CFDS-Notebooks

CFDS-Notebooks copied to clipboard

AZEK & DVFA Chartered Financial Data Scientist (CFDS) ®

A series of interactive lab notebooks we prepared for the DFVA and AZEK Chartered Financial Data Scientist (CFDS) ® Certification. The content of the series is based on Python, IPython Notebook, and PyTorch.

This is currently work in progress so please expect minor errors and some rough edges ;)

Seminar "Warm-Up" - Lab Notebooks

Lab 00: "Testing the CFDS Lab Environment"

(Launch Notebook: , )

Lab 01: "Introduction to the CFDS Lab Environment"

(Launch Notebook: , )

Lab 02: "Fundamentals of Python Programming"

(Launch Notebook: , )

First Seminar Day - Lab Notebooks

Lab 03: "Financial Data Science - Moving Average Trading Strategies"

(Launch Notebook: , )

Lab 04: "Financial Data Science - Mean Reversion Trading Strategies"

(Launch Notebook: , )

Lab 05: "Supervised Machine Learning - Naive Bayes"

(Launch Notebook: , )

Lab 06: "Supervised Machine Learning - k Nearest-Neighbors"

(Launch Notebook: , )

Lab 07: "Supervised Machine Learning - Support Vector Machines"

(Launch Notebook: , )

Second Seminar Day - Lab Notebooks

Lab 09: "Unsupervised Machine Learning - k-Means Clustering"

(Launch Notebook: , )

Lab 10: "Unsupervised Machine Learning - Expectation Maximization Clustering"

(Launch Notebook: , )

Lab 11: "Supervised Deep Learning - Artificial Neural Networks"

(Launch Notebook: , CPU: , GPU: )

Lab 12: "Supervised Deep Learning - Convolutional Neural Networks (CNNs)"

(Launch Notebook: , )

Online Webinars - Lab Notebooks

Lab 13: "Unsupervised Deep Learning - Autoencoder Neural Networks (AENs)"

(Launch Notebook: , )

Lab 14: "Supervised Deep Learning - Recurrent Neural Networks (RNNs) (One-To-One)"

(Launch Notebook: , )

Lab 15: "Supervised Deep Learning - Recurrent Neural Networks (RNNs) (Many-To-One)"

(Launch Notebook: , )

Getting Started

Install dependencies via pip install -r requirements.txt.

Questions?

Please feel free to get in touch by opening an issue report, submitting a pull request, or sending us an email.