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Reading list

6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 GitHub Repositories to Master Statistics

Yana Khare
Yana Khare 13 Aug, 2024
9 min read

Introduction

Statistics is a cornerstone of data science, machine learning, and many analytical domains. Mastering it can significantly enhance your ability to interpret data and make informed decisions. GitHub hosts numerous repositories that are excellent resources for anyone looking to deepen their statistical knowledge. This looks at the top 10 GitHub repositories that can help you master statistics.

Top 10 GitHub Repositories to Master Statistics

Table of contents

How Do GitHub Repositories Help to Master Statistics?

GitHub repositories provide a wealth of materials accessible to various levels of experience and learning styles, making them an effective tool for understanding statistics. You may learn statistics by using GitHub repositories in the following ways:

  • Interactive Examples: Many GitHub repositories include code examples, and projects that allow you to practice statistical concepts hands on. This active engagement helps reinforce learning and solidify your understanding.
  • Curated Resources: Many repositories provide curated books, courses, and other educational materials to help you on your learning journey, whether you’re an accomplished learner or a novice.
  • Open-Source Collaboration: Users may collaborate on projects using the open source GitHub platform. Contributing to repositories related to statistics or reading the efforts of others can help you learn from the community and obtain a variety of viewpoints on statistical techniques.
  • Research and Innovation: To keep abreast of new techniques and trends in the discipline, peruse repositories that showcase the most recent statistical research and innovations.

Top 10 GitHub Repositories to Master Statistics

1. Data Science Resources

The Data Science Resources repository is a carefully chosen compilation of resources, instruments, and guides for understanding and using data science. It is a thorough manual covering a wide range of subjects, including statistics, machine learning, data visualization, and programming, for novice and seasoned data scientists. The repository is a one stop shop for anybody wishing to improve their data science abilities because it contains connections to tutorials, books, courses, datasets, and software tools.

Key features of the repository include:

  • Curated Learning Paths: Users may follow a guided progression across several data science domains using the repository’s materials, organized into structured learning routes. This is especially helpful for newcomers who want guidance on where to begin and how to improve their abilities.
  • Extensive Coverage: The materials address several data science issues, ranging from big data technology and sophisticated machine learning techniques to basic statistics and probability. As a result, they can be used by individuals at any skill level, from beginners to experts looking to expand their knowledge.
  • Community Contributions: The repository is available for contributions from the data science community to keep it current with the newest methods, instruments, and best practices. This cooperative approach maintains the content’s value and relevance.

Access GitHub Repositories to Master Statistics Here

2. Elements of Statistical Learning

The groundbreaking book by Trevor Hastie, Robert Tibshirani, and Jerome Friedman has an accompanying resource in the Elements of Statistical Learning repository. The book is one of the most comprehensive on statistical learning. It thoroughly discusses subjects like linear regression, classification, resampling techniques, model selection, and unsupervised learning.

GitHub Repositories to Master Statistics

The repository includes:

  • Exercises and Solutions: The book provides practical exercises that allow learners to apply the principles covered. These exercises also provide self-assessment solutions.
  • Code Examples: R, Python, or other programming language implementations of different statistical learning methods that show how abstract ideas are applied in real-world scenarios.
  • Supplementary Materials: Extra materials that improve the learning process, such as datasets, code scripts, and lecture slides.

Access GitHub Repositories to Master Statistics Here

3. Think Bayes

A Python repository called Think Bayes provides an introduction to Bayesian statistics. It is based on Allen B. Downey’s book Think Bayes, renowned for its clear and helpful explanation of Bayesian statistics. The repository makes complicated ideas understandable to a broad audience by offering a succinct and straightforward introduction to Bayesian approaches.

The repository features:

  • Python Code Examples: Learners can observe how Bayesian analysis is carried out programmatically using Python scripts that apply Bayesian statistical methods.
  • Practical Scenarios: Real-world examples demonstrate how Bayesian statistics can solve practical problems, such as predicting outcomes and updating beliefs based on new data.
  • Detailed Explanations: Users can better grasp the underlying statistical ideas and the logic of Bayesian techniques by consulting the extensive explanations accompanying each example.

Access GitHub Repositories to Master Statistics Here

4. Think Stats

For people who would instead learn statistics through a programming-centric approach, there is a repository called Think Stats. It is based on Allen B. Downey’s book Think Stats, which provides a valuable introduction to statistical principles using Python. This repository covers numerous subjects, including regression analysis, estimation, probability distributions, and hypothesis testing. Code examples demonstrate how these ideas are applied in real-world situations.

GitHub Repositories to Master Statistics

The repository includes:

  • Step-by-Step Code Examples: Python programs that guide you through statistical studies so you may learn by doing are examples of step-by-step code. Every example expands on the one before it, progressively getting more sophisticated to cover more complex subjects.
  • Data Sets: Real-world data sets are integrated, offering a valuable framework for utilizing statistical methods. This allows you to deal with data representing real difficulties, which helps to reinforce your learning.
  • Exercises and Projects: The repository also has a few exercises and quick projects that cover the topics discussed in the book, helping you put what you’ve learned into practice.

Access GitHub Repositories to Master Statistics Here

5. Introduction to Statistical Learning

The book An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani has a Python companion resource called the Introduction to Statistical Learning repository. The fundamentals of statistical learning are covered in the book and the repository. These include support vector machines, clustering, resampling techniques, shrinkage approaches, tree-based algorithms, and linear regression.

The repository provides:

  • Python Implementations: The examples and exercises in the text are replicated in Python code that goes with each chapter. This makes things simpler for students who would study Python rather than R, the original language used in the book.
  • Detailed Notebooks: Jupyter Notebooks that demonstrate important ideas and let you view the code and the result interactively. These notebooks aid in bridging the knowledge gap between practice and theory.
  • Supplementary Materials: Additional resources, including datasets and visualizations, enhance the learning experience by making the material more interactive and applied.

Access GitHub Repositories to Master Statistics Here

6. Bayesian Methods for Hackers

The Bayesian Methods for Hackers repository provides a dynamic and approachable introduction to Bayesian statistics and probabilistic programming. Situated in a sequence of Jupyter Notebooks, it provides an accessible substitute for conventional, more mathematically demanding statistical methods by guiding you through the fundamentals of Bayesian inference.

GitHub Repositories to Master Statistics

Key features of the repository include:

  • Interactive Learning: Jupyter Notebooks provide an interactive learning environment. Users can conduct simulations, adjust parameters, and observe the real-time effects of their modifications. This practical method aids in deciphering Bayesian statistics.
  • Visual Explanations: By simplifying complex ideas, visualizations aid in understanding the fundamental concepts of Bayesian techniques. Prior distributions, likelihoods, and posterior distributions are examples of abstract concepts that are easier to comprehend when using the visual method.
  • Real-World Examples: The repository contains practical examples demonstrating the application of Bayesian approaches to real-world issues, such as forecasting election outcomes or calculating the likelihood of occurrences. These illustrations put the theory in perspective and show how useful Bayesian statistics are in real-world situations.

Access GitHub Repositories to Master Statistics Here

7. Stats-Maths-with-Python

The GitHub repository “Stats-Maths-with-Python” by tirthajyoti provides a comprehensive collection of Jupyter notebooks, Python scripts, and resources focused on statistics, mathematics, and their applications using Python. The repository is designed to help users understand and apply fundamental concepts in statistics and mathematics through practical coding examples. Key topics include probability distributions, hypothesis testing, linear algebra, calculus, and data visualization. The repository is a valuable resource for students, educators, and professionals looking to enhance their knowledge in these areas through hands-on Python programming..

Key features of the repository include:

  1. Comprehensive Coverage: The repository includes a wide range of topics such as probability distributions, hypothesis testing, linear algebra, calculus, and more, providing a solid foundation in both statistics and mathematics.
  2. Hands-On Learning: Each concept is accompanied by practical examples and Python code, allowing users to directly apply what they learn and gain hands-on experience.
  3. Jupyter Notebooks: The use of Jupyter notebooks makes the content interactive and easy to follow, with clear explanations and visualizations to enhance understanding.
  4. Educational Resource: The repository serves as an excellent educational tool for students, educators, and professionals aiming to improve their knowledge in statistics and mathematics through Python programming.

Access GitHub Repositories to Master Statistics Here

8. Probabilistic Reasoning and Statistical Analysis in TensorFlow

TensorFlow Probability is a library built on top of TensorFlow. It is a potent library that aims to integrate sophisticated probabilistic reasoning into deep learning and machine learning. Users may include uncertainty and variability in their models by utilizing the repository’s probabilistic modeling, statistical inference, and machine learning tools. This is very helpful for tasks like Bayesian inference, where comprehending the uncertainty in predictions is just as crucial as the predictions themselves.

GitHub Repositories to Master Statistics

Key features of the repository include:

  • Probabilistic Models: The library facilitates the construction of sophisticated models, such as variational inference, Gaussian processes, and hierarchical models. These models are essential for situations where forecasts need to account for uncertainty.
  • Integration with TensorFlow: TensorFlow Probability is integrated with TensorFlow. Thus enabling users to use TensorFlow’s robust computational graph and GPU acceleration features. This facilitates the scalability of probabilistic models to manage big datasets and intricate calculations.
  • Rich Set of Distributions: The repository contains many random variables, bijections, and probability distributions necessary for creating and utilizing probabilistic models. These technologies make it possible to model data uncertainty effectively and adaptably.

Access GitHub Repositories to Master Statistics Here

9. Practical Statistics for Data Scientists

The Practical Statistics for Data Scientists repository is an add-on for Peter and Andrew Bruce’s book. It highlights the vital statistical ideas that data scientists should be familiar with, especially those most pertinent to practical data analysis applications. Numerous subjects are covered, including probability distributions, machine learning, regression, hypothesis testing, and exploratory data analysis.

Key aspects of the repository include:

  • Focus on Data Science Applications: The repository strongly emphasizes using statistical techniques in real-world data science applications. This covers the combination of conventional statistical methods with machine learning algorithms.
  • Python Implementations: The repository has code that shows how to use the statistical techniques covered in the book in Python. This is particularly useful for data scientists who use Python as their primary programming language.
  • Case Studies and Examples: Case studies and real-world examples show how statistical techniques may address typical data science issues, including feature selection, data cleansing, and predictive modeling.

Access GitHub Repositories to Master Statistics Here

10. Statsmodels: Statistical Modeling and Econometrics in Python

Statsmodels repository offers classes and methods for estimating various statistical models, running statistical tests, and analyzing data. Since it specializes in econometric analysis, professionals in subjects requiring intricate statistical modeling highly recommend it as a resource.

GitHub Repositories to Master Statistics

Features of the repository include:

  • Wide Range of Models: Time series analysis, mixed effects models, linear regression, and generalized linear models (GLMs) are just a few of the statistical models that Statsmodels offers. Because of its adaptability, it is suitable for a variety of statistical analytic jobs.
  • Statistical Tests: To conduct thorough hypothesis testing and data validation, the library offers instruments for various statistical tests, including chi-square and t-tests.
  • Econometrics Focus: It is explicitly tailored for econometrics, such as instrumental variable estimation, systems of equations, and panel data models. This makes it particularly useful for economists and financial analysts.

Access GitHub Repositories to Master Statistics Here

Conclusion

These 10 GitHub repositories offer many resources for mastering statistics, from theoretical foundations to practical applications. Whether you are a beginner or an experienced data scientist, these repositories can help you enhance your statistical knowledge. Dive in, explore the code, and start mastering statistics today!

github repositoriesMaster Statistics
Yana Khare
Yana Khare 13 Aug, 2024

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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