{"id":20688940,"url":"https://github.com/johnsesana/statistics-for-ds","last_synced_at":"2026-01-22T07:32:52.497Z","repository":{"id":256085509,"uuid":"854273916","full_name":"JohnSesana/Statistics-for-DS","owner":"JohnSesana","description":"General guide on topics to study for Data Science and Machine Learning","archived":false,"fork":false,"pushed_at":"2024-10-13T05:48:52.000Z","size":1041,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-13T23:14:37.620Z","etag":null,"topics":["data-science","machine-learning","python","statistics"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JohnSesana.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-08T20:39:22.000Z","updated_at":"2024-11-19T01:16:40.000Z","dependencies_parsed_at":"2025-01-17T16:52:33.311Z","dependency_job_id":"c8c5115f-9fba-423d-82bd-c61cbcb4f77e","html_url":"https://github.com/JohnSesana/Statistics-for-DS","commit_stats":null,"previous_names":["johnsesana/statistics-for-ds"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/JohnSesana/Statistics-for-DS","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnSesana%2FStatistics-for-DS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnSesana%2FStatistics-for-DS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnSesana%2FStatistics-for-DS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnSesana%2FStatistics-for-DS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JohnSesana","download_url":"https://codeload.github.com/JohnSesana/Statistics-for-DS/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JohnSesana%2FStatistics-for-DS/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28658107,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-22T01:17:37.254Z","status":"online","status_checked_at":"2026-01-22T02:00:07.137Z","response_time":144,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","machine-learning","python","statistics"],"created_at":"2024-11-16T23:07:34.705Z","updated_at":"2026-01-22T07:32:52.475Z","avatar_url":"https://github.com/JohnSesana.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"![image](https://github.com/user-attachments/assets/1483c2ec-19e9-40c5-85c5-f09932815fd6)\n\n# Statistics for Data Science\n\nThis repo contains a list of essential statistics topics to study for data science and machine learning.\n- Every topic contains a jupyter notebook with explanations and code examples. (Work in Progress)\n\n## 1. [Descriptive Statistics](https://github.com/JohnSesana/Statistics-for-DS/blob/main/01-Descriptive-Statistics.ipynb)\n\n- **Measures of Central Tendency**: Mean, Median, Mode\n- **Measures of Dispersion**: Variance, Standard Deviation, Range,Interquartile Range (IQR)\n- **Skewness and Kurtosis**: Understanding the shape of data distributions\n- **Percentiles and Quartiles**: Breaking data into sections\n\n## 2. [Probability Theory](https://github.com/JohnSesana/Statistics-for-DS/blob/main/02-Probability-Theory.ipynb)\n\n- **Probability Distributions**: Uniform, Normal (Gaussian), Binomial, Poisson, Exponential, etc.\n- **Conditional Probability**: Bayes’ Theorem, Independence, and Conditional Independence\n- **Law of Large Numbers and Central Limit Theorem (CLT)**\n- **Combinatorics**: Permutations and Combinations\n\n## 3. [Statistical Inference](https://github.com/JohnSesana/Statistics-for-DS/blob/main/03-Statistical-Inference.ipynb)\n\n- **Hypothesis Testing**: Null and Alternative Hypotheses, p-value, Significance Levels (α)\n- **Confidence Intervals**: Estimating population parameters\n- **Z-test, t-test, Chi-Square Test, ANOVA**: Parametric and Non-parametric tests\n- **Type I and Type II Errors**: False positives and false negatives\n- **Power of a Test**: Understanding how likely a test is to detect an effect\n\n## 4. [Regression Analysis](https://github.com/JohnSesana/Statistics-for-DS/blob/main/04-Regression-Analysis.ipynb)\n\n- **Linear Regression**: Simple and Multiple Regression, Assumptions, Interpretation of Coefficients\n- **Logistic Regression**: Binary classification, Log-Odds, Interpretation\n- **Polynomial Regression**: Fitting non-linear data with polynomial terms\n- **Ridge and Lasso Regression**: Regularization techniques to prevent overfitting\n- **Bias-Variance Tradeoff**: Understanding the balance between model complexity and prediction accuracy\n\n## 5. Experimental Design\n\n- **A/B Testing**: Randomized controlled experiments, hypothesis testing\n- **Randomization and Blocking**: Techniques for reducing bias in experiments\n- **Design of Experiments (DOE)**: Factorial design, fractional factorial design, Latin squares\n\n## 6. Bayesian Statistics\n\n- **Bayesian Inference**: Prior, Likelihood, Posterior, and Updating Beliefs\n- **Markov Chain Monte Carlo (MCMC)**: Techniques for Bayesian estimation\n- **Hierarchical Bayesian Models**: Modeling complex data structures\n\n## 7. Time Series Analysis\n\n- **Stationarity**: Understanding trends, seasonality, and how to make data stationary\n- **Autocorrelation and Partial Autocorrelation**\n- **ARIMA Models**: Autoregressive Integrated Moving Average\n- **Exponential Smoothing**: Simple, Holt’s, and Holt-Winters models\n\n## 8. Multivariate Statistics\n\n- **Principal Component Analysis (PCA)**: Dimensionality reduction techniques\n- **Factor Analysis**: Identifying latent variables\n- **Clustering**: K-Means, Hierarchical Clustering, DBSCAN\n- **Discriminant Analysis**: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA)\n\n## 9. Probability Distributions\n\n- **Discrete Distributions**: Bernoulli, Binomial, Poisson, Geometric\n- **Continuous Distributions**: Normal, Exponential, Gamma, Beta\n- **Multivariate Distributions**: Multivariate Normal Distribution, Covariance, and Correlation\n\n## 10. Resampling Methods\n\n- **Bootstrap**: Estimating the sampling distribution of a statistic by resampling with replacement\n- **Cross-Validation**: Techniques like k-fold cross-validation for model validation\n- **Jackknife**: Estimating bias and variance in small samples\n\n## 11. Advanced Machine Learning Metrics\n\n- **Confusion Matrix**: True Positives, True Negatives, False Positives, False Negatives\n- **Precision, Recall, F1-Score**: Evaluating classification models\n- **ROC Curves and AUC**: Receiver Operating Characteristic curve for evaluating classifiers\n- **Lift and Gain Charts**: Performance of models in marketing analytics\n\n## 12. Survival Analysis\n\n- **Censoring and Survival Functions**: Understanding time-to-event data\n- **Kaplan-Meier Estimator**\n- **Cox Proportional Hazards Model**\n\n## 13. Non-Parametric Methods\n\n- **Mann-Whitney U Test**\n- **Kruskal-Wallis Test**\n- **Spearman’s Rank Correlation**\n- **Wilcoxon Signed-Rank Test**\n\n## 14. Spatial Statistics\n\n- **Spatial Autocorrelation**: Moran’s I, Geary’s C\n- **Kriging**: Geostatistical interpolation\n- **Point Pattern Analysis**: Analyzing spatial point data\n\n## 15. Missing Data Handling\n\n- **Imputation Techniques**: Mean/Median Imputation, KNN Imputation, Multiple Imputation\n- **Dealing with Missingness**: Missing Completely at Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR)\n\n## 16. Causal Inference\n\n- **Correlation vs. Causation**\n- **Causal Diagrams (DAGs)**: Direct and Indirect Effects\n- **Instrumental Variables**: Handling confounding variables\n- **Difference-in-Differences (DiD)**: Comparing treatment and control groups over time\n- **Propensity Score Matching**: Reducing selection bias\n\n## 17. Generalized Linear Models (GLM)\n\n- **Log-Linear Models**: Extension of linear models for count data\n- **Poisson and Negative Binomial Regression**: For modeling count data\n- **Probit and Tobit Models**: Handling censored or ordinal data\n\n## 18. Statistical Programming\n\n- **Python/R for Statistics**: Libraries like numpy, pandas, statsmodels, scikit-learn, and R packages like ggplot2, dplyr, caret, and glmnet\n- **Simulation and Monte Carlo Methods**: Simulating data and probabilistic models\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjohnsesana%2Fstatistics-for-ds","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjohnsesana%2Fstatistics-for-ds","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjohnsesana%2Fstatistics-for-ds/lists"}