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Online Course

Python and Statistics for Financial Analysis

All-Time Top 100

The Hong Kong University of Science and Technology via Coursera

(139)
  • Provider Coursera
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Effort 3-4 hours a week
  • Duration 4 weeks long
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Overview

Course Overview: https://youtu.be/JgFV5qzAYno

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.

Syllabus

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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Reviews for Coursera's Python and Statistics for Financial Analysis Based on 139 reviews

  • 5 stars 52%
  • 4 stars 29%
  • 3 stars 14%
  • 2 stars 4%
  • 1 star 1%

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  • 1
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 a baby intro course!!! it assumes you are either strong...
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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 variance and standard deviation between the test and...
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Ariel G
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 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.
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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, with some python codes to help us understand and...
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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!
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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
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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.

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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.
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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.
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Kurp K
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. This course definitely expect you to know a bit about statistics and also to know Python programming, on basic level at least. On the other hand I think the course does not cover the topic deep enough, we've got only some simple linear regression model based on some not-so-creative feature engineering. It does not cover such aspects as HFT vs swing trading strategies, using slipage and transaction costs to evaluate strategy, managing invested capital and many more. I've expected a bit more, to be honest. The course is well done as ready-to-use implementation of very simple concept - but there's nothing more to expect here.
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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 I tried to re-create the model in my JN the codes didn't work. Even the codes already given in the course notebook contain so much errors when I try to run it. Please update the codes so they can run properly.

Also all the technical formulas were not explained clearly. The professor just showed the formula and explained a little bit but did not go in too much depth leaving me confused of the use of the formulas.

I would not say doing the course is a total waste of time. It has some values but I think it will be much much better if the codes are updated and the formulas are explained better.
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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 are AFTER the video classes and NOT BEFORE.

I wasted some time trying to get Jupyter working in my computer and saw a lot of students in the forums having similar/related issues. Not necessary since coursera has a web app with Jupyter, not even the "Jupyter tutorial" provided is needed. Just a waste of time...

This waste of time could be avoided and I'm sure would increase engagement ratio in the course, since people have to deal with it in the very begining.

Good course overall.
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Anonymous
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 viability of investment in the US, EU or Asian markets respectively, taking advantage of the opening and closing time of trade of these markets, thereby making reasonable prediction haven analysed the maximum draw down and sharpe ratio strategy.

My instructor, Prof. Xuhu WAN, was not only excellent but very detailed, leaving no miss leading thought untouched.

Courses on coursera are strongly recommended for everyone dedicated to learn.

EDET, Nneoyi Abity.
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Anonymous
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.
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Anonymous
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 set-up for practice.
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Sabarinathan C
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
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Anonymous
Anonymous completed this course.
The course provides an overview of how to build a quantitative trading model. However, the instructor does not go into details while either introducing python functions to someone unfamiliar with the language or talking about statistical concepts. I could follow the code based on my background in other programming languages.

I will be following up this course with other courses that go in depth on both the programming and statistics front.

The Jupyter notebooks are quite helpful and I will be using them for future reference.

3.5 would probably be a more honest rating of the course but I don't think the course could have taught the learner more given its length.
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Anonymous
Anonymous completed this course.
Good introduction to Python coding applied to financial models. It only remains an introduction though, many concepts are briefly touched on and necessitate prior knowledge or to be completed with other courses on statistics and/or financial models. Likewise with Python coding, I liked the first hands-on module, but thereafter all codes are given to you and definitely need you to practice them by yourself unless you want to forget everything a few hours later. I would still recommend this course because it is clear and concise and a good introduction, but it's definitely worth taking harder classes about coding and/or financial models later on.
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Anonymous
Anonymous completed this course.
The course has an interesting structure, but I would prefer a course more focused on python than statistical concepts. It's attempted to do a little of both and neither is well explained or thoroughly examined.

Material is very outdated. Several functions are needed to be updated, like pandas.tools.plotting, or statsmodel.formula.api that are now pandas.plotting and statsmodel.api and syntax to run the OLS is much simples now with only smf.OLS(Y, X).

Quiz is full of mistakes, typos and questions with no precise answer. I suggest a review.

Course is well appreciated in general. Wish I helped improving it because it's an overall good course!
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Amran M
Amran is taking this course right now.
The python and statistics for financial analyst course has taught me a lot of things some I did not know before. The course is quite informative and beneficial as it presents you with many opportunities for you to test out your knowledge and practise an ample amount of times which inevitably enhances the amount of information you are retaining. I believe this method of teaching is quite powerful as the practical section of the course enables course takers to work independently, familiarise themselves with the work and builds course takers confidence up in the course.
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