Very clear explaining of the significant aspects when structuring a financial analysis, applicable in many forms of data if you don't want to make predictions only for the stock market.

Tricky accent at time but great content and absolutely super Jupyter notebooks. The presentation looks cheap but this is one of the best finance/python starter courses on coursera.

The videos in this course are exceptional and very interesting. The Jupyter notebooks provide a good template for applying the methods and techniques.

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Code does not work as well as it is outdated. No explanation on how to use the Jupyter notebook either. Leaves a bad taste in my mouth just thinking i had to waste my time with this when instead i learnt much more from youtube trying to fix this broken, outdated code of theirs

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.

Confusing and the trainer is no clear at all

Outdated and not really informative

The labs are not challenging

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not a comprehensive

no entendi nada

Very bad course

not worth it

very bad

too bad

I would be happy to provide a review of the "Python and Statistics for Financial Analysis" course on Coursera.

Overall, I believe this is an excellent course for individuals who want to learn about using Python for financial analysis. The course is presented by the instructor who is an experienced data scientist and financial analyst, and he explains the concepts in a clear and easy-to-understand manner.

The course covers a range of topics, including statistical analysis, time series analysis, and asset valuation. The course also teaches students how to use various Python libraries for financial analysis, such as Pandas, NumPy, and Matplotlib.

One of the things I appreciated most about this course is its practical approach. Throughout the course, the instructor provides real-world examples and exercises, which allows students to apply what they have learned in a meaningful way. The course also includes a capstone project, which requires students to use their new skills to analyze real financial data.

The course is well-structured, and the lectures are organized in a logical and coherent manner. The course materials, including the lectures, readings, and exercises, are all well-prepared and presented clearly.

The Python and Statistics for Financial Analysis course is a great introduction to the use of Python for financial analysis. The course covers a wide range of topics, from basic Python syntax to more advanced statistical concepts. The course is well-structured and easy to follow, and the instructor, Prof. Xuhu Wan, is clear and engaging.

One of the strengths of the course is the hands-on projects. In each project, students are given a real-world financial data set and asked to use Python to analyze the data. This gives students the opportunity to apply the concepts they have learned in the course and to develop their problem-solving skills.

Another strength of the course is the use of Jupyter Notebooks. Jupyter Notebooks are a great way to combine text, code, and visualizations, and they make it easy to share your work with others. The course uses Jupyter Notebooks extensively, and this helps students to learn how to use this powerful tool.

Overall, I would highly recommend the Python and Statistics for Financial Analysis course to anyone who is interested in learning how to use Python for financial analysis. The course is well-designed, engaging, and informative.

The course is well done. I appreciate that some minor things about Phyton are ommitted in order to explain the important things more rapidly. If you have never coded before (like me) and you want to follow this course seriosuly, understanding every line of code, then you have to do additional research online, to read for example the exact synthax of a certain method or function that is being used.

I truly wish there was a follow up to this course, to go more in depth and to see more examples. Sometimes the accent of the teacher and the wrong (english) subtitles can make it harder to understand certain part, one funny example is "shot" a stock instead of shorting it. Only in very few instances it actually made it tougher to understand certain parts as for example the verb-noun agreement was not easy to understand, english is not my first language so take what I said with a grain of salt as well! Thanks a lot for this course! Again, I really hope you decide to do a Part 2 ! I would enroll immediately

This course is challenging if you have not yet gained a sufficient understanding of both statistics (i.e., normal distribution, linear regression, etc.) and Python (e.g., list comprehension, how to call class methods, etc.). Other than that it is a great course to show how you can blend both and build a trading model that can be applied to the market. The course will guide you step by step from explaining the different libraries used (pandas, statsmodels, numbpy, matplotlib) to both analyze and visualize the data, how to build your trading model, how to diagnose and test your model, and finally how to evaluate its profitability. A great course for everyone who would like to see in more detail how you actually build a trading model with Python and statistics. Very recommendable if you are willing to put in the work.

The Coursera overview of this course is exactly what it turns out to be. Prof. Wan does a nice job of balancing this 3-legged stool: 1) a bit of Python (mostly about the pandas and numpy libraries), 2) basic Financial modeling for informed trading, and 3) the long leg of the stool - statistics!

If you haven't had like stats 101 and 102 you will be running hard to digest this intensely powerful information. For me this was first a review but then took me into places I hadn't been yet. I'm still going over it. The statistical principles shown here can be applied to many different real world situations. It could be categorized as 'supervised learning'.

The Python coding (library implementations of the math formula/equations) is made seamless with the Jupyter notebook examples. Drink the Kool Aid!

I audited the course for free and I liked it very much. I feel I learned a lot. I wanted to purchase the shareable cerfitificate, after I completed all my work and passed all the tests (I didn't purchase it when a message offering the cerfificate kept popping up during the course). However, the instructions to purchase the cerficate after having taken the course leave a lot to be desired. It should be easier. There should be a button, right on the course's page that I could just click on in order to pay and then be done. The instructions sent me all over the place, from page to page. I'm still not able to find a way to purchase my cerficate . Frustrating! I guess I can do without it. Other than that, Great Course!

The course is not an introductory level. It requires a solid background in probability and statistics: random numbers, frequency, sampling, probability distributions, sample statistics, hypothesis testing and prediction modeling. In the second part, it teaches several python methods of doing statistics and gives more focus on data literacy than mathematics. The final part is some financial applications in time-series analysis of daily return of a stock, statistics of stock return and two important measures: Sharpe ratio and maximum drawdown. The course is a good comprehensive module in the statistical use of something and not teaching you statistics.

P.S. There are some missing links in the questions. Python needs some update.

I am an software engineer with little statistic knowledge before. This course is a little bit challenging for me because I need to read a lot of extra statistics knowledge from Google otherwise it is difficult to understand the concepts, especially for week 3 and 4. I have repeatly watched each video for 3 to 4 times and finally found that Prof Xu has done his best to explain some basic concept on Normal distribution, CLT etc. You just need to spend time to digest these concept which has applied to his sample code. Overall this is a very useful course. I will try to use the knowledge to code some new investment strategy for myself.

I really like how the instructor putting together the real world examples and how we can solve the problems and also precautions with Python. It is better for any students to brush up the statistics & probability concept / knowledge before taking the course as you would enjoy and understand it more (or you can still read up further about those concepts or definitions by yourself when they got mentioned in the videos). Just one suggestion that it would be better to have the slide deck or lecture note for student download. Thanks a mill for making this good course available to us.