What Students Said About My MOOC (Spring 2013)

Posted on May 25, 2013 by

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The Good, The Bad, and The Ugly: These are quotes provided by students who completed the course I taught with TA Sourabh Bajaj via coursera in Spring 2013.

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Background

These quotes were selected by me from over 900 comments provided by the students.  There is certainly a potential for bias, so take these with a grain of salt.  My goal in selecting these quotes was to:

  • Help a potential future student know what to expect , and
  • Cover each of the major pros and cons of the course.

As you can see, I think I included a balanced set of negative and positive comments. As an instructor, my overall takeaway from these responses:

  • The course was great for folks in the “sweet spot:” namely students with strong programming skills and an introductory level experience in finance.
  • Students with a more advanced background in finance found the content less rigorous than they’d like. However, they seem to have appreciated and learned from the programming component.
  • The video production needs improvement in some cases.

I hope you find these quotes useful and informative.

The Good: Student responses to “What was the best thing about the course?”

The exercises effectively demonstrated the topics and provided a useful tool to use after the course ended.

Even the most complicated ideas were presented in a simple and engaging manner.

Bridge the gap between theory and real investing

The way the theory is presented by Dr Tucker Balch – very accurate descriptions, easy to comprehend examples even for someone who never had experience with financial markets and engineering.

The guest lectures were really helpful in understanding some of the current trends in the industry. Lastly, the help and ideas exchanged via the forums as a result of the active participation of staff and the students was a strong source of learning and support through the course

Coding the market simulator was by far the best/coolest thing about the course.

Knowing how to use Python in quantitative trading.

The best part was the assignments. Learning by doing was most effective. They were challenging, I submitted close to the deadline a few times and some days I finished past midnight and had to apply late days, but the challenge is also a big component in what makes them useful. A very close second best was the forums, good discussion between students and good interaction with the professor and TA.

The course was really online and not just a record, there was a real interaction with instructors, professor, and other students. The course was really alive. the materials were didactic.

Starting from zero, the class gave me a good grasp on how stocks work. The initial sessions describing market cap and index investing were tremendously interesting. The descriptions of the way orders are processed were great!

All the extra explanation about the stock market buying and selling and other mechanism, these were very good. Also the instructor was very engaging.

The comfortable, familiar feeling I got every time I watched a video from Tucker

It was my first Coursera course. I really liked the setup / videos / quizzes. It worked out very well for me. Gave me new ideas and tools without burying me under maths. I started to use Python thanks to the course (+ add-on packages). I liked the interviews with real market makers / investors. I am really looking for the second part of this course. Thanks Mr Tucker.

Dr. Tucker’s enthusiasm and learning about tips and tricks that one may not necessarily learn from a book on computation investing.

I thought it was pitched at the right level for someone with experience of other programming languages

1. Theories and concepts were well explained. 2. Exposed to QSTK libraries, python numpy and pandas 3. Very active forum

It was Hands on

The feedback from Instructor and students were fantastic in helping slow starters

Enthusiasm and dynamism of the instructor, and also the course team as a whole.

I liked the curriculum. I felt it was paced well and built on top of previous things well.

The different interviews were really engaging A really good introduction to the basics of computer investing. Seeing the event studies turn into orders turn into statistics about the portfollio was great. I now really want to dive deeper into the subject.

The fact that I reinforced my understanding of the theory by developing software to implement it. I am relatively new to Python and pandas and found that my Python knowledge and experience was greatly enhanced by participating in this course. Also, I like the fact that I now have some working software that is readily generalised and extended. So you have “taught me to fish” rather than “feed me”! Thank you!

Being able to code simple things and have a real hands-on 1st experience on the topic (as opposed to other classes, that are more theory oriented) The interviews that Tucker made of financial industry professionals were very interesting also I’m really looking forward to a part 2 of this class, where Tucker could talk more about how he applies his machine learning knowledge to finance.

The software package provided by the instructor is very easy to use. Thanks very much!

Tucker is a highly engaging lecturer and was a pleasure to watch; his active involvement in the forums was also greatly appreciated.

Exposed to a lot of new things that I had never heard about before especially pandas.

Very good starter for those who don’t have any knowledge about stock markets. Learning Pandas and other numerical tools and Statistical tools in python. Engaging programming assignments that push you to learn by your own.

when finishing the course, we got a back testing system

That we were able to actually simulate events and create a framework that really allows us to test, to a reasonable degree, our hypotheses and understandings on real market data.

1. Easy to digest introduction to computational investing. The examples and homework helped solidify the material and actually left me with something to work with and improve going forward. 2. Got me back to programming after almost 20 years. Taught me python, but more importantly opened up possibilities for “Big Data” processing. I was impressed with the power of Pandas. 3. Gives me a better assessment of the what the talking heads at CNBC are saying. Perhaps I can quit watching it now as there is very little investing wisdom there! 4. Opened up a whole world of learning – especially for someone who took early retirement. 5. The TAs did an excellent job supporting the forum. Kudos to them. 6. Looking forward to the follow-on class and keeping up with your blog. 7. Thank you for being a pioneer on coursera.

The best thing about this course is the use of Python, Numpy and Panda. I learnt all these just by taking the course, and I believe these tools will prove useful in my later career. Despite what some students might have said about the course prerequisites being too hard, I disagree. This is the right amount of difficulty required for such a course like this. The concentration of programming is just right in this course (although the later assignments, especially the last 3, was much easier than the first few). Also, the second best thing was the interviews. They contain a lot of insights that, to be honest, I might not be able to absorb all at once. But it’s extremely useful to offer a brief look into the industry. I wish there were more of these.

pretty damn cool explanation of the complex stuff

I have a history with both stock trading and Python programming, so giving just enough basics to ensure I had the right foundation was a nice introduction. Without it, I would’ve been concerned that I started out incorrectly.

Honestly, I loved this course, and thought everything was tailored just right. It’s difficult to teach so many people with a single curriculum, but it was masterfully done.

The biggest benefits is the empowerment I felt at the end of the course that I could do things on my own. It was not a magic bullet in how to earn money in the stock market, but it was not meant to be. Rather, it provided training in how to use tools to build your own knowledge from here on out. Thank you for the class!

The best part of Computational Investing Part I was the instructor’s and TA specific preparation for this course plus their interaction on the forums. Other “MOCCs” will show you videos shot during class sessions – this class, being taylor-made for the Coursera experience was crucial to its success. Second (do I get a second?) I liked learning Python with a purpose.

The quality of instruction and forum discussions were terrific. Fun, rewarding, and really well done.
I enjoyed that the videos were short. I prefer many short videos to few longer ones.

Tucker. He’s a joy to watch. The first time I heard the music had me laughing out loud. I like his dry humor; those were great CEO stick figures. I liked being able to see him (for gestures and other body language) along with the content he was presenting. The content was still large enough. I also liked the coffee cup labled “coffee.”

Explaination of the essence of high frequency bidding vs long-time investing.

The way each lecture/assignment kept building on previous topics to build a tool to analyze the data.

I loved the teaching style of Professor Tucker Balch. I took both: the first and the second offerings of the course, and I think the 2nd one is just perfect. Though, the 1st one had many issues that were improved. Looking forward for Computational Investing Part II ;)

My favorite part of this course was all of the cool programming components that we completed. I thought it was a great experience creating those, and I love that I now have those at my disposal to use for my own personal applications.

Very well crafted and delivered. Great balance of concepts and practical skills. Excellent support via forums.

I tool this course twice. both offerings. I felt the improvements to the second course to be significant. In particular the additional time to explain Numpy and Event Matrix manipulation to be extremely helpful.

I was not a programmer, so I loved how I could learn stuff about quant investing even if I didn’t fully understand all the assignments. That being said, through many helpful people on the forums, I was able to complete most of the assignments and it felt great to do something new. I feel like I have a better understanding of some modern investing strategies and may pursue more education about in the future.

The way it combined active portfolio management theory with small programming projects and homework assignments. The short and succinct pacing of the video lectures.

*Python/numpy/pandas – new to me *portfolio theory/metrics and application to my personal investment management *lively forum – energetic, knowledgeable and funny participants, staff participation * Sourabh! – incredibly dedicated, responsive, helpful (Tucker, too) *overall fantastic experience!

For me the idea of matrix algebra as a representation of market activity and its subsequent ease of use in analysing the market was an eye=oppener.

It gave a glimpse into a field that I’m interested in but don’t use in my daily job

I came to the course with some history of programming (but not in Python) and a degree that included economics. I chose this course as it offered an interesting blending of the two. Inevitably the investment material was simpler than I would have liked but the programming side was generally more challenging. Best thing about the course was the gradual building of a platform to identify and test approaches to computational investing and gaining an insight into what this investment space offers – opening a door for my own further research. Other comments: Piazza worked well – interesting debates and excellent participation by Tucker and the TAs. Thanks for the making this course available.

I’ve learned Python, Numpy, Pandas from scratch. And QSTK rocks. The forums were very helpful, to the extent I never needed to post, everything was answered by the time I had a question. The videos were fun, I really liked the extra “studio works” with the background city image and the music. Programming exercises were very helpful. Also the interviews were cool, could have more of that. Above all: it all came together into one big picture and I’ve learned things I can use in real life right away. Thank you very much for Tucker and the entire team!

The participation of the teacher in the forums.

I liked the fact that even a person without too much knowledge on the trade market (like me) could follow and learn a lot with the course.

I particularly liked the homeworks. They were not tedious like in many other courses and instead provided one longer in-depth assignment per homework. The assignments were an essential part of the cours in creating a picture of how a trading and backtesting platform works. The instructor explicitly stated the assumptions made during the course, and provided convincing arguments and additional readings to support those assumptions.

The hand-on nature of it.

Learning more about portfolios and how to measure the performance of a portfolio. I also like learning Python and how to pull stock information via Yahoo using Python.

the right mix between theory and practise

Lectures easy to undersatnd, the lecturer was giving much effort to make the course practically useful, interesting and even entertaining, which is highly appreciated

the last half of the class was excellent and I found myself wishing it had another few homeworks and weeks. I’ll definitely take a Computational Investing II if it’s offered.

Tucker’s teaching style. His ability to clearly explain what may otherwise be difficult material. Using python. Learning about numpy, pandas and qstk, since they will be helpful in other areas of my work. The 1 dollar business and explanation of valuing a business was clever.

A truly enlightening expertience. if anything it was too short and I’d love to learn more

Presented a lot of good material with excellent support from the examples and forums. The instructor’s response to issues on the forums was heroic.

Having done the course last October, the improvements in Video Lectures and Homeworks was substantial. Additional videos from last time were very welcome, and the structure of the Homeworks was superior.

The programming assignments. Easy, but required a lot of thinking to come up with efficient solutions.

The course effectively taught the basic concepts of hedge fund like investing

I really liked that the assignments slowly improved on past assignments so that by the end of the course we had a large project with more value than several disconnected mini-projects.

I especially liked the interviews. They added an element I couldnt have found by googling and I felt i actually learned the most from that segment including the order flow from broker to collocated servers a the NYSE.

The Bad: Student answers to “Which aspect of the course needs the most improvement?”

More material and lectures

There isn’t much content in the course. Definitely I learnt a lot, but there seems to be very little content relative to the duration of the whole course.

Stock data supply is US centric – lots of frustration when trying to repeat course work with international stock. Interviews are a very attractive idea but were only semi-inspiring; need more ‘connection’ with course material.

Although I was somewhat familiar with python, I believe the coding assignment in week 3 or week 4 might have been quite difficult for many others. I don’t believe it should be made easier however but instead, some knowledge of python/coding should be a course prerequisite.

1. The materials on investing are too basic, most of which have been covered in CFA level 1 curriculum. 2. Lacked introdution to pandas dataframe, advanced Numpy features, and algorithmic trading techniques. 3. Relied too heavily on QSTK, which is merely a small database for backtesting; didn’t mention how to backtest on other databases with Python.

The latter part of the course was not prepared well enough. Course went from Excel based (last time) to Python based without sufficient preparation. Should not be modifying the course during the course. Adding the extra week was not good. Threw off my schedule with other activities. Please prepare the whole course BEFORE you offer it next time. The Python programming instruction was not sufficient to get the most out of numpy, etc. in a reasonable time. Provide more programming instruction, more examples, etc. I spent way too much time on this aspect and not enough learning investment concepts. Video technical quality control was not good. Varied from lesson to lesson. Sound quality, etc.

As an introductory course this was very good and it hard for me to think about any improvements (perhaps the only problem was song intro sync with lecturer speach in some lecture videos) I would appreciate more programming assignments and more extended scope (more theory from quantitative analysis domain) in an advanced course.

I don’t have any major complaints. Obviously there were times when materials were a bit late, but this is the other side of the coin of having such a dynamic hands-on course team, and I liked it the way it was. If we did have a computational investment 2, I’d be very interested. Hopefully this would be very practical but also contain a bit of theoretical background too on the principles as to why particular techniques do or do not work. (I’ve studied machine learning in two courses so far. One was Andrew Ng’s coursera course which was excellent, very practical but covered enough of the principles of the underlying maths so you felt confident the techniques made sense. The other was reading a book called “Machine Learning for Hackers” (using R) which unfortunately was wholly practical and ignored the theoretical aspects, to the point where there was little or no understanding of the assumptions underlying the models etc. Hopefully comp investment 2 would be practical without completely sacrificing theoretical insights.)

Homework descriptions (too many errors on the wiki)

Video production quality

Amount of information (need to be more condensed) presented in lectures

Needs more rigor and clarity in materials. For example first day return (=0) of any study should not be included in average or std. deviation. When a black box returns an unexpected value it should be explained (e.g., number of events found by EventStudy versus number counted in calling program were very different. The reason was eventually clear but should have been explained in videos before using routine.) I also still believe coursera forums are easier to use than Piazza.

The last homeworks were too much copy-pasting, and they didn’t really test for any real understanding of the topic. Please make them more challenging.

More on machine learning applications with daily return data. This course should have a follow on, on coursera. Not a private course to attend in person at Georgia Tech with a 1500 dollar fee. That makes me sad :( Great course tucker and team though, keep up the great work!

I would like to see a bigger focus on the financial side rather than the programming side

Please note these are suggestions for improvement, not complaints. We should have used modern openly available libraries such as pandas. We were stuck with an old version that had some quirks. In written text, it should be more clearly explained how data in exercises needs to be sourced and why. E.g. if we use column X from dataset Y, why is it this specific column and why that data set. What other options are there? Why is this the best one? If the teacher thinks establishing that should be a part of the exercise, it should be a clearly stated goal. I would have liked to see more advanced subjects and to see how we could apply the concepts we learned more efficiently. My code felt slow when implemented according to the instructions. Finally, the slides could be a bit richer with more detailed backup material/text and pre-designed figures. Hand drawn explanations are very good but could use some support. Overall, I think it was a great introduction but it often felt too slow. Some concepts were repeated too many times – the pacing is fine if someone has lots of time for this and needs to learn from scratch. Many thanks.

More focus. E.g. alpha in CAPM was explained maybe 5 times in different lectures – way too much for such a simple concept.

Need a little bit more focus on investing aspects than programming.

The Ugly: Student answers to “Which aspect of the course needs the most improvement?”

it is not clear whether I will have any use for the things I learned.