Python and Statistics for Financial Analysis Course Syllabus

Overview: This course provides a comprehensive introduction to using Python for financial data analysis, combining programming skills with statistical methods. Learners will gain hands-on experience processing and visualizing stock data, applying statistical concepts to investment risk, and building trading models. The course is structured into four core modules and a final project, with approximately 3 hours per module, totaling around 15 hours of flexible learning. All coding is done in Jupyter Notebook, enabling immediate application without setup.

Module 1: Visualizing and Munging Stock Data

Estimated time: 3 hours

• Importing financial data using pandas DataFrames
• Data cleaning and manipulation for stock datasets
• Visualizing stock price trends and trading volumes
• Building a basic trend-following trading strategy

Module 2: Random Variables and Distribution

Estimated time: 3 hours

• Understanding random variables in financial contexts
• Working with probability distributions in Python
• Measuring investment risk using distribution properties
• Simulating returns with common financial distributions

Module 3: Sampling and Confidence Interval

Estimated time: 3 hours

• Sampling techniques for financial data
• Constructing confidence intervals for stock returns
• Interpreting uncertainty in financial estimates

Module 4: Linear Regression and Building a Trading Model

Estimated time: 3 hours

• Implementing simple and multiple linear regression in Python
• Interpreting regression output for financial data
• Building and evaluating a predictive trading model

Module 5: Applying Python and Statistics in Practice

Estimated time: 3 hours

• Integrating data processing, visualization, and statistical modeling
• Using Jupyter Notebook for end-to-end financial analysis
• Validating model assumptions and results

Module 6: Final Project

Estimated time: 3 hours

• Develop a complete trading strategy using real stock data
• Apply statistical analysis and regression modeling
• Present findings with visualizations and interpretation

Prerequisites

• Familiarity with basic Python programming
• Understanding of fundamental statistical concepts
• Access to a web browser (Jupyter Notebook via Coursera platform)

What You'll Be Able to Do After

• Import and process real-world financial data using Python
• Apply statistical methods to assess investment risk and return
• Build and evaluate linear regression models for trading strategies
• Visualize financial trends and communicate insights effectively
• Use Jupyter Notebook for hands-on financial analysis without local setup