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The Hong Kong University of Science and Technology

Python and Statistics for Financial Analysis

The Hong Kong University of Science and Technology via Coursera


Course Overview:

Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

By the end of the course, you can achieve the following using python:

- Import, pre-process, save and visualize financial data into pandas Dataframe

- Manipulate the existing financial data by generating new variables using multiple columns

- Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts

- Build a trading model using multiple linear regression model

- Evaluate the performance of the trading model using different investment indicators

Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.


  • Visualizing and Munging Stock Data
    • Why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? What makes Python one of the most popular tools for financial analysis? You are going to learn basic python to import, manipulate and visualize stock data in this module. As Python is highly readable and simple enough, you can build one of the most popular trading models - Trend following strategy by the end of this module!
  • Random variables and distribution
    • In the previous module, we built a simple trading strategy base on Moving Average 10 and 50, which are "random variables" in statistics. In this module, we are going to explore basic concepts of random variables. By understanding the frequency and distribution of random variables, we extend further to the discussion of probability. In the later part of the module, we apply the probability concept in measuring the risk of investing a stock by looking at the distribution of log daily return using python. Learners are expected to have basic knowledge of probability before taking this module.
  • Sampling and Inference
    • In financial analysis, we always infer the real mean return of stocks, or equity funds, based on the historical data of a couple years. This situation is in line with a core part of statistics - Statistical Inference - which we also base on sample data to infer the population of a target variable.In this module, you are going to understand the basic concept of statistical inference such as population, samples and random sampling. In the second part of the module, we shall estimate the range of mean return of a stock using a concept called confidence interval, after we understand the distribution of sample mean.We will also testify the claim of investment return using another statistical concept - hypothesis testing.
  • Linear Regression Models for Financial Analysis
    • In this module, we will explore the most often used prediction method - linear regression. From learning the association of random variables to simple and multiple linear regression model, we finally come to the most interesting part of this course: we will build a model using multiple indices from the global markets and predict the price change of an ETF of S&P500. In addition to building a stock trading model, it is also great fun to test the performance of your own models, which I will also show you how to evaluate them!

Taught by

Xuhu Wan

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4.3 rating, based on 248 reviews

Start your review of Python and Statistics for Financial Analysis

  • Ronny De Winter completed this course, spending 2 hours a week on it and found the course difficulty to be medium.

    This is a compact course on statistical analysis using python on downloaded historical stock prices. You learn how to calculate moving averages (MA), buy signals based on MA, strategy profits, stock return frequency distributions, Value at Risk (VaR),...
  • Berbelek completed this course, spending 2 hours a week on it and found the course difficulty to be easy.

    I have mixed feelings about the course. It shows very practical aspects of building trading stategy in Python, which is still quite unique topic here. It also offers a lot of practice and ready to use and modify solutions delivered as Jupyter notebooks....
  • Anonymous

    Anonymous completed this course.

    Executive summary: Recommend, but i personally did not like it and could spend my time better on harder and more useful courses. Complete review: The course is well arranged in terms of what contents they show in each module and it is quite practical,...
  • Anonymous

    Anonymous is taking this course right now.

    this course is very practical! it explains how statistic concepts can be applied into financial-related examples using python. some argue the course do not cover enough of python nor financial, nor statistics concepts. hey man !!! this course is not...
  • Anonymous

    Anonymous completed this course.

    I feel like I got the overall gist of modelling. However, there were many details in the lectures that were left wanting. For example, the Sharpe Ratio; I still don't understand why this a useful measure in our model - why is this better than using...
  • Anonymous

    Anonymous completed this course.

    Overall, I would recommend this for a beginner of python. The classes, until the end in my opinion, were easy to follow. Some of the quizzes were a little strange and unorganized where a few of the answers honestly didnt quite make sense, but for the...
  • Sabarinathan Alias Cheryn

    Sabarinathan Alias Cheryn completed this course, spending 4 hours a week on it and found the course difficulty to be medium.

    This gives a application of all the three famous sectors viz, finance, python and statistics. Actually speaking i am searching for these kind of courses and did not get one. Atlast got this one for my solace. This suited my need. This course cannot be easily designed as other courses . This really needs one time . Thanks to the person who devised the course and also to the instructor Mr. Xuhu Wan for his meticulous time to provide the information in a precise way.

    Infact the while explaining errors actually in a very short time he explained the unexplained, explained and total error in a concise and apt way. really its a wonderful course.

    Sabarinathan alias Cheryn
  • Profile image for Ariel García
    Ariel García

    Ariel García completed this course, spending 6 hours a week on it and found the course difficulty to be medium.

    Its a complete course which gives the fundamentals for programming financial applications into Python. I would suggest to improve in trying to use the code presented in the Jupyter applications, not only for the variable names but for the code consistency. Also, I would suggest that the data files been published for the students which work off-line and try to keep peace; I use Sublime + Python interpreter on my laptop and no Jupyter, for example. I recommend the course for people which takes a very first idea of Phyton, anyways it's no difficult to keep the rhythm.
  • Anonymous
    This course is really helpful since I was busy finding a new job in the same field recently. And this course provided me with practical skills that I needed the most. Through this course, I learned how to build a trade market model and linear regression model for financial analysis, more importantly, but the methods to evaluate the strategy. Therefore, I recommend you to take this course, and you will definitely learn a lot.
  • Anonymous

    Anonymous completed this course.

    This course gives great insights into the use of statistics and its implementation in python for financial analysis. The curriculum is well structured which gradually increases in difficulty every week. It gives a healthy introduction to various techniques which can be applied to Finance & will definitely motivate you enough to delve deeper into concepts to explore more. A perfect blend of Statistics, Finance & Computing!
  • Anonymous
    The course was really helpful and the practical approach alongwith videos and readings was one of the best thing about this course. It gave me insight about python's application in statistical areas and predicting the various variables of the market. Very helpful for a student of MBA. And not to forget it gave me a better approach to understand the correlation, random variables, predictables etc.
  • Anonymous

    Anonymous completed this course.

    The course was quite instructive. The examples bring out the concepts taught. The course is of grate benefit, especially if one is in the investment banking discipline. Its a great introduction to use of ML in financial analysis.

    What may be required is to give the students more chance to code - from basic principles to application. This way, out of practice the concepts sink in.
  • Anonymous

    Anonymous completed this course.

    I would expect the course to provide more in-depth and more practical financial models. For me it's more like a revision on statistics and very simple Python coding.

  • Anonymous

    Anonymous is taking this course right now.

    Good start for a beginner, but I would prefer a bit more intermediate stuff. Apart, from that the course is well organised & Systematic making it an interesting one.
  • Anonymous

    Anonymous completed this course.

    It is a superb course even though it requires Statistics.
    I was waiting for this kind of course to predict my ROI.
    I hereby recommend all the people involved in investments and financial analysis to opt this course
  • Anonymous

    Anonymous completed this course.

    It is very practical and well organized course. Thank you for good teaching. Really enjoyed it. If we can learn more of nonlinear regressions for finance parameters in python would be even better.
  • Anonymous

    Anonymous completed this course.

    I expected to learn to build stock market modeling in Python using statistics but did not really learn anything. The videos are short but you may have to take hours to digest the videos. Also, many of the codes shown in the videos are outdated so when...
  • Profile image for Akhil Rana
    Akhil Rana
    In-depth knowledge of financial analysis with statistics provided within the course. Pros -depth in course -conceptual clarity (although you will have to do a lot of googling) -clean codes -good examples Drawbacks (rather feedbacks to improve this) -some...
  • Anonymous

    Anonymous completed this course.

    Pretty good course if you already have some knowledge of statistics and python. Could reach a wider audience if the instructor had more tutorials and explained some of the steps in more depth. Should also just alert the students that the Jupyter Notebooks...
  • Anonymous
    The course topic and modules covered were really well chosen, interesting and kept my attention. The videos had good video and audio quality, the visuals were great, and the professor had fine presentation skills. I had issue with the pace : simple...

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