FDAP Student Resources

This repository has material that supplements what is posted on the Babson FIN 6200 Canvas page. It exists mainly to provide publicly accessible URLs for shared data files (in /data/) and template notebook files (in /templates/), a course schedule, and links to external resources.

• Syllabus
• Course schedule
• Teaser/welcome video
• Data files
• Template notebooks

Python

Installation

Python notebooks can run in the cloud using Google Colab or Binder, but you will probably want a local installation. I strongly recommend using the Anaconda Python distribution.

Anaconda includes (almost) everything you need to get going, but in line with these recommendations, I prefer to work in Visual Studio Code with some add-in extensions.

• My video demo: Installing Anaconda and Visual Studio Code for Python notebooks
• Arthur Turrell’s Python with Anaconda and VS Code installation instructions
• Microsoft Jupyter Notebooks in VS Code (and video)
• Microsoft’s Data Wrangler for VS Code

Packages

These are the critical packages we will rely on; if you need a package not included with Anaconda, you should first try to install it using conda install and only if that doesn’t work, install using pip

• pandas (data analysis)
• Pandas data reader (access to sources including Yahoo Finance and FRED)
• Seaborn (data visualization) and an overview of Python visualization tools
• NumPy and SciPy (scientific computing)
• WRDS (access to the authoritative source of historical financial data)

Additional packages that may be useful include YData Profiling (automated EDA), Pyjanitor (data cleaning), and dataprep (data cleaning and automated EDA)

Books

• Think Python (2nd ed., and repo), Allen B. Downey
• Whirlwind Tour of Python (and repo), Jake VanderPlas
• Python Data Science Handbook (and repo), Jake VanderPlas
• Python for Data Analysis (3rd ed.), Wes McKinney
• Coding for Economists, Arthur Turrell
• Introduction to Python for Econometrics, Statistics and Data Analysis (5th ed.), Kevin Sheppard

Other References

• Datacamp cheatsheets: Python basics, pandas basics, pandas advanced, Seaborn, NumPy, and importing data
• Learn Python in Y Minutes
• pandas User Guide
• Kaggle Welcome to Data Visualization (uses Python, Pandas, and Seaborn)
• How to read most commonly used file formats in Data Science (using Python)
• Stata to Python equivalents, Daniel M. Sullivan

Statistics and Inference

Introduction to Modern Statistics (1st ed., and repo, data repo), Mine Çetinkaya-Rundel and Johanna Hardin

Financial Data

• Bloomberg Anywhere
• Open Source Asset Pricing
• Wharton Research Data Services (WRDS)
  • Datasets by concept and by product
  • CRSP Methodologies

Microsoft Excel

Data Analytics Using Microsoft Excel With Accounting and Finance Datasets (v2.0 for Excel 2016 or v3.0 for Excel 365), Joseph M. Manzo

Financial Applications

Relevant resources TBA

Other Tools

Markdown

• Commonmark Markdown Tutorial
• Markdown cheat sheet

Acknowledgements

Course designed with significant advice/help/inspiration from Don Bowen, Michael Goldstein, Grant McDermott, Cameron Pfiffer, Seth Pruitt, and Arthur Turrell