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.
- 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
- 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
- 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
- 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
- 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
About
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Copyright
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}}