Too many mistakes in the use of English throughout the course made it hard for me to follow the course properly without getting annoyed. The instructor's English was terrible as well, just too many mistakes, which made it impossible to follow without subtitles. The content was good though which is why it deserves two points.

I was hoping for a little more from this course. While it effectively combines statistics, finance and python, it does not go into great detail in any of the three. There are spelling mistakes and the audio quality is not great. For me the most useful part was learning the python functions and syntax.

Don't waste your time. The statistics part isn't explained well enough. Python code, which this course provides, is slow and uses outdated concepts. The idea of predicting stock prices using linear models is just a gem! If you are new to python, the course can show you how not to write code.

Course is good but (a) some Python bits are not up-to-date and (b) final week exam questions should be updated and brought in sync with either lectures or the lectures need to be updated. If Google has a different answer than the course, well, that's not a great place to be in.

Course uses <pd.DataFrame.from_csv >. This has been discarded by Python. Since I am begineer to Python and on assumtion that such discarded notations will again be used going further in course, I have no choice but leave the course in first week itself.

i am happy with subject and course syllabus but if the data that the lecturer worked on them, was available the course would be much more useful,

without the data, it was just some code that we were watching.

thank you coursera :)

I would change the title. It has little practical content on trade, and explains more statistical methods.I would call it "how to use and graph statistics in python, with some trade samples".

Does not explain technical terms well. I had to search online to understand much of this content. Some of the quiz questions related lab to are not explained in course / lab

The course tried to cover a lot but wasn't really helpful for those who didn't have basic knowledge of either Python or Statistics

Poor Explanation of topics, was in a very hurry to complete than in explaining the concepts in depth. Did not reach expectations.

More coding please. I was looking for more linear modeling examples and implementation. The rest was relatively easy.

Not enough detail. Lectures have good material but a lot of information is presented too quickly. Labs are good

Lectures are not very informative. Things are said directly and not explained well. Sadly I paid $50 for this.

weak, not really good course, just showing some basics of data analisys and concepts.

The teacher need to learn better pronunciation and slower pacing

video lecturing lacks clarity and big picture context

very basic,

not improving python skills

Too difficult to beginner

This course teaches statistics more than Python coding. The codes are not well explained or even not explained by the instructor. The instructor spent more time on statistics concepts than going through the Python coding. I learned very little about Python in this course.

Should be better explained. Could not get past the first few lines in the practical. Looked at the discussion board and people were saying it was because the course is outdated and the code he explained might not even work with the version we were using

1- t​he language spoken is not clear (I had t oread the subtitles more than listening which was a horrible experience)

2​- the labs are bot built in the right way, lots of errors and lots of data changes that effected the application of the commands

Horribly worded questions. Difficult to understand the lecturer. Week 1 was good to practice python. Week 2 was awful.

The code examples and quizzes have not been properly reviewed and there were multiple mistakes in them.

codes are not updated to python 3. Also little opportunity to apply codes in IDLE/Anacdona yourself.

Disorganized, and the quizzes are not testing abilities nor knowledge but just quantitative skills.