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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. Nearly all of the machine studying fashions utilized in trendy expertise are statistical fashions. So, having a powerful understanding of statistics will make it simpler so that you can be taught 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 embody 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 gives sensible examples and code snippets from the e-book “Practical Statistics for Data Scientists” that cowl important statistical methods 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 observe 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 regulation 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 consists of complete documentation and examples for performing numerous statistical assessments, linear fashions, time collection evaluation, and extra. We are able to 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 Likelihood
Repository: tensorflow/likelihood
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 wonderful useful resource for these serious about combining deep studying with statistical modeling.
The documentation accommodates examples of linear blended results fashions, hierarchical linear fashions, probabilistic principal parts 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 numerous statistical duties. It gives concise steerage for likelihood and statistics, together with ideas comparable to steady distribution, likelihood idea, random variables, expectation, variance, and inequalities. You possibly can both use the make command to entry the cookbook domestically or obtain the PDF file. The repository additionally consists of LaTeX information for the varied statistical ideas.
6. Seeing Principle
Repository: seeingtheory/Seeing-Principle
Seeing Principle is a visible introduction to likelihood and statistics. This repository consists of interactive visualizations and explanations that make advanced statistical ideas extra accessible and simpler to grasp, particularly for visible learners.
It’s a extremely interactive e-book for novices and covers numerous subjects comparable to fundamental likelihood, compound likelihood, likelihood 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 protecting basic statistics, mathematical programming, and scientific computing utilizing Python. It’s a precious useful resource for anybody seeking to strengthen their statistical and mathematical programming expertise.
It consists of 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 consists of code examples and Jupyter notebooks from the e-book “Python for Probability, Statistics, and Machine Learning” that cowl a variety of subjects, from fundamental likelihood and statistics to superior machine studying methods.
Inside the “chapters” folder, there are three subfolders containing Jupyter notebooks on statistics, likelihood, and machine studying. Every pocket book consists of 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 accommodates VIP cheatsheets for Stanford’s Likelihood 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 fashionable cheatsheet that covers subjects on conditional likelihood, 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 assist you to be taught the fundamentals of algebra, calculus, statistics, likelihood, vectors, and matrices by Python Jupyter Notebooks.
Closing Ideas
Studying sources shared on GitHub are created by specialists and the open-source neighborhood, aiming to share their information to pave a better path for novices 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 fashionable 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 learning and hold constructing wonderful issues.
Abid Ali Awan (@1abidaliawan) is an authorized knowledge 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 knowledge science applied sciences. Abid holds a Grasp’s diploma in expertise 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.
