Statistics is at the core of data science. Unfortunately, our knowledge of statistics is not where it should be. We are generally eager to try new state-of-the-art models. How could we not be? They look fancy, their performance is impressive, and, in many cases, preprocessing steps are handled automatically. However, as these models advance, our control over the data decreases. This is especially problematic in the new AI era. Many practitioners can apply some kind of statistical procedure to the data with the help of AI, but the results can be misleading without that knowledge. Nowadays, being able to run a procedure is not what sets you apart; data expertise is.
This is an applied statistical course focusing on many statistical topics without getting lost in the mathematics. The first 18 notebooks explain frequentist concepts, while the rest focus on Bayesian statistics. Although each topic is covered separately, this course is ideally designed to be followed from beginning to end, since some topics serve as foundations for the upcoming ones. I hope the readers find it useful.
- Chandler, D. (1987). Introduction to modern statistical. Mechanics. Oxford University Press, Oxford, UK, 5(449), 11.
- Herzog, M. H., Francis, G., & Clarke, A. (2019). Understanding statistics and experimental design: how to not lie with statistics (p. 142). Springer Nature.
- Spiegelhalter, D. (2019). The art of statistics: Learning from data. Penguin UK.
- Newbold, P., Carlson, W. L., & Thorne, B. M. (2013). Statistics for business and economics. Pearson.
- Efron, B., & Hastie, T. (2021). Computer age statistical inference, student edition: algorithms, evidence, and data science (Vol. 6). Cambridge University Press.
- Martin, O., Ma, E., & Rochford, A. (2018). Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. Packt Publishing Ltd.
- Clyde, M., Cetinkaya-Rundel, M., Rundel, C., Banks, D., Chai, C., & Huang, L. (2020). An introduction to Bayesian thinking. A Companion to the Statistics with R Course.
- Harrell, F. E. (2001). Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis (Vol. 608). New York: springer.
- McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.
- Gelman, A., Hill, J., & Vehtari, A. (2021). Regression and other stories. Cambridge University Press.
Other References
- Gillett, R. (1989). Confidence interval construction by Stein's method: a practical and economical approach to sample size determination. Journal of marketing research, 26(2), 237-240.
- MarinStatsLectures-R Programming & Statistics (Link)
- Lecture Notes of ENS 505 Course by Sinan Yıldırım
├── 01 - Sampling.ipynb
├── 02 - Resampling.ipynb
├── 03 - Statistical Inference of a Single Population - Continuous Data.ipynb
├── 04 - Statistical Inference of a Single Population - Proportions.ipynb
├── 05 - Statistical Inference of Two Populations.ipynb
├── 06 - Statistical Inference of Multiple Populations.ipynb
├── 07 - Categorical and Nonparametric Tests.ipynb
├── 08 - Sample Size Selection - Continuous Data.ipynb
├── 09 - Sample Size Selection - Proportions.ipynb
├── 10 - Linear Regression.ipynb
├── 11 - Generalized Linear Models.ipynb
├── 12 - Nonlinear Regression.ipynb
├── 13 - Prespescifying Model Complexity.ipynb
├── 14 - Regularized Linear Models.ipynb
├── 15 - Data Reduction.ipynb
├── 16 - Handling Missing Values for Inference.ipynb
├── 17 - Data Transformations.ipynb
├── 18 - Model Evaluation.ipynb
├── 19 - Introduction to Bayesian Statistics.ipynb
├── 20 - Hierarchical Models.ipynb
├── 21 - Bayesian Hypothesis Testing.ipynb
├── 22 - Bayesian Linear Regression.ipynb
├── 23 - Bayesian Generalized Linear Models.ipynb
├── 24 - Bayesian Regularized Linear Models.ipynb
├── 25 - Bayesian Model Evaluation.ipynb
├── 26 - Bayesian Updates.ipynb
├── src/
│ ├── redun.py
│ └── utils.py
├── requirements.txt
└── README.md