Financial Data Analysis and Practice

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Prof. Luke Stein

View the Project on GitHub lukestein-classes/fdap

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.

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.

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

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

Books

Other References

Statistics and Inference

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

Financial Data

Microsoft Excel

Data Analytics Using Microsoft Excel With Accounting and Finance Datasets (v.2.0), Joseph M. Manzo

Financial Applications

Relevant resources TBA

Other Tools

Markdown

Acknowledgements

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