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Studying statistics is a core a part of your journey towards changing into a knowledge scientist, information analyst, and even an AI engineer. Nearly all of the machine studying fashions utilized in fashionable know-how are statistical fashions. So, having a robust understanding of statistics will make it simpler so that you can be taught and construct superior AI applied sciences.
On this weblog, we’ll discover 10 GitHub repositories that will help you grasp statistics. These repositories embrace code examples, books, Python libraries, guides, documentations, and visible studying supplies.
1. Sensible Statistics for Knowledge Scientists
Repository: gedeck/practical-statistics-for-data-scientists
This repository affords sensible examples and code snippets from the e-book “Practical Statistics for Data Scientists” that cowl important statistical strategies and ideas. It’s a nice place to begin for information scientists who wish to apply statistical strategies in real-world eventualities.
The e-book’s code repository comprises correct R and Python code examples. If you’re used to the Jupyter Pocket book type of coding, it additionally supplies related examples in a Jupyter Pocket book for Python and R.
2. Probabilistic Programming and Bayesian Strategies for Hackers
Repository: CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Strategies-for-Hackers
This repository supplies an interactive, hands-on introduction to Bayesian strategies utilizing Python. The content material is introduced as Jupyter notebooks utilizing nbviewer, making it straightforward to comply with concept and Python code about Bayesian fashions and probabilistic programming.
The interactive e-book consists of an introduction to Bayesian strategies, getting began with Python’s PyMC library, Markov Chain Monte Carlo, the regulation of enormous numbers, loss capabilities, and extra.
3. Statsmodels: Statistical Modeling and Econometrics in Python
Repository: statsmodels/statsmodels
Statsmodels is a strong library for statistical modeling and econometrics in Python. This repository contains complete documentation and examples for performing varied statistical assessments, linear fashions, time collection evaluation, and extra. We are able to use these examples from the documentation to learn to carry out all types of statistical evaluation, together with time collection evaluation, survival evaluation, multivariate evaluation, linear regression, and extra.
4. TensorFlow Likelihood
Repository: tensorflow/chance
TensorFlow Likelihood is a library for probabilistic reasoning and statistical evaluation in TensorFlow. It extends TensorFlow core library with instruments for constructing and coaching probabilistic fashions, making it a superb useful resource for these excited about combining deep studying with statistical modeling.
The documentation comprises examples of linear combined results fashions, hierarchical linear fashions, probabilistic principal elements evaluation, bayesian neural networks, and extra.
5. The Likelihood and Statistics Cookbook
Repository: mavam/stat-cookbook
This repository is a group of recipes for fixing frequent statistical issues, serving as a useful reference for locating fast options and examples for varied statistical duties. It supplies concise steering for chance and statistics, together with ideas comparable to steady distribution, chance concept, random variables, expectation, variance, and inequalities. You’ll be able to both use the make command to entry the cookbook regionally or obtain the PDF file. The repository additionally contains LaTeX recordsdata for the varied statistical ideas.
6. Seeing Idea
Repository: seeingtheory/Seeing-Idea
Seeing Idea is a visible introduction to chance and statistics. This repository contains interactive visualizations and explanations that make complicated statistical ideas extra accessible and simpler to grasp, particularly for visible learners.
It’s a extremely interactive e-book for inexperienced persons and covers varied matters comparable to primary chance, compound chance, chance distributions, frequentist inference, bayesian inference, and regression evaluation.
7. Stats Maths with Python
Repository: tirthajyoti/Stats-Maths-with-Python
This repository comprises scripts and Jupyter notebooks overlaying common statistics, mathematical programming, and scientific computing utilizing Python. It’s a beneficial useful resource for anybody trying to strengthen their statistical and mathematical programming abilities.
It contains the examples on bayes rule, brownian movement, speculation testing, linear regression, and extra.
8. Python for Likelihood, Statistics, and Machine Studying
Repository: unpingco/Python-for-Likelihood-Statistics-and-Machine-Studying
This repository contains code examples and Jupyter notebooks from the e-book “Python for Probability, Statistics, and Machine Learning” that cowl a variety of matters, from primary chance and statistics to superior machine studying strategies.
Inside the “chapters” folder, there are three subfolders containing Jupyter notebooks on statistics, chance, and machine studying. Every pocket book contains code, output, and an outline explaining the methodology, code, and outcomes.
9. Likelihood and Statistics VIP Cheatsheets
Repository: shervinea/stanford-cme-106-probability-and-statistics
This repository comprises VIP cheatsheets for Stanford’s Likelihood and Statistics for Engineers course. The cheatsheets present concise summaries of key ideas and formulation, making them a helpful reference for college students and professionals.
It’s a in style cheatsheet that covers matters on conditional chance, random variables, parameter estimation, speculation testing, and extra.
10. Fundamental Arithmetic for Machine Studying
Repository: hrnbot/Fundamental-Arithmetic-for-Machine-Studying
Understanding the mathematical foundations is essential for mastering machine studying and statistics. This repository goals to demystify arithmetic and assist you be taught the fundamentals of algebra, calculus, statistics, chance, vectors, and matrices via Python Jupyter Notebooks.
Ultimate Ideas
Studying sources shared on GitHub are created by consultants and the open-source group, aiming to share their information to pave a neater path for inexperienced persons within the fields of knowledge science and statistics. You’ll be taught statistics by studying concept, fixing code examples, understanding mathematical ideas, constructing tasks, performing varied analyses, and exploring in style statistical instruments. All of those are coated within the GitHub repository talked about above. These sources are free, and anybody can contribute to enhance them. So, continue learning and hold constructing wonderful issues.
Abid Ali Awan (@1abidaliawan) is an authorized information scientist skilled who loves constructing machine studying fashions. At present, he’s specializing in content material creation and writing technical blogs on machine studying and information science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students fighting psychological sickness.
