I was a very interesting course with plenty useful examples! The lecturer is very personable. But unfortunately his dialect is so extreme that I can hardly understand him most of the time. In addition, without in-depth knowledge of statistics, the theory was not sufficiently explained. I had to do a lot of external research, which was ok at university, where this definitely can and should be expected of me, at the time, but in a Coursera course there actually should be provided all the information one needs to understand a Modul.

Xuhu made a commendable effort in the early part of the class in teaching the basics of this course. Over the last 2 weeks of content however, significant external reading is required for students to do independently from the course content (not a bad thing) but know that extra effort is required to pass the later stages. For the most part, you should have the basics of python prior to taking this course or you will struggle midway. Nonetheless, it was definitely a commendable effort from this course.

The topics are very interesting, but the explanation is not very clear and more insights and explanations could have been provided on the explained techniques. Slides are quite poor of content, and it is often more useful to read the transcript than to follow the slides, also given that the English of the trainer is hard to follow. Sometimes, even the transcript has sentences that are not correct. The labs are well structured, but they could be enriched with comments on the results.

Either content should be split into more modules or more lectures to be added in existing modules such that details are covered with a slightly more elaboration. Notebooks must include exercises rather than passive demos of techniques. Students may be provided with reading materials/links as followed by Coursera in other courses.

This course gave me an idea of financial analysis tools available and its usage in Python. Thank you for the free course

Overall good, the professor is delicated and responds to the forum actively. But the course could be better designed. Even though I have learned the knowledge of statistics, econometric, and python and got a 100% certificate, the course is still difficult for me to digest. I have to pause the video and think 5-8 times per video. The pace is so fast that some usages of the python or applicaions of finance equations lack sufficient illustration.

The course is good to give an insight into python and statistics. At many places I found the course to be rushed through skipping basics. But if you can be curious you can find the explanations on google and continue with the course. Over all a 3 as I found the examples to be insufficient and the entire course was only taught through coursera Jupyter so transitioning to Python on your system will require learning curve.

1: pronunciation of the professor shold be improved, god please. It bothered me a lot that i can learnt efficiently. 2: some links are wrong when i took the test, so how can i answer the question that i can not even look at the related contents of the questions? 3: some parts are too rough that has not goood experience especially the gap between 2nd week infos and the 3rd week infos.

I wish the concepts in this course were gone into more in depth. They aren't necessarily difficult but they can get complex and when the instructor spends an accumulation of 30 or less per module it is hard to fully understand. More practice is needed as well. All the code was done for you except for just a few lines. People who learn by application will not gain much from this course.

Though, the course has got potential to be further refined with the inclusion of some of the foundational concepts of finance.

I would rate it 3 out of 5, because of slight difficulty in understanding lectures due to speech delivery, lack of challenge in the form of already filled labs, however, there are some good foundational concepts as well to get yourself started in stock trading.

Pros: Learn some python code and review statistical knowledge (SLR, MLR)

Cons: The python code is outdated and may not usable. So time consuming to update the code.

For Example: pd.DataFrame.from_csv(..\...\AAA.csv) which is commonly used in the course

It is not usable as DataFrame.from_csv has been replaced by pd.read_csv(r'...)

Also for loc has significant changes

Overall the course is good for teaching the basics of financial data analysis using python, however when the more technical areas are taught, the Jupyter labs are pretty bad at explaining stuff and helping you understand. You also can't reopen the labs and the code won't run since it accuses it all from a data folder. There are also a lot of spelling mistakes.

The statistics element is basic and there is very little practice coding with Python. Instead, it is more of a demonstration how Python can be used to implement some regressions and basic trading strategies. Informative in showing this, however overall a bit disappointing as there was less Python learning and practice as anticipated/advertised.

The course is trying to cover multiple area of skills (python, statistics, and financal analysis) in a short period of time. It might be difficult for people who do not know much computer programming experience to follow the Python part. Vice versa, it might be difficult for people to pick up much financial concepts as an amateur.

It's very hard to follow what the instructor is meaning. I dont agree with some mathematical definitions he states as learned by other methods (estimators as aleatory variables to determine the best estimator). It is the first time that heard of "degrees of freedom". I wish placing more focus on Python than in statitics.

Interesting and easy to understand for people with basic background or have basic knowledge about finance or statistic. However, I wish some of the videos may have explained more about how to use the data to solve real life issues. Even though some of the practices may explore it, it appears not deep enough for me

I'm not sure for whom this course is dedicated. To undestand what's going on here you have to know statistics' topics and some Python libraries. This course is showing (not explaining, just showing) to you how to use your statictic knowledge in Python libraries whiches you have to be familiar with.

The course is helpful but they trick you, before taking it they say it’s free, when you’re done they now want you to purchase it, like why make a fool of people because you should indicate from the word go that you pay, now I’m stuck I can’t get my certificate because I don’t have the money to pay

The pace is too fast and instructor speaks rapidly and can hardly catch what he is talking about without the subtitles, not to mention the new terms and concepts, esp for those non-Stats learners.

Too many typos in the course notes and lab exercise.

Many sleepless hours to pass this course. :)

This is overall a good course; it is well explained and very quick. However, it would benefit with assigning more exercises or ensuring that the labs are more interactive. In this topic, practice makes perfect and is very necessary. Also, the quizzes require clicking on links that do not work.

Good course but I think that economic concepts should be explained in more depth so that we can work better on Jupyter (which is a great way to teach / learn). I also think that more written material with illustrative examples could be included, not just lines of code that generate results.

Course content is valuable from Statistics applied in Python code. Unfortunately, it didn't give enough examples or use cases for Financial Analysis. I'd like to see more stocks market predictions, studies and models using the Statistics concepts explained on this course.

Mixed opinion for this course: is a good introduction to Python, statistics and econometrics, and you actually learn something. But sometimes it asumes you know things that weren't explained before, the exams contain some errors, and the explanations are not always good.

It has wide explanation of statistics basics on the other hand the model development with python applied in finance has too few examples. I would also suggest data acquisition with Yahoo Finance or Google to be explained as it is widely used in financial analysis.

Tough for a person not familiar with statistics and completely new to python. Would have been good to provide more basics to python as well. However, i would believe it'll be very beneficial for one who has already strong foundation in statistics to follow.