Avis et commentaires pour d'étudiants pour Python and Statistics for Financial Analysis par Université des sciences et technologies de Hong Kong

Thank You

Awesome!!

Very good

Perfect!

NICE ONE

Thanks

good !

v.good

Ótimo.

good.

great

great

good

nice

good

Good

Good

good

good

good

Using short videos was a good way to keep things interesting. The course was broken up into very manageable sections so I never felt I had too much work to complete in order to progress to the next section (especially since I work long hours and do not have much free time). The videos, along with the subtitles at the bottom of the page, were clear and easy to understand. The exercises were a little disappointing in my opinion. I believe the best way to learn most programming language is to type out the code from scratch and test at every step as you go along. I understand that some sections of the code we used to the analysis were complex, so my suggestion is to only include those parts of the code in the exercises, and have the student type out the easy parts repeatedly. For example the from excel, print, head, tail and other easy code can be filled out by the students instead of already having it in place. This will really help nail down the syntax and nuances of the language. You can include a help button that shows the correct code if the students can't figure it out themselves. Overall I'd give this course a 8.5/10 since I was able to apply this knowledge easily to my work. Thank you, Coursera & Xuhu Wan!

Jitendra De Silva

Not for beginners, but very condensed and a good summary if you know these already.

The course contains very condensed information which combines: statistical inference methods, intermediate python language and evaluation methods of trading strategies.

I would not recommend it if you have not done at least two of three: a) Completed basic statistics course b) Completed a beginner to python programming course c) Understand the basics of trading, creating and evaluating trading strategies (sharpe ratios, overfitting etc).

For me it was a pleasure to see such information condensed, as I've refreshed my econometrics (ie statistical inference methods) knowledge, I can use the code to create my own variations of strategies and dig deeper to testing and training of trading models.

But overall I would struggle if I would be missing knowledge, as every single word from the professor has a very specific reason to be there. Every words matters and is used to create a solid line of logic.

English could be better, but I don't care about that. All was clear to me.

Learned a lot, however, it gets complicated really quickly. My intention for this course was to learn more about how Python is used in business analysis as this is something I am planning to do for college. I feel that it would be greatly appreciated if even basic statistics tools would be expounded more on as I did not really know these and it took a toll on my progress in this course. I would also appreciate if the labs were more hands-on, meaning that we would be the ones to build the code and applying our understanding of the videos. Although in certain labs this is done, some of the labs would just let me run the code that was already pre-typed. I think that allowing students to apply their understanding would allow them to remember more the lessons provided by the course.

I learned a lot and I greatly appreciate Mr. Wan for creating this course as it will certainly help me in the future.

A fine introduction to the use of statistical models for finance (stock trading), showing its implementation in Python. It is NOT a course in either Python or Statistics but shows what one should learn. Alas, it does not give any pointers as to where to go to delve deeper into the needed statistics (nor trading, for that matter). It contains a fair summary explanation of linear regression models, but the recipes for their evaluation are discussed way too briefly.As for Python, it uses 4 common important libraries and directs the student to the corresponding sites. It gives no explanations as to the kind of structures being manipulated. The Jupyter notebooks are well set-up for practice.

The course provides an overview of how to build a quantitative trading model. However, the instructor does not go into details while either introducing python functions to someone unfamiliar with the language or talking about statistical concepts. I could follow the code based on my background in other programming languages.I will be following up this course with other courses that go in depth on both the programming and statistics front.The Jupyter notebooks are quite helpful and I will be using them for future reference.3.5 would probably be a more honest rating of the course but I don't think the course could have taught the learner more given its length.