Data Science Topics

Preface

This book is to teach students specialized areas and topics of Data Science. To follow along and execute the code samples, you will need Docker installed. The Docker container is located on Docker Hub. After you have installed Docker, you may run the container as follows.

docker run -it \
 -p 8888:8888 \
 -p 6006:6006 \
 --gpus all \
 oneoffcoder/book-datascience

Note that this Docker container has Jupyter Lab running on port 8888. You may access Jupyter Lab at http://localhost:8888 when the Docker container is running.

The diagram below is a high-level map for this notebook-heavy collection. It groups the topics into foundations, modeling methods, causal reasoning, and domain applications so the overall structure is easier to scan before diving into individual notebooks.

Use the diagram as orientation rather than a strict prerequisite chain. The book is intentionally broad, but most notebooks fit into one of those larger clusters.

General

• 1. Missing Data
• 2. Principal Component Analysis
• 3. Principal Component Analysis - Iris
• 4. Dimension Reduction or Concept Learning?
• 5. Latent Dirichlet Allocation
• 6. Conditional Multivariate Normal Distribution
• 7. Conditional Bivariate Gaussians
• 8. Conditional Multivariate Gaussian, In Depth
• 9. Entropy and Mutual Information
• 10. Mutual Information for Gaussian Variables
• 11. Conditional Mutual Information for Gaussian Variables
• 12. Partial Correlation
• 13. Precision-Recall and Receiver Operating Characteristic Curves
• 14. Kalman Filter, I
• 15. Kalman Filter, II
• 16. Gaussian Hidden Markov Models
• 17. Iterative Proportional Fitting
• 18. Iterative Proportional Fitting, Higher Dimensions
• 19. One-Way ANOVA
• 20. Optimizing a Function
• 21. Brier Score
• 22. Log Loss
• 23. Spherical Payoff
• 24. Proper Scoring Rules and Model Calibration

Distributions

• 1. Generating Normally Distributed Values
• 2. Kullback-Leibler Divergence
• 3. Dirichlet-Multinomial Distribution
• 4. Schedule Risk Analysis Distributions
• 5. Gaussian Mixture Models
• 6. Dirichlet and Guassian Mixture Models
• 7. Data Discretization and Gaussian Mixture Models
• 8. K-means, BIC, AIC
• 9. PDFs and CDFs
• 10. s-curves
• 11. Markov Chain, Stationary Distribution

Regression

• 1. Correlation vs Regression Coefficient
• 2. Regression Errors
• 3. Psuedo r-squared for logistic regression
• 4. McFadden’s Psuedo R^2
• 5. Logistic Regression and Naive Bayes
• 6. Estimating Standard Error and Significance of Regression Coefficients
• 7. Logistic Regression on Probabilities
• 8. Safe and Strong Screening for Generalized LASSO
• 9. Log-linear Models for Three-way Tables
• 10. Log-Linear Models and Graphical Models
• 11. Linear Mixed Model
• 12. Iteratively Reweighted Least Squares Regression
• 13. Linear Mixed-Effect Regression

Survival Analysis

• 1. Survival Analysis Functions
• 2. Multivariate Survival Analysis

Rating and Ranking

• 1. Bradley-Terry-Luce Model
• 2. Massey’s Method
• 3. Massey’s Method, Offense and Defense
• 4. Colley’s Method
• 5. Keener’s Method
• 6. Markov Method
• 7. Reordering Method

Gradient Descent

• 1. Gradient descent
• 2. Regression, Gradient Descent
• 3. Logistic Regression, Gradient Descent
• 4. Poisson Regression, Gradient Descent
• 5. Neural Networks, Gradient Descent
• 6. Stochastic Gradient Descent for Online Learning
• 7. Differentiation and Gradient

Natural Language Processing

• 1. Latent Semantic Analysis
• 2. Topic Modeling with Gensim
• 3. Classification with GPT/Ollama Embeddings

Deep Learning

• 1. Artificial Neural Network
• 2. Restricted Boltzmann Machine
• 3. Recurrent Neural Network (RNN), Classification
• 4. Pose Estimation
• 5. Outlier Detection with Autoencoders
• 6. Data Imputation with Autoencoders
• 7. Autoencoders, Detecting Malicious URLs

COVID-19

• 1. COVID Hubei Data
• 2. Differential Diagnosis of COVID-19 with Bayesian Belief Networks

Chernoff Faces

• 1. Chernoff Faces
• 2. Chernoff Faces, Deep Learning
• 3. Chernoff Faces, Classification
• 4. Chernoff Faces, Inception v3

Causality

• 1. Simpson’s Paradox
• 2. d-separation, Active Paths
• 3. d-separation, Graph Transformation
• 4. Backdoor Criterion
• 5. Frontdoor/Backdoor Adjustment Formulas
• 6. Generating Random Bayesian Network
• 7. Creating a Junction Tree
• 8. Inference in Gaussian Networks
• 9. Causal Inference
• 10. Dynamic Bayesian Network, Markov Chain
• 11. Dynamic Bayesian Networks, Hidden Markov Models
• 12. Propensity Score Matching
• 13. Estimating Conditional Probabilities
• 14. Explanation vs Prediction
• 15. Explanation vs Prediction, Imbalanced Data
• 16. Frisch-Waugh-Lowell Theorem
• 17. Pearl Causal Hierarchy

Retail

• 1. Pricing Elasticity of Demand
• 2. Pricing Elasticity of Demand Modeling
• 3. Optimizing Marginal Revenue from the Demand Curve
• 4. Optimizing Marginal Revenue from the Demand Curve, Kaggle
• 5. Multi-Objective Optimization for Demand Curve
• 6. Demand Curve Fitting
• 7. Modeling Non-linear Pricing Elasiticity

References

• Bibliography

About

One-Off Coder is an educational, service and product company. Please visit us online to discover how we may help you achieve life-long success in your personal coding career or with your company’s business goals and objectives.

• Website

• Facebook

• Twitter

• Instagram

• YouTube

• LinkedIn

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License by One-Off Coder.



The full source code is available on GitHub.

Cite this book as follows.:

@misc{oneoffcoder_datascience_topics_2019,
title={Data Science Topics},
url={https://datascience.oneoffcoder.com},
author={One-Off Coder},
year={2019},
month={Nov}}

Author

Jee Vang, Ph.D.

• Patreon