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Studying statistics is a core a part of your journey towards changing into a knowledge scientist, knowledge analyst, and even an AI engineer. The vast majority of the machine studying fashions utilized in trendy 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 embody code examples, books, Python libraries, guides, documentations, and visible studying supplies.
1. Sensible Statistics for Information Scientists
Repository: gedeck/practical-statistics-for-data-scientists
This repository gives 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 start line for knowledge scientists who wish to apply statistical strategies in real-world eventualities.
The e-book’s code repository accommodates correct R and Python code examples. If you’re used to the Jupyter Pocket book model of coding, it additionally gives comparable 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 gives an interactive, hands-on introduction to Bayesian strategies utilizing Python. The content material is introduced as Jupyter notebooks utilizing nbviewer, making it simple to comply with idea 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 legislation of huge 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 numerous statistical exams, linear fashions, time collection evaluation, and extra. We will use these examples from the documentation to discover ways to carry out all types of statistical evaluation, together with time collection evaluation, survival evaluation, multivariate evaluation, linear regression, and extra.
4. TensorFlow Chance
Repository: tensorflow/chance
TensorFlow Chance 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 concerned about combining deep studying with statistical modeling.
The documentation accommodates examples of linear blended results fashions, hierarchical linear fashions, probabilistic principal elements evaluation, bayesian neural networks, and extra.
5. The Chance and Statistics Cookbook
Repository: mavam/stat-cookbook
This repository is a set of recipes for fixing widespread statistical issues, serving as a useful reference for locating fast options and examples for numerous statistical duties. It gives concise steerage for chance and statistics, together with ideas equivalent to steady distribution, chance idea, 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 assorted statistical ideas.
6. Seeing Principle
Repository: seeingtheory/Seeing-Principle
Seeing Principle 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 know, particularly for visible learners.
It’s a extremely interactive e-book for inexperienced persons and covers numerous subjects equivalent 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 accommodates scripts and Jupyter notebooks overlaying basic statistics, mathematical programming, and scientific computing utilizing Python. It’s a helpful useful resource for anybody trying to strengthen their statistical and mathematical programming expertise.
It contains the examples on bayes rule, brownian movement, speculation testing, linear regression, and extra.
8. Python for Chance, Statistics, and Machine Studying
Repository: unpingco/Python-for-Chance-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 subjects, from primary chance and statistics to superior machine studying strategies.
Throughout 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. Chance and Statistics VIP Cheatsheets
Repository: shervinea/stanford-cme-106-probability-and-statistics
This repository accommodates VIP cheatsheets for Stanford’s Chance and Statistics for Engineers course. The cheatsheets present concise summaries of key ideas and formulation, making them a useful reference for college kids and professionals.
It’s a common cheatsheet that covers subjects on conditional chance, random variables, parameter estimation, speculation testing, and extra.
10. Primary Arithmetic for Machine Studying
Repository: hrnbot/Primary-Arithmetic-for-Machine-Studying
Understanding the mathematical foundations is essential for mastering machine studying and statistics. This repository goals to demystify arithmetic and show you how to be taught the fundamentals of algebra, calculus, statistics, chance, vectors, and matrices via Python Jupyter Notebooks.
Remaining Ideas
Studying sources shared on GitHub are created by specialists 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 idea, fixing code examples, understanding mathematical ideas, constructing initiatives, performing numerous analyses, and exploring common 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 preserve constructing wonderful issues.
Abid Ali Awan (@1abidaliawan) is a licensed knowledge scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge 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 kids battling psychological sickness.
