Best of AllTime Online Course
Python and Statistics for Financial Analysis
The Hong Kong University of Science and Technology via Coursera

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Overview
Class Central Tips
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, preprocess, 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.
Syllabus
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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Reviews
4.3 rating, based on 173 reviews

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 is taking this course right now.
this course is very practical! it explains how statistic concepts can be applied into financialrelated 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 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 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... 
Ariel 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 offline 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 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! 
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 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 completed this course.
I would expect the course to provide more indepth and more practical financial models. For me it's more like a revision on statistics and very simple Python coding.

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 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 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. 
Ronny 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),... 
Kurp 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 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... 
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 completed this course.
This course: Python and for financial analysis is a great deal of broadening. Centered on how to translate statistical models into computer base language to solve daily financial realities in juxtapose to stock tradings. To a greater depth i can now decide... 
Anonymous completed this course.
The course is okay. There is a lot of information and sometimes the lecture material feels very dry lacking detailed explanations of certain concepts, particularly in week 4. Also, there were incorrect answers in some of the weekly quizzes.
For example they give you a link where it displays the table of information you need to answer the question but lets say the value you see from that table is 0.782. The actual options for answers on the quiz doesn't have 0.782. Instead they might have an option that says 0.740. If you pick the closest option to 0.782 then you should get the mark. I don't like this as it doesn't test the students knowledge if the correct option isn't available. 
Anonymous completed this course.
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 setup for practice. 
Sabarinathan 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.
Thanks
Sabarinathan alias Cheryn