tools and methods. Ensure that the replication matches the original in terms of accuracy, formatting, and presentation.
   
5. Compare. Identify and discuss the similarities between two or more items, concepts, or processes. Highlight key points of resemblance and ensure the comparison is structured and focused.
   
6. Contrast. Identify and discuss the differences between two or more items, concepts, or processes. Highlight key points of distinction while maintaining a clear structure in your response.
   
7. Evaluate. Assess the strengths, weaknesses, or implications of a concept, argument, or result. Your answer should include a reasoned judgment supported by evidence or criteria, rather than mere opinion.
   
8. Analyze. Break down a topic or problem into its essential components to examine relationships, patterns, or underlying principles. Your response should include interpretation and insight based on evidence or logical reasoning.
   
9. Extend. (e.g., extend an analysis or model) Go beyond the original scope of an analysis or model by adding new elements, perspectives, or considerations. This could involve applying it to new data, proposing modifications, or exploring additional implications.
   
10. Estimate. Provide an approximate calculation, measurement, or judgment based on available data, information, or assumptions. Clearly state the method or reasoning used to arrive at your estimate and acknowledge its potential limitations.
    
11. Comment. Provide a brief but insightful observation or opinion on a topic, issue, or result. Your response should be concise and focused, offering interpretation, critique, or additional perspective without requiring extensive elaboration.
12. Discuss. Explore a topic in depth by presenting a balanced argument or analysis. Your response should include multiple viewpoints, evidence, or examples, and may evaluate or interpret different aspects of the topic. A structured and thorough approach is expected.

5.4 Extra marks.

Extra marks activities are individual tasks. As with any extra marks activity, you will not lose points if you fail to complete them, but you can earn additional marks if you complete them on time. For each DataCamp and UN CC:Learn course completed on time, you will receive a nice PDF certificate, which looks great on your CV and LinkedIn profile.

DataCamp. You can earn extra marks for your next \(G_{E1}, G_{E1}\) and \(G_{E1}\) exams group marks by completing DataCamp assignments, which involve accumulating a specific amount of XP. XP is a measure of your engagement within DataCamp, automatically calculated based on the courses, exercises, or other content you complete. For more details, refer to the DataCamp Assignments section and the course calendar, where they are marked with the extra marks tag. The deadline is 10:00 a.m. on the date indicated in the course calendar. Since XP is calculated automatically, you do not need to submit any certificates, screenshots, or other evidence. Just make sure to complete the assignment on time.

The amount of extra marks is based on the cumulative number of DataCamp assignments completed during the semester. There are 6 DataCamp assignments in total, allocated as 2 in the first partial, 2 in the second, and 2 in the third. You earn \(n+1\) extra marks for each cumulative assignment completed, where \(n\) is the number of assignments completed so far. If you complete all of them on time, for the \(E_1\) exam you will earn:

\(X_{E1}=\displaystyle \sum_{n=1}^{2} (n+1) = (1+1) + (2+1) = 2 + 3 = 5\).

In sum:

• \(X_{E1}= \displaystyle \sum_{n=1}^{2} (n+1) = 5\) extra marks over your \(G_{E1}\).
• \(X_{E2}= \displaystyle \sum_{n=3}^{4} (n+1) = 9\) extra marks over your \(G_{E2}\).
• \(X_{EF}=\displaystyle \sum_{n=5}^{6} (n+1) = 13\) extra marks over your \(G_{EF}\).

However, if you complete only one assignment in each period, you earn:

• \(X_{E1}=\displaystyle \sum_{n=1}^{1} (n+1) = 2\) extra marks over your \(G_{E1}\).
• \(X_{E2}=\displaystyle \sum_{n=2}^{2} (n+1) = 3\) extra marks over your \(G_{E2}\).
• \(X_{EF}=\displaystyle \sum_{n=3}^{3} (n+1) = 4\) extra marks over your \(G_{EF}\).

To complete the XP assignments, you will need to choose courses, skill tracks, career tracks, practice exercises, and other types of DataCamp content on your own. You are free to select based on your personal interests and preferences. However, if you would like some guidance, I recommend starting with basic or fundamental R programming courses, data visualization in R, as well as finance courses with R. Other options include Python, SQL, Tableau, Power BI, Excel, ChatGPT, and more. Visit the “Learn” section on DataCamp to browse, search, and filter all available learning content to accumulate XP. Feel free to contact me if you would like further advice on which courses to take.

DataCamp top 5 XP for the first 30, 60, and 90 days. DataCamp has a leaderboard that ranks participants based on the amount of XP they earn. I will assign +5 extra marks to the top 5 students with the highest XP during three specific time ranges: the first 30 days, days 31 to 60, and days 61 to 90 of the semester. The allocation of these extra marks will be recorded according to the course calendar.

The dates on which I will assign these marks according to the DataCamp leaderboard are: Wednesday, February 12; Friday, March 14; and Monday, April 14.

UN CC:Learn courses. You can earn extra marks for your next \(G_{E1}, G_{E2}\) and \(G_{EF}\) exams group marks by completing UN CC:Learn courses, which are listed in the course calendar under the extra marks tag. The deadline is 10:00 a.m. on the date indicated in the course calendar. Please note that UN CC:Learn courses may require a minimum score to obtain a valid certificate.

Some students may have completed a UN CC:Learn course in the past. If your certificate is older than the starting date of the current academic term, it is not valid, and you will need to choose a different course to substitute it. There are 3 UN CC:Learn activities in the semester, allocated as 1 in the first term, 1 in the second, and 1 in the third. You will earn +5 extra marks for each completed course.

As with DataCamp, you are expected to choose the UN CC:Learn course based on your own interests and preferences. Visit the course section to browse the full catalog.

Stickers. There are some opportunities throughout the semester to get extra marks. In this course, extra marks are allocated in the form of stickers, every sticker stands for 5 extra marks on your \(G_{E1}, G_{E2}\) and \(G_{EF}\) exams group marks. They are called stickers because I give real stickers to my students. Stickers are assigned by merit. It is not very easy to get a sticker, but it is worth a try. The record so far is one student who got 30 extra marks over the \(G_{EF}\), and he passed the course partially because of that.

The wheel of fortune. I spin a virtual wheel of fortune three times during the semester to randomly allocate extra marks to a lucky group of students. Attendance in these sessions is mandatory to claim the extra marks if you are selected by the wheel. In total, you could earn +0, +5, or +10 extra marks for your next \(G_{E1}, G_{E2}\) and \(G_{EF}\) exams group marks.

Mentimeter. We may have a few sessions that include a Mentimeter activity. This activity may be evaluated, and if it is, the top 10 best answers will earn extra marks. Points will be awarded based on your rank in the top 10: the first place earns +10, the second place earns +9, and so on, with the tenth place earning +1 extra marks for your next \(G_{E1}, G_{E2}\) and \(G_{EF}\) exams group marks.

5.5 Non-graded.

A non-graded activity is a task that does not contribute to the final grade of the course.

DataCamp webinars. You are free to attend live webinars organized by DataCamp. See the live events DataCamp section for the upcoming webinars in this semester. Please note that you have to register to attend. Let me know which ones you are planning to attend.

Videos. You can record and submit one video per period (up to one per partial exam). This is an individual non-graded activity, in Spanish. The submission of this activity is by the discussion forum. I recommend you to upload the video as a YouTube link or any other similar platform so you can submit only the web url in the discussion forum. I would like to avoid others downloading the video, so I believe sharing the link is the best way to submit it. By sharing the video url will allow you to delete your video after the semester ends if you wish. The design of the video and the length is free although you have to start by introducing yourself and the course name.

There are four types of videos.

• Type 1: Feynman. The Feynman technique for teaching and communication is a mental model (a breakdown of a personal thought process) to convey information using concise thoughts and simple language. The Feynman model is named after the Nobel prize-winning physicist Richard Feynman, who was recognized as someone who could clearly explain complex topics in a way that everybody — even those without degrees in the sciences — could understand. He was also named The Smartest Man in the World in 1979. According to him: The person who says he knows what he thinks but cannot express it usually does not know what he thinks. There are four simple steps to the Feynman technique: (1) choose a concept; (2) teach it to a toddler; (3) identify gaps and go back to the source material; (4) review and simplify. Teaching it to a toddler should not take it literally, it basically means that your explanation should be as clear and simple as a toddler could understand it.

Further details about the Feynman technique here:

• Type 2: The interview. You can interview someone who can share some thoughts with us. For example, you can interview your mom or dad to discuss topics about his or her job. You are free to design the questions and the format. This could be a good opportunity to know how people in a specific industry tackle business problems, or challenges of people working in the public sector.

• Type 3: Your pet. You can show us your pet. Tell us something about your pet, and how special it is for you and your family. Do you have a spider , fish , frog , cat , dog , craw , dragon ? All kinds of pets are welcome.

• Type 4: Your hobby or talent. You may have a special artistic or sport talent you would like to share with us or a hobby which could be interesting for all. This could be a good opportunity to get to know you better.

Why is \(H_3\) a non-graded activity? The \(H_3\) is a special assignment that is designed to help you to practice and study for your final exam by learning from your previous mistakes. It has a deadline but it is a non-graded activity. Given that it is non-graded, this activity is optional. The \(H_3\) instructions are the following: You are required to (1) correct all your mistakes in all your previous graded activities, including all the four \(E_1\) and \(E_2\) questions, and assignments; and (2) complete all your missed extra mark activities, mostly from DataCamp and UN CC:Learn. The format and delivery is the same as the rest of the assignments. You can do it in group or individually. You will not receive \(H_3\) feedback as by then you will have access to all exam and assignment answers. In any case, you can ask me to review it if necessary.

6 Checklist.

As a student in this course and more broadly, as a member of the academic community, you are expected to respect the rules outlined in this syllabus and in the university’s academic regulations. These rules are not arbitrary; they exist to ensure fairness, transparency, and equity for everyone enrolled in the course.

At times, students may feel that their personal situation warrants an exception to one of these rules. It is important to understand that, even when such requests are made sincerely and in good faith, they are, in effect, asking for different treatment from that received by others, which amounts to altering the rules.

I recognize that such requests often do not come from a desire for unfair advantage, but from a genuine belief that the exception is reasonable. However, this often involves what is known as a special pleading fallacy assuming that a general rule should not apply in a particular case without an objective or institutional justification. It can also reflect a form of motivated reasoning, in which one interprets a situation in a way that aligns with what one wishes to be true.

I also understand that students may sometimes argue: Yes, I understand the rules, but what’s happening to me is unfair, and I have the right to have my case considered. As your instructor, I will always listen respectfully to your concerns. Being heard is a right. However, being heard does not guarantee that an exception can be granted. My responsibility is to apply rules consistently to ensure fairness across the board. Making individual exceptions, even with the best intentions, could compromise that fairness and create new forms of injustice toward others.

My commitment is to be clear, respectful, and fair with each of you. In return, I expect every student to approach their responsibilities with maturity, understanding that the rules are not obstacles, but essential parts of the learning environment we all share.

Please consider the following checklist to improve the chances to achieve a good performance in this course.

1. Read very consciously this document as it contains important information about the course, including the attached videos and references. You might need to read it several times during the semester. Quoting Yoda : read the syllabus you must.
2. If you consider learning how to code is very difficult and you think you need more practice, then take all the Swirl and DataCamp courses that you want or need. I teach a course of R every year, and you can also contact me if you need further help. You are expected to learn new things, and this is only one of them.
3. If I ever take longer than expected to answer an email or any other request, please insist and kindly remind me.
4. Always keep academic quality standards high for your own work and overall course performance. Find your own motivation and keep a regular weekly progress to study the course material in advance.
5. If you fail one activity do not get angry or upset as this is the best way to frustration. You better do the activity again by yourself the best way you can. You may say how can I do it again if I failed? Well, just remember we have one class session in which we discuss the correct exam and homework assignment answers, so look at your class notes again because you will find the answers there. Once you do that, you are free to contact me to comment on your work or to verify that now you know how to do it correctly. Most of the students who sadly fail this class ignore this recommendation because they are truly convinced that they do not understand or get frustrated because they fail to understand in the first attempts. Try and try again, eventually you will get it.
6. If you have trouble with your group members because they fail to work under the basic standards, and you consider it unfair to include their names in the assignment cover sheet, remember you can co-evaluate them with 0 to activate a straight penalty in his or her mark. Your homework assignment co-evaluation will remain anonymous.
7. Never get frustrated for too long because there will be no challenge that you cannot overcome with the right amount of time and effort. If after all you get frustrated, upset, or angry, do not let it happen too frequently and do not let it last for long. Ask for help whenever you need it, and remember you are free to contact me.
8. Remember the evaluation method is particularly beneficial for students because of how the average is calculated. Instead of simply averaging the grades, I use a weighted system for both assignments and partial exams. For each category, the lower grade is weighted at 30% and the higher grade at 70%. This approach ensures that stronger performance is given greater importance, encouraging improvement and rewarding effort.
9. If you would like to share something (anything) with me, feel free to do so. My email is: <martin.lozano@udem.edu>.
10. Follow number 1.

Prof. Dan Fleisch describes an effective way to ask for help in college classes.

7 Schedule.

ARF, Spring 2025. Room 3301, Monday & Thursday 16:00 – 17:30. Zoom https://us02web.zoom.us/j/81676995266

7.1 Part 1.

Session 1, Zoom. Monday, January 13.

• Reading. Course syllabus (this document).
• Activity. Welcome and introduction to the course.
• Activity. Set up your free DataCamp account. Invitations have been sent to your UDEM student email.
• Activity. Define your team of no more than 4 members.
• Extra marks. Quiz 1. At 5:00 p.m., Quiz 1 will be available on Blackboard in the “Homeworks & exams” section. This is an in-class activity; you will have 25 minutes to complete it, and access to the quiz will close at 5:10 p.m. Quiz 1 consists of 10 true/false questions about the syllabus, with each question worth 0.5 points, allowing you to earn up to +5 extra points on your \(E_1\) if answered correctly. With this activity, we will conclude the class.

Session 2, Zoom. Thursday, January 16.

• Reading. Drake and Fabozzi (2010), Chapter 9 Financial risk management.
• Complementary material. Crouhy, Galai, and Mark (2014), video 1 The building blocks of risk management; video 2 Risk management: A helicopter view; video 3 Corporate risk management: A primer.
• Complementary material. Brealey et al. (2020), Chapter 26 Managing risk. Crouhy, Galai, and Mark (2014), Chapter 1 Risk management a helicopter view. Crouhy, Galai, and Mark (2014), Chapter 2 Corporate risk management a primer.
• Extra marks. Quiz 2. At 5:00 p.m., Quiz 2 will be available on Blackboard in the “Homeworks & exams” section. This is an in-class activity; you will have 25 minutes to complete it, and access to the quiz will close at 5:10 p.m. Quiz 2 consists of 10 true/false questions about the syllabus, with each question worth 0.5 points, allowing you to earn up to +5 extra points on your \(E_1\) if answered correctly. With this activity, we will conclude the class. Quiz 1 and Quiz 2 are from the same question pool, so it is possible that you may encounter some repeated questions from Quiz 1.

Session 3, Zoom. Monday, January 20.

• Reading. Drake and Fabozzi (2010), Chapter 9 Financial risk management.
• Complementary material. Crouhy, Galai, and Mark (2014), video 1 The building blocks of risk management; video 2 Risk management: A helicopter view; video 3 Corporate risk management: A primer.
• Complementary material. Brealey et al. (2020), Chapter 26 Managing risk. Crouhy, Galai, and Mark (2014), Chapter 1 Risk management a helicopter view. Crouhy, Galai, and Mark (2014), Chapter 2 Corporate risk management a primer.
• Extra marks. Quiz 3. At 5:00 p.m., Quiz 3 will be available on Blackboard in the “Homeworks & exams” section. This is an in-class activity, and you will have 25 minutes to complete it. Access to the quiz will close at 5:10 p.m. Quiz 3 consists of 10 true/false questions about the syllabus, at a higher level of difficulty compared to Quiz 1 and Quiz 2. Each question is worth 0.5 points, allowing you to earn up to +5 extra points on your \(E_1\) if answered correctly. With this activity, we will conclude the class.

Session 4, Zoom. Thursday, January 23.

• Reading. Lozano (2024a), Chapter 1 Loan analysis.

Session 5, Zoom. Monday, January 27.

• Reading. Lozano (2024a), Chapter 1 Loan analysis.
• To know more about regression with a binary dependent variable, and references to credit risk please see Hanck et al. (2020), Chapter 11. Hull (2020), Chapter 3.
• Extra marks. Actividad +5 puntos sobe la calificación del primer parcial. Compartir, reaccionar y/o agregar un comentario en redes sociales. Subir evidencia (screenshot) al foro de discusión del primer parcial. La hora límite es 10:00 a.m. de la sesión 5: (1) Retos y oportunidades en el aprendizaje de ciencia de datos en las escuelas de negocios. Spotify ; YouTube ; LinkedIn . Café de Datos, Datlas. Temporada 9, capítulo 132. Diciembre 2023. (2) Riesgo financiero. Spotify . Hablemos de economía y política. Asociación para la Conciencia Económica y Política (ACEyP). Noviembre 2024.

Session 6, Zoom. Thursday, January 30.

• Reading. Lozano (2024a), Chapter 1 Loan analysis.
• Complementary material. Alonso and Carbó Martínez (2021).

Holiday. Monday, February 3.

Session 7. Thursday, February 6.

• Reading. Lozano (2024a), Chapter 1 Loan analysis.

Session 8. Monday, February 10.

• Reading. Lozano (2024a), Chapter 1 Loan analysis.
• Complementary material. Video: Ethics and trust in the investment profession.
• Activity. \(H_1\) review.
• Extra marks. DataCamp top 5 XP for the first 30 days. Wednesday, February 12.

Session 9. Thursday, February 13.

• Graded. Complete \(H_1\) before 10:00 a.m.
• Activity. \(H_1\) presentations.

Session 10. Monday, February 17.

• Activity. \(E_1\) review.
• Extra marks. Complete DataCamp assignments. It is not required to send evidence of having completed the tasks because the system automatically records if they were completed on time.
• Extra marks. Complete one UN CC:Learn course. Submit your PDF certificate before 10:00 a.m. by the discussion forum.

Session 11, Zoom. Thursday, February 20.

• Graded. \(E_1\) Instructions in Blackboard.

7.2 Part 2.

Session 12. Monday, February 24.

• Activity. Discuss \(E_1\) answers.
• Extra marks. The wheel of fortune 1/3. This is a class activity.
• Graded. Submit \(H_1\) and \(E_1\) co-evaluation before 10:00 a.m. Instructions in Blackboard.
• No conozco la fecha límite, pero deben completar la encuesta avanza 180 por favor.

Session 13. Thursday, February 27.

• Reading. Hull (2015), Section 24.1 Credit ratings; Section 24.2 Historical default probabilities; Section 24.3 Recovery rates.
• Complementary material. Fictional video (warning, it has offensive language): FrontPoint partners confronts Morgan Stanley risk assessors and S&P.
• Complementary material. PowerPoint slides: John C. Hull.

Session 14. Monday, March 3.

• Reading. Hull (2015), Section 24.6 Using equity prices to estimate default probabilities.

Session 15. Thursday, March 6.

• Reading. Hull (2015), Section 24.6 Using equity prices to estimate default probabilities.
• Reading. Lozano (2024a), Chapter 2 The Merton model.
• Complementary material. Brealey et al. (2020), Chapter 23 Credit risk and the value of corporate debt.

Session 16. Monday, March 10.

• Reading. Hull (2015), Section 24.6 Using equity prices to estimate default probabilities.
• Reading. Lozano (2024a), Chapter 2 The Merton model.
• Complementary material. Video: FRM: How \(d_2\) in Black-Scholes becomes PD in Merton model.

Session 17. \(\pi\) Thursday, March 13.

• Reading. Hull (2015), Section 24.6 Using equity prices to estimate default probabilities.
• Reading. Lozano (2024a), Chapter 2 The Merton model.
• Complementary material. Video: Measuring credit risk.
• Activity. \(H_2\) review.
• Extra marks. DataCamp top 5 XP for the days 31 to 60. Friday, March 14.

Holiday. Monday, March 17.

Session 18. Thursday, March 20.

• Graded. Complete \(H_2\) before 10:00 a.m.
• Activity. \(H_2\) presentations.

Session 19, Zoom. Monday, March 24.

• Activity. \(E_2\) review.
• Extra marks. Complete DataCamp assignments. It is not required to send evidence of having completed the tasks because the system automatically records if they were completed on time.
• Extra marks. Complete one UN CC:Learn course. Submit your PDF certificate before 10:00 a.m. by the discussion forum.

Session 20, Zoom. Thursday, March 27.

• Graded. \(E_2\) Instructions in Blackboard.

7.3 Part 3.

Session 21. Monday, March 31.

• Activity. Discuss \(E_2\) answers.
• Extra marks. The wheel of fortune 2/3. This is a class activity.
• Graded. Submit \(H_2\) and \(E_2\) co-evaluation before 10:00 a.m. Instructions in Blackboard.

Session 22. Thursday, April 3.

• Reading. Drake and Fabozzi (2010), Chapter 15 Investment management.
• Complementary material. The Plastic Recycling Myth.

Session 23. Monday, April 7.

• Reading. Hull (2015), Chapter 22 Value at Risk (sections 22.1 to 22.4).
• Reading. Lozano (2024b), Chapter 5 Value at Risk.

Session 24, Zoom. Thursday, April 10.

• Reading. Hull (2015), Chapter 22 Value at Risk (sections 22.1 to 22.4).
• Reading. Lozano (2024b), Chapter 5 Value at Risk.
• Extra marks. DataCamp top 5 XP for the days 61 to 90. Monday, April 14.

Holiday. Monday, April 14.

Holiday. Thursday, April 17.

Session 25. Monday, April 21.

• Reading. Drake and Fabozzi (2010), Chapter 16 The theory of portfolio selection.
• Complementary material. Brealey et al. (2020), Chapter 8 Portfolio theory and the capital asset pricing model.
• To know more about portfolio selection see Ruppert and Matteson (2011), Chapters 16.
• Non graded. Mention DataCamp in social media if possible. Instructions.

Session 26. Thursday, April 24.

• Reading. Hull (2015), Section 23.8 Estimating volatilities and correlations.
• Reading. Lozano (2024b), Chapter 5 Value at Risk.

Session 27. Monday, April 28.

• Reading. Hull (2015), Section 23.8 Estimating volatilities and correlations.
• Reading. Lozano (2024b), Chapter 5 Value at Risk.

Holiday. Thursday, May 1.

Session 28, Zoom. Monday, May 5.

• Reading. Hull (2015), Section 23.8 Estimating volatilities and correlations.
• Reading. Lozano (2024b), Chapter 5 Value at Risk.
• Non-graded. Complete \(H_3\) before 10:00 a.m.
• No conozco la fecha límite, pero deben completar la encuesta avanza 360 por favor.

Session 29, Zoom. Thursday, May 8.

• Activity. \(E_F\) review.
• Extra marks. Complete DataCamp assignments. It is not required to send evidence of having completed the tasks because the system automatically records if they were completed on time.
• Extra marks. Complete one UN CC:Learn course. Submit your PDF certificate before 10:00 a.m. by the discussion forum.
• Extra marks. The wheel of fortune 3/3. This is a class activity.
• Farewell .

7.4 The end.

Final exam, Zoom. Thursday, May 22, 16:00 – 18:00.

• Graded. \(E_F\) Instructions in Blackboard.

8 Internet resources.

The amount of free online resources and references to learn R, and its applications in finance and economics is huge. This list is constantly growing.

8.1 Learn .

Consider the following specific online and free resources to start or continue learning R.

• As my student you have a free DataCamp account. You can find at least 151 courses and 45 projects about R.
• LearnR. An interactive introduction to data analysis with R. In this course, you’ll learn the basics of using R for data analysis. This should provide you with the necessary skills to use R when learning more advanced and specialised topics. You don’t need any prior experience with R, statistics, or programming to work through this material, however if you already have some experience you can start from any chapter you’d like to learn from.
• Swirl teaches you R programming and data science interactively, at your own pace, and right in the R console.
• Interactive Tutorials for R. The learnr package makes it easy to turn any R Markdown document into an interactive tutorial.
• R for Dummies. De Vries and Meys (2015).
• Introduction to Econometrics with R. Hanck et al. (2020).
• Introduction to Econometrics with R. Oswald et al. (2020).
• Using R for Introductory Econometrics. Heiss (2020).
• Handbook of Regression Modeling in People Analytics. With Examples in R and Python. McNulty (2021).
• R Programming for Data Science. Peng (2016).
• ggplot: elegant graphics for data analysis. Wickham (2016).
• Bookdown: Authoring Books and Technical Documents with R Markdown. Xie (2021).
• R Markdown Cookbook. Xie, Dervieux, and Riederer (2020).
• R markdown: The Definitive Guide. Xie, Allaire, and Grolemund (2021).
• R for Data Science. Grolemund and Wickham (2018).
• To understand how R Markdown works: R Markdown guidelines.
• Thiyanga Talagala: A detailed R Markdown guide.
• Let’s Git started. Bryan (2018).
• Probability, Statistics, and Data: A fresh approach using R. Foundations of Statistics with R.

8.2 YouTube installation guides.

In principle, as we are using Deepnote, you do not need to install any software on your computer. However, you may be interested to work with R on your local computer. Here is a good list of YouTube installation guides to do so.

As you may understand, R and RStudio versions are frequently updated and people regularly upload installation guides on YouTube. If you find a newer video for installing a newer version please share. In any case, these videos can definitely help you as a guide to install the newer version available.

• Download & Install R 3.6.3
• Download & Install RStudio Desktop 1.3.959
• Installing R and Rstudio on MacOS.
• Orientation and Setting up R (Setting up R)
• Orientation and Setting up R (Setting up RStudio)
• RStudio: A Guided Tour (by Jamison Crawford).
• How to Install RStudio (and Knit to PDF).
• RStudio: Explaining the Interface & R Markdown.
• How to Connect RStudio with Git (Github) for Cloning and Pushing Repo.

8.3 Software.

Main technology used in this course.

• The R Project for Statistical Computing. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
• RStudio. Inspired by innovators in science, education, government, and industry, RStudio develops free and open tools for R, and enterprise-ready professional products for teams who use both R and Python, to scale and share their work.
• DataCamp. Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.
• Tiny\(\TeX{}\). A lightweight, cross-platform, portable, and easy-to-maintain \(\LaTeX{}\) distribution based on \(\TeX{}\) Live.
• Compile R online.
• \(\LaTeX{}\) base. A web-based \(\LaTeX{}\) editor with live document preview.
• Overleaf. The easy to use, online, collaborative \(\LaTeX{}\) editor.
• \(\LaTeX{}\). \(\LaTeX{}\) is a high-quality typesetting system; it includes features designed for the production of technical and scientific documentation.
• Git. A free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
• GitHub. A provider of Internet hosting for software development and version control using Git. It offers the distributed version control and source code management functionality of Git, plus its own features.

8.4 Blogs and resources.

Here you can find questions and answers about programming in R.

• R-Bloggers is a blog aggregator of content contributed by bloggers who write about R (in English). The site helps R bloggers and users to connect and follow the R blogosphere.
• Stack Overflow. Founded in 2008, Stack Overflow’s public platform is used by nearly everyone who codes to learn, share their knowledge, collaborate, and build their careers.
• R and Data Mining.
• Revolutions. Milestones in AI, Machine Learning, Data Science, and visualization with R and Python since 2008.
• R-ladies is a worldwide organization whose mission is to promote gender diversity in the R community.
• These sources are the ones that most often hold the data that social science students and researchers at Tufts are looking for. Social Science Data and Statistics Resources.
• Stack Exchange is a question and answer site for users of \(\TeX{}\), \(\LaTeX{}\), Con\(\TeX{}\)t, and related typesetting systems.
• Kaggle. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.
• A gap exists between the Data Scientist’s skillset and the Business Objectives. Learn Data Science.
• Rdatasets A collection of nearly 1500 datasets that were originally distributed alongside the statistical software environment R and some of its add-on packages.

8.5 Others.

• ProjectElon. Study Motivation.
• iPanda. Pandas are precious and vulnerable species in the world today.
  
• X hashtags: #rstats, #DataScience

This document took 2.38 seconds to compile in Quarto version 1.5.54, and R version 4.4.1 (2024-06-14 ucrt).

References.

Alonso, Andrés, and José Manuel Carbó Martínez. 2021. “Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation.” Documentos de Trabajo/Banco de España, 2105. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3774075.

Boon Ng, Soo et al. 2019. “Exploring STEM Competences for the 21st Century.” https://unesdoc.unesco.org/ark:/48223/pf0000368485.

Brealey, Richard A, Stewart C Myers, Franklin Allen, and Pitabas Mohanty. 2020. Principles of Corporate Finance. 13th ed. McGraw-Hill Education.

Bryan, Jennifer. 2018. Happy Git and GitHub for the UseR. GitHub. https://happygitwithr.com/.

Crouhy, Michel, Dan Galai, and Robert Mark. 2014. The Essentials of Risk Management. 2nd ed. McGraw-Hill.

D’Ignazio, Catherine, and Lauren F Klein. 2020. Data Feminism. MIT Press. https://datafeminism.io/.

De Vries, Andrie, and Joris Meys. 2015. R for Dummies. 2nd ed. John Wiley & Sons. http://sgpwe.izt.uam.mx/files/users/uami/gma/R_for_dummies.pdf.

Drake, Pamela Peterson, and Frank J Fabozzi. 2010. The Basics of Finance: An Introduction to Financial Markets, Business Finance, and Portfolio Management. Vol. 192. John Wiley & Sons.

Grolemund, Garrett, and Hadley Wickham. 2018. R for Data Science. https://r4ds.had.co.nz/.

Hanck, Christoph, Martin Arnold, Alexander Gerber, and Martin Schmelzer. 2020. Introduction to Econometrics with R. Essen: University of Duisburg-Essen. https://www.econometrics-with-r.org/.

Heiss, F. 2020. Using R for Introductory Econometrics. Independently Published. http://www.urfie.net/read/index.html.

Hull, John C. 2015. Options, Futures, and Other Derivatives. 9th ed. Prentice Hall.

———. 2020. Machine Learning in Business: An Introduction to the World of Data Science. Amazon Distribution.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning. Second. Springer. https://hastie.su.domains/ISLRv2_website.pdf.

Klein, Lauren F, and Matthew K Gold. 2019. Debates in the Digital Humanities 2019. Project Muse. https://dhdebates.gc.cuny.edu/.

Lozano, Martín. 2024a. Credit Risk with R. GitHub Pages. https://mlozanoqf.github.io/tutorial_arf/.

———. 2024b. Options and VaR with R. GitHub Pages. https://mlozanoqf.github.io/tutorial_if/.

McNulty, Keith. 2021. Handbook of Regression Modeling in People Analytics: With Examples in R and Python. Chapman; Hall/CRC. http://peopleanalytics-regression-book.org/index.html.

Oswald, Florian, Vincent Viers, Pierre Villedieu, and Gustave Kennedi. 2020. Introduction to Econometrics with R. Paris, France: SciencesPo Department of Economics. https://scpoecon.github.io/ScPoEconometrics/.

Peng, Roger D. 2016. R Programming for Data Science. Leanpub. https://bookdown.org/rdpeng/rprogdatascience/.

R Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Ruppert, David, and David S Matteson. 2011. Statistics and Data Analysis for Financial Engineering with R Examples. Vol. 13. Springer. https://github.com/mlozanoqf/Statistical-Computing .

UN. 2015. UN General Assembly, Transforming Our World : The 2030 Agenda for Sustainable Development. U.N. https://www.refworld.org/docid/57b6e3e44.html.

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Bookdown. https://ggplot2-book.org/index.html.

Xie, Yihui. 2021. Bookdown: Authoring Books and Technical Documents with R Markdown. CRC Press. https://bookdown.org/yihui/bookdown/.

Xie, Yihui, Joseph J Allaire, and Garrett Grolemund. 2021. R Markdown: The Definitive Guide. CRC Press. https://bookdown.org/yihui/rmarkdown/.

Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R Markdown Cookbook. CRC Press. https://bookdown.org/yihui/rmarkdown-cookbook/.