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# Python and Statistics for Financial Analysis

## Overview

##### Class Central Tips
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!

Xuhu Wan

## Reviews

4.3 rating, based on 280 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),...
• 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
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.
• Balaji Bahirwal
The course is really good with practical applications on financial data using python. The taught applications of testing of hypothesis, linear regression and developing model and evaluating model in practical sense using real trade data financial anlysis of companies.
• 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.

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...
• 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 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
This course was a good refresher for statistical concepts I was rusty on, and gave decent exposure to Python for someone who is familiar with other programming languages. The labs were useful to see how to do financial analysis with Python, but weren't very interactive - a lot of the time, I just ran the code already there without doing any programming on my own. I'd recommend this course if you want exposure to basic Python (but without the "programming" stuff you probably already know - data structures, algorithms, etc.) and a refresher on statistics. It won't make you a day trader or Python pro, but gives decent exposure to important concepts you can study more in-depth on your own.
• 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 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
This is a great introduction to the possibilities of using python in financial analysis. You should probably know some python or be familiar with coding, and you should probably know some statistics and be familiar with linear regression. It's a good "practice" course, and you get a ton of good coding examples that you can use and modify for your own purposes. I was able to start playing around with data from Yahoo! finance right away. You won't be a Wall Street quant after this course, but you'll definitely get a taste of where this could lead. Thank you, professor Xuhu Wan!
• Anonymous
This course is a good introductory course into the application of data analytics with python. In reality, without a very good understanding in statistics and python, you will struggle to follow along. I often found myself re-reading simple code as it was not explained well, and I have some experience in Python.

For someone trying to learn data analytics, I would say this is a good course to audit, and do some exercises. I completed this in a very short period of time so maybe if you split it up it may be easier to do.

Happy Learning :)
• Anonymous

Anonymous completed this course.

Best course to quickly know financial modeling application, required underlying statistics and Python. Prof Wan has assembled the perfect trifecta in this introductory course.

Some have suggested in their comments that it doesn't cover certain things in detail. However, that is not the intent of this course. It is the perfectly assembled course to introduce the required concepts and provide just enough clarification to understand the trifecta in data science. Details can be found in other textbooks or Google search, etc.
• José Rourich
The course is about topics that are too complex to get in so little time and so little detail and depth. The complexity is such that not only is it not enough to watch the videos and do the practice, it is directly necessary to do a parallel course in order to fully understand the topics. I liked it during the first week, but afterwards the complexity and little detail provided in each topic made me lose interest and got confused. In my opinion it is a course that requires much more time and more examples and practice.
• Anonymous
It is a good introductory course that explains how to use statistical models in financial industry and trading, but if you don't have strong knowledge in statistics you will need to study other materials to understand clearly the formulas used by instructor to be able to connect both of them.

If you have no experience in statistics I advise you to start this course but don't skip any concept until you make sure you understand it fully from external sources, each module builds on the previous module.
• Palak Agarwal
This course is quiet good with proper explanation. The best part is one can practice python practically in Jupyter Notebook available within the course. Even, after every course you have to attend the Quizzes which is the best part I liked. As once you take the quiz after completing the course your level of understanding is tested. And you come to know your mistakes.

I loved the course very much. Thankyou so much for uploading such type of course and making easier for we students.
• Anonymous
I really appreciate the well-designed courses and the hardship the teacher devoted. It does me a great favor. Not only the course form is convenient, but also the flexible schedule can help me to balance all my stuff. Besides, this course provided me with practical skills that I needed the most. Actually, I had learned something about the financial theories. Seldom oppotunity do I have to put theory into practice. I hope this can be my brick to open another career window.
• Anonymous
Good course for a beginner to intermediate. Although some prerequisite is a must, I believe some part of the python code could have been explained in a bit more detailed manner. Overall good start for analyzing data in the finance industry. I liked the predictive modeling part. The statistics part ( Wek 2 and Week 3) could have been more elaborate and clear. Overall I'd recommend this course to anyone interested in trading and analyzing using computer language.

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