- Chapter 0: Foundations of Python
- Basic syntax
- Data types, indexing, and slicing
- Flow control and looping
- Functions
- Object-oriented programming
- List comprehensions
- Regular expression
- Data input and output
- Basic text files
- Excel
- Database

- Chapter 1: Essential libraries
- Numpy
- Pandas
- Basic data visualization
- Scatter Plots
- Histograms
- Cumulative Frequencies
- Error-bars
- Box plots
- Pie Charts

- Chapter 2: Statistics brief review
- Descriptive statistics
- Distribution center
- Quantifying variability

- Random variables and distributions
- Discrete distributions
- Continuous distributions
- Maximum Likelihood

- Hypothesis testing
- General procedure and normality check
- Types of Error and ROC curve

- Common tests
- T-test for a mean value and Wilcoxon signed rank sum test
- Paired t-test and Mann-Whitney test
- ANOVA
- Multiple comparisons (Tukey’s test, Bonferroni correction, and Holm correction)
- Kruskal-Wallis test
- Two-way and Three-way ANOVA
- Tests on categorical data

- Design of Experiments

- Descriptive statistics
- Chapter 3: Statistical methods and modeling
- Linear correlation
- Linear regression
- Ordinary least squares
- Polynomial regression
- Ridge regression
- Lasso regression
- Elastic-net regression

- Regression analysis
- Logistic regression
- Ordinary logistic regression
- Nonparametric methods
- Bootstrapping
- Multivariate data analysis
- Markov-chain-Monte-Carlo simulation
- Time series analysis
- Extracting statistics
- Autocorrelation & moving average models
- ARIMA models
- Seasonality and exogenous variables
- Hidden Markov Model

- Dimension reduction and feature extraction
- Singular value decomposition and matrix factorization
- Principal components analysis (PCA)
- Multi-dimensional scaling (MDS)

- Chapter 4: Clustering
- Hierarchical clustering
- K-means clustering
- Gaussian mixture models
- Model selection and fine-tuning the clustering

- Chapter 5: Classification
- k-Nearest neighbors
- Decision tree
- Regression tree
- Random forests
- Naïve Bayes
- Gradient boosted decision trees
- Support vector machines
- Neural networks

- Chapter 6: Association rules
- Apriori algorithm
- FP-growth algorithm

- Chapter 7: Text mining
- Basic natural language processing
- Data processing and conversion
- Text classification
- Topic modeling
- Generative models and latent dirichlet allocation (LDA)
- Social network analysis

- Chapter 8: Deep learning
- Preface
- Convolutional neural networks

- Chapter 9: Reinforcement learning
- Chapter 10: Mathematical programming
- Resources in Python
- Sicipy(optimize)
- Pyomo
- Pulp

- Common solvers
- Gurobi
- CPLEX
- lp_solver
- GLPK
- COIN-OR
- SCIP
- Google OR-Tools

- Resources in Python
- Chapter 11: (Meta)Heuristic search techniques
- Local search
- Basic of genetic algorithms and introduction of DEAP

- Chapter 12: Discrete-event simulation
- Knowledge for simulation
- Output analysis
- SimPy

- Chapter 13: Advanced data visualization
- Interactive plots
- 3D plots

- Appendix A: Data cleansing and wrangling
- Appendix B: Working with varied data sources