Introduction

The world of financial engineering is vast, and education has now become more accessible through Massive Open Online Courses (MOOCs). Two notable courses are Computational Investing and Financial Engineering and Risk Management. This article provides a detailed comparison of these two MOOCs, focusing on curriculum content, instructors, format and structure, duration and commitment, cost, reviews and ratings, certification, and outcomes. By the end of this comparison, you will have a clear understanding of which course might be more suitable for you.

Course Overview

Two courses in financial engineering have been reviewed and compared:

Computational Investing - A Python-based course designed to teach the components of a trading system and build a basic trading system using Python and QSTK. Financial Engineering and Risk Management (FERM) - A math-heavy course starting from basic no-arbitrage concepts to mortgage pricing, with no programming components.

Curriculum Content

The curriculum content of these two courses is quite different but equally important for a holistic understanding of financial engineering.

Computational Investing (CI) - The course primarily focuses on practical applications of quantitative methods. You will learn about the different abstract components of a trading system and how to implement them in Python. Financial Engineering and Risk Management (FERM) - This course is heavily theoretical, covering concepts such as no-arbitrage, financial theory, and mortgage pricing. It offers a deeper understanding of the foundational principles of financial engineering.

Instructors

The instructors play a crucial role in the success of a course.

Computational Investing - The instructors of CI are knowledgeable in Python programming, which complements the practical aspects of the course. However, the depth might be shallower compared to FERM. Financial Engineering and Risk Management (FERM) - The instructors of FERM are likely to be experienced in pure finance, offering theoretical insights that might be more comprehensive than CI.

Format and Structure

The format and structure of courses can significantly impact learning outcomes.

Computational Investing - CI is self-paced, which allows flexibility in learning but might lack the structure of a fixed schedule. The content is spread across multiple areas, touching on many topics but not in depth. Financial Engineering and Risk Management (FERM) - FERM follows a structured format with fixed schedules, making it easier for learners to keep a consistent pace. The content is more focused on theoretical concepts.

Duration and Commitment

The duration and expected weekly time commitment of each course are as follows:

Computational Investing - The course is relatively shorter with a lighter weekly commitment, making it easier to fit into a busy schedule. Financial Engineering and Risk Management (FERM) - FERM requires a more substantial time investment due to its theoretical focus and structured format.

Cost

Both courses offer different cost structures.

Computational Investing (CI) - CI is free but does not offer a certification upon completion, which might be a drawback for those seeking recognition. Financial Engineering and Risk Management (FERM) - FERM components are accessible without any payment, and the course offers a certificate that might be more valuable for professional development.

Reviews and Ratings

Reviews and ratings from past participants give valuable insights into the effectiveness of a course.

Computational Investing (CI) - CI receives mixed reviews, mainly because it has fewer lectures with less depth. However, for those interested in practical applications, the course is highly recommended. Financial Engineering and Risk Management (FERM) - FERM is often rated highly for its detailed theoretical content, though the dryness of the theory can be a drawback for some learners.

Certification and Outcomes

Both courses offer different outcomes, depending on your learning goals.

Computational Investing (CI) - The lack of certification might be a disadvantage for individuals seeking formal recognition in the industry. Financial Engineering and Risk Management (FERM) - The certificate provided by FERM can be a valuable addition to your professional credentials, especially if you are looking to advance in financial engineering roles.

Conclusion

Both Computational Investing and Financial Engineering and Risk Management offer unique value propositions. CI is a more practical course, perfect for those looking to apply quantitative methods and build a basic trading system using Python. On the other hand, FERM is more theoretical and math-heavy, ideal for learners who want a deep understanding of the principles of financial engineering. By taking both courses, you can gain a comprehensive understanding of the field and cater to your specific learning needs.

While both courses have their strengths and weaknesses, they can significantly complement each other. By the end of this comparison, it is clear that the choice between these financial engineering MOOCs depends on your learning preferences and career goals. Whether you are looking to enhance your practical skills with CI or deepen your theoretical knowledge with FERM, both courses offer valuable insights into the world of financial engineering.

Keywords

Financial Engineering MOOCs, Comparative Analysis, Python Programming, Theoretical Finance