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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. 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 study and construct superior AI applied sciences.
On this weblog, we are going to discover 10 GitHub repositories that can assist you grasp statistics. These repositories embrace 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 affords sensible examples and code snippets from the e book “Sensible Statistics for Information Scientists” that cowl important statistical methods 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 fashion of coding, it additionally offers 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 offers an interactive, hands-on introduction to Bayesian strategies utilizing Python. The content material is offered as Jupyter notebooks utilizing nbviewer, making it straightforward to observe principle 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 huge numbers, loss capabilities, and extra.
3. Statsmodels: Statistical Modeling and Econometrics in Python
Repository: statsmodels/statsmodels
Statsmodels is a robust library for statistical modeling and econometrics in Python. This repository consists of complete documentation and examples for performing varied statistical assessments, linear fashions, time sequence evaluation, and extra. We are able to use these examples from the documentation to discover ways to carry out every kind of statistical evaluation, together with time sequence 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 desirous about combining deep studying with statistical modeling.
The documentation comprises 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 group of recipes for fixing widespread statistical issues, serving as a useful reference for locating fast options and examples for varied statistical duties. It offers concise steering for chance and statistics, together with ideas corresponding to steady distribution, chance principle, random variables, expectation, variance, and inequalities. You’ll be able to both use the make command to entry the cookbook domestically or obtain the PDF file. The repository additionally consists of 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 consists of 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 subjects corresponding to fundamental 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 masking basic statistics, mathematical programming, and scientific computing utilizing Python. It’s a beneficial useful resource for anybody seeking to strengthen their statistical and mathematical programming abilities.
It consists of 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 consists of code examples and Jupyter notebooks from the e book “Python for Chance, Statistics, and Machine Studying” that cowl a variety of subjects, from fundamental chance and statistics to superior machine studying methods.
Throughout the “chapters” folder, there are three subfolders containing Jupyter notebooks on statistics, chance, and machine studying. Every pocket book consists of 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 comprises 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 students and professionals.
It’s a widespread cheatsheet that covers subjects 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 make it easier to study the fundamentals of algebra, calculus, statistics, chance, vectors, and matrices by Python Jupyter Notebooks.
Ultimate Ideas
Studying sources shared on GitHub are created by consultants and the open-source neighborhood, aiming to share their data to pave a better path for inexperienced persons within the fields of information science and statistics. You’ll study statistics by studying principle, fixing code examples, understanding mathematical ideas, constructing initiatives, performing varied analyses, and exploring widespread statistical instruments. All of those are lined within the GitHub repository talked about above. These sources are free, and anybody can contribute to enhance them. So, continue to learn and maintain constructing wonderful issues.
Abid Ali Awan (@1abidaliawan) is an authorized information scientist skilled who loves constructing machine studying fashions. At the moment, 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 scuffling with psychological sickness.