Applied-Data-Science

This developing repository contains the teaching materials for my CUSP course on applied data science. They are written in either R script or Python 3.X contained in Jupyter notebooks. I've done my best to ensure the syntax still works, especially for the Bayesian lecture.

May 15, 2017 Update: Yahoo Finance has either altered or discontinued its API functionality.

June 12, 2017 Update: I've added older R materials on neural networks and other classifiers, including XGBoost and tsne. Unfortunately, MXNET has not been compiled for R 3.2.2, so that API does not currently work. I will aim to develop a Juptyer notebook in Python to address neural networks and other classifiers.

June 16, 2017 Update: Ran Aroussi has developed a fix that saves accessibility to Yahoo Finance. Thanks, Ran! Great stuff. See the forked directory. I will update the libraries later in preparation for teaching.

July 22, 2017 Update: I've added a notebook on deep learning with an application in finance. It uses the Keras API and Google's TensorFlow as the backend. (TensorFlow is very impressive, as is Keras.)

March 10, 2018 Update: Time series notebook and lab updated.

Feel free to use and adapt.

Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
An Introduction to Neural Networks and Deep Learning.ipynb		An Introduction to Neural Networks and Deep Learning.ipynb
Bayesian soup to nuts.R		Bayesian soup to nuts.R
Bivariate Model.ipynb		Bivariate Model.ipynb
CAPM.ipynb		CAPM.ipynb
Classification Algos.R		Classification Algos.R
Classification and Clustering.ipynb		Classification and Clustering.ipynb
Coin Flip (Beta prior).R		Coin Flip (Beta prior).R
Distributions.ipynb		Distributions.ipynb
Efficiency.ipynb		Efficiency.ipynb
Fun with Math.ipynb		Fun with Math.ipynb
Hypothesis Testing.ipynb		Hypothesis Testing.ipynb
Introduction of Bayesian Inference.pdf		Introduction of Bayesian Inference.pdf
Introduction to Bayesian Inference.ipynb		Introduction to Bayesian Inference.ipynb
Lab Session.ipynb		Lab Session.ipynb
Math Boot Camp.ipynb		Math Boot Camp.ipynb
Optimization.ipynb		Optimization.ipynb
README.md		README.md
Regression Diagnostics, Multivariate Models, and Logit.ipynb		Regression Diagnostics, Multivariate Models, and Logit.ipynb
Session 2 Highlights.ipynb		Session 2 Highlights.ipynb
Stats Boot Camp.ipynb		Stats Boot Camp.ipynb
Time Series Analysis.ipynb		Time Series Analysis.ipynb
Train Test Split.ipynb		Train Test Split.ipynb
sales_si_03.xls		sales_si_03.xls
sales_si_04.xls		sales_si_04.xls
sales_si_05.xls		sales_si_05.xls
sales_si_06.xls		sales_si_06.xls
taximarkov.dta		taximarkov.dta

thsavage/Applied-Data-Science

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