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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

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

Reviews

4.4 rating, based on 392 Class Central reviews

4.4 rating at Coursera based on 3294 ratings

Start your review of Python and Statistics for Financial Analysis

  • 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,...
  • Be specific and provide examples when commenting on the course or the instructor. Focus on observable behaviors of the instructor or particular aspects of the course. Describe the situation you are commenting about for the feedback. For example, "we couldn't...
  • Anonymous
    This course is very much useful. I enjoyed the course and learned a lot from it. The content is well organised and focused on practical situations. The course is well arranged in terms of what contents they show in each module and it is quite practical,...
  • 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
    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 play with the topics. But there were some things that kept me wondering that I should be spending...
  • Anonymous
    This course is very much useful. I enjoyed the course and learned a lot from it. The content is well organised and focused on practical situations. The course is well arranged in terms of what contents they show in each module and it is quite practical,...
  • Anonymous
    Very easy to understand! 1. Ease of use for beginners First and foremost, Python is one of the easiest programming languages to learn. You don’t need to have any programming experience to start performing data analysis in Python. Unlike R and MATLAB,...
  • Anonymous
    In this topic, we have explored linear regression model mainly from perspectives of statistics. We then trained the application of a linear regression model in SPY trading. We conclude that the derived model is not overfitted and the performance of signal-based...
  • Anonymous
    In this topic, we have explored linear regression model mainly from perspectives of statistics. We then trained the application of a linear regression model in SPY trading. We conclude that the derived model is not overfitted and the performance of signal-based...
  • Profile image for Danyang Zhao
    Danyang Zhao
    课程老师很用心,讲得通俗易懂,初学者也可以很快学会!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!非常好,非常好,非常好!
  • Profile image for Chengkai Zhang
    Chengkai Zhang
    个人感觉课程内容十分充实,但是带来的问题是没有铺垫就快速的进入讲解,应该多提示一下前置课程,也可以多说明一下怎么运用包中的函数,没有讲解直接就用了
    I personally feel that the course content is very full, but the problem is that there is no padding to quickly enter the explanation, should be more hints about the pre-course, can also explain more about how to use the functions in the package, no explanation directly to use.
  • Anonymous
    Very useful ,It is a good introductory course that explains how to use statistical models in financial industry and trading, but nned to learn this courese you need 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.

    A littile suggestion , if the instructore can give one more week to enhanced any concepion about statistics that will be better, if there is a cheating sheet like some code symbol summary that will be perfect . Don't skip any module class as each module builds on the previous module. The logical must be stack at start.
  • Anonymous
    This course moves quickly through some basic concepts statistical methods financial data analysis with python. The course moves quickly which keeps it interesting but depending on your prior knowledge in basic statistical methods and their application in python the course content will range from easy to hard. The links to workbooks are not up to date which can be frustrating. The course nevertheless provides a good basis for self-learning and point to areas or concepts that may require further study.
  • Anonymous
    The course is outdated, when there are links related to quiz questions, they do not work in the quizzes so it is a guessing game and you must pass all other questions which are not related to links in order to have at least 80%.

    There are spelling mistakes in the videos and the transcripts.

    The code does not work when you want to code in your own time as for example the file path used is personal and so you cannot work with the data on your own unless you can retrieve it from somewhere.
  • 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....
  • Profile image for Debjit Roy
    Debjit Roy
    I have learnt a lot of things from this course and new invention from coding world. This course made me feel very comfortable about coding and financial analysis i learnt python here very well all the courses are very good and well described. Everyone can learn from this platform its a nice way for the upcoming generation its very useful for us also to learn and gain some knowledge and certificate which will take a huge margin in ur career
  • Anonymous
    I think this course is very good. I have learned a lot, not only python, but also model building. From the beginning of sampling statistics, and then learned confidence interval, and then linear regression analysis, multiple linear regression analysis, to the final model building. I think the teacher is doing a guide for me, let me think step by step, explore, research, the teacher's course is very good! Very good teacher!
  • 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
    Interesting and very good
    This course has helped me a lot in my journey to become a good data scientist.
    At university I learned statistics and statistical inference, I already knew how to calculate. But this course helped me to know how to use them on Python and how to use them for information. Getting information is a big step towards my goal of becoming a great data scientist.
  • 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...

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