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Python and Statistics for Financial Analysis
  • Level Professional
  • Duration 13 hours
  • Course by The Hong Kong University of Science and Technology
  • Offered by

About

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.

Modules

Orientation

    • 1 Videos
    • 2 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Meet and Greet

1 Videos

  • Course overview

2 Readings

  • Grading Criteria
  • Getting started with Jupyter Notebook

Introduction

    • 2 Videos
Show info about module content

2 Videos

  • 1.0 Module Introduction
  • 1.1 Packages for Data Analysis

Importing Data

    • 1 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Importing data from CSV files into Jupyter Notebook

1 Videos

  • 1.2 Importing data

1 Readings

  • pd.read_csv or pd.DataFrame.from_csv

Basics of DataFrame

    • 1 Videos
    • 1 Lab
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1 Labs

  • Basics of DataFrame

1 Videos

  • 1.3 Basics of Dataframe

Generate new variables in Dataframe

    • 1 Videos
    • 1 Lab
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1 Labs

  • Create features and columns in DataFrame

1 Videos

  • 1.4 Generate new variables in Dataframe

Building a simple trading strategy!

    • 1 Videos
    • 1 Lab
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1 Labs

  • Build a simple trading strategy

1 Videos

  • 1.5 Trading Strategy

Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 1

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • 2.0 Module Introduction

Outcomes and Random Variables

    • 1 Videos
    • 1 Lab
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1 Labs

  • Outcomes and Random Variables

1 Videos

  • 2.1 Outcomes and Random Variables

Frequency and Distribution

    • 1 Videos
    • 1 Lab
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1 Labs

  • Frequency and Distributions

1 Videos

  • 2.2 Frequency and Distributions

Models of Stock Return

    • 1 Videos
    • 1 Lab
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1 Labs

  • Models of stock return

1 Videos

  • 2.3 Models of Distribution

Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 2

Introduction

    • 1 Videos
Show info about module content

1 Videos

  • 3.0 Introduction

Population and Sample

    • 1 Videos
    • 1 Lab
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1 Labs

  • Population and Sample

1 Videos

  • 3.1 Population and Sample

Variation of Sample - Understand Distribution of Sample Mean

    • 1 Videos
    • 1 Lab
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1 Labs

  • Variation of Sample

1 Videos

  • 3.2 Variation of Sample

Confidence Interval - Estimate the average stock return

    • 1 Videos
    • 1 Lab
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1 Labs

  • Confidence Interval

1 Videos

  • 3.3 Confidence Interval

Hypothesis Testing - Validate the claim of average stock return

    • 1 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Hypothesis Testing

1 Videos

  • 3.4 Hypothesis Testing

1 Readings

  • P-value

Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 3

Introduction

    • 1 Videos
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1 Videos

  • 4.0 Introduction

Association of random variables

    • 1 Videos
    • 1 Lab
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1 Labs

  • Association between two random variables

1 Videos

  • 4.1 Association of random variables

Simple Linear Regression

    • 1 Videos
    • 1 Lab
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1 Labs

  • Simple linear regression model

1 Videos

  • 4.2 Simple linear regression model

Diagnostics of Models

    • 1 Videos
    • 1 Lab
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1 Labs

  • Diagnostic of linear regression model

1 Videos

  • 4.3 Diagnostic of linear regression model

Multiple Linear Regression - Generate a signal based trading strategy

    • 1 Videos
    • 1 Lab
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1 Labs

  • Build the trading model by yourself!

1 Videos

  • 4.4 Multiple linear regression model

Evaluate the strategy

    • 1 Videos
    • 1 Lab
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1 Labs

  • Evaluating strategy built from Regression model

1 Videos

  • 4.5 Evaluate the strategy

Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 4

Farewell

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Post-course survey

1 Readings

  • Please rate this course!

Auto Summary

Discover the powerful synergy of Python programming and statistical analysis tailored for financial data exploration in the "Python and Statistics for Financial Analysis" course. Designed within the Business & Management domain and offered by Coursera, this professional-level course is ideal for those aiming to leverage Python's simplicity and high readability in the financial sector. Under the expert guidance of industry professionals, you'll dive deep into Python coding and statistical concepts, focusing on practical applications to financial data, such as stock market analysis. You'll learn to import, pre-process, save, and visualize financial data using pandas DataFrame, manipulate data to generate new variables, and apply key statistical concepts like random variables, frequency, distribution, confidence intervals, and linear regression in financial contexts. One of the key highlights is constructing a trading model utilizing multiple linear regression and evaluating its performance through various investment indicators. The course also includes a pre-configured Jupyter Notebook environment, allowing you to practice Python coding seamlessly without the need for additional software installations. Spanning 780 hours, this comprehensive course is available under the Starter subscription, making it accessible for professionals eager to enhance their financial data analysis skills using Python. Whether you're a financial analyst, data scientist, or anyone keen on merging coding with finance, this course offers the expertise and practical knowledge to advance your career.

Xuhu Wan
Instructor

Xuhu Wan