A One-Stop Guide to Data Science Resources
Tags: learning, machinelearning, education
Published: 2023-10-16 11:34:33



![search](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/2vniiiny4v0s9hc6qgnv.png)
The landscape of data science is vast and ever-evolving. With the myriad of resources available, finding a comprehensive list that encapsulates the essence of this field can be daunting. I have curated a guide that offers a holistic view of data science, from understanding classical machine learning algorithms to Bayesian inference and deep learning.

I have hand-selected _free_ courses, ebooks, interactive webpages and podcasts that have played a role in my data science journey. Pick one or two and dive deep.


🔋 - requires more brain power 
🪫 - requires less brain power
💎 - personal favourites 
👑 - unique 


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##Understanding Machine Learning Predictions

We start with the foundational resources that introduce classical statistical and machine-learning algorithms

🔋🔋🪫🪫🪫
- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) - Jake VanderPlas

- [An Introduction to Statistical Learning](https://www.statlearning.com/) - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani - [\w Python PDF](https://hastie.su.domains/ISLP/ISLP_website.pdf)

- [Mathematics for Machine Learning](https://mml-book.github.io/) - PDF, video and Jupyter Notebooks for introductions to mathematics required for ML

- [Algorithms for Decision Making](https://algorithmsbook.com/) - 💎 an extensive overview of decision-making algorithms under uncertainty, with mathematical formulations and solutions.

- [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf) - Cambridge PDF - Shai Shalev Shwartz and Ben David
- [CS50's Introduction to Artificial Intelligence with Python](https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python?delta=0) - Harvard School of Engineering and Applied Sciences 

- [Introduction to Machine Learning](https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/course/) - MIT Open Learning Library

- [CS229 - Machine Learning](https://see.stanford.edu/Course/CS229) - Andrew Ng Stanford University
- [Machine Learning Foundations](https://github.com/jonkrohn/ML-foundations) - Classical Notebooks, Udemy|Youtube course covering Maths and Code by Jon Khrohn

- [mlcourse.ai](https://mlcourse.ai/book/index.html) - Open Machine Learning Course

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###Deep + Reinforcement Learning
🔋🔋🔋🪫🪫
The world of neural networks and deep architectures is vast. *MIT 6.S191* offers a comprehensive introduction, while resources like *Deep Learning for Coders with fastai & PyTorch* bridge the gap between theory and real-world applications.

- [MIT 6.S191 Introduction to Deep Learning](http://introtodeeplearning.com/) 
- [Deep Learning](https://atcold.github.io/pytorch-Deep-Learning/) - DS-GA 1008 · Spring 2020 · NYU Center For Data Science
- [Deep Learning
for Coders with
fastai & PyTorch](https://course.fast.ai/Resources/book.html) - Jeremy Howard &
Sylvain Gugger - [Practical Deep Learning](https://course.fast.ai/) course
- [Deep Reinforcement Learning](http://rail.eecs.berkeley.edu/deeprlcourse/) - CS 285 at UC Berkeley
- [🤗 Huggingface Reinforcement Learning](https://huggingface.co/learn/deep-rl-course/unit1/rl-framework)

- [mlcourse.ai](https://mlcourse.ai/book/index.html) - Open Machine Learning Course

- [Natural Language Processing](https://huggingface.co/learn/nlp-course/en/chapter1/1?fw=pt) 
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###Seeing Machine Learning Algorithms
🔋🪫🪫🪫🪫
For visual learners, this category is a goldmine. From *Illustrated ML* to *Distill*, these resources visually break down complex ML concepts, making them accessible and engaging.<br>

- [Illustrated ML](https://illustrated-machine-learning.github.io/) - 👑 Aims to simplify the intricate world of Machine Learning with clear illustrations.
- [Seeing Theory](https://seeing-theory.brown.edu/index.html) - A visual introduction to probability and statistics 💎
- [MLU-Expl{ai}n](https://mlu-explain.github.io/) - Visual explanations of core machine learning concepts
- [Distill](https://distill.pub/) - Machine Learning Research Should Be Clear, Dynamic and Vivid

___
![bayesian](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/bdd4eigf0ib55pjk2u7l.jpeg) 
###Bayesian Modelling: The White Box Machine Learning
🔋🔋🔋🔋🪫
Bayesian statistics has gained significant traction in the data science community. Resources like *Think Bayes* and *Bayesian Methods for Hackers* provide a perfect blend of theory and application. For those seeking depth, Richard McElreath's *Statistical Rethinking* lectures are a treasure trove.

- [Think Bayes](http://allendowney.github.io/ThinkBayes2) - Allen Downey’s classic as Jupyter Book
- [Bayesian Modeling and Computation in Python](https://bayesiancomputationbook.com/welcome.html) - Martin Osvaldo A, Kumar Ravin; Lao Junpeng, 2021
- [Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Probabilistic Programming and Bayesian Inference - DevAuthors
- [Statistical Rethinking 2019](https://github.com/Booleans/statistical-rethinking/raw/master/Statistical%20Rethinking%202nd%20Edition.pdf) - 👑 [Course Fall 2017](https://youtube.com/playlist?list=PLDcUM9US4XdM9_N6XUUFrhghGJ4K25bFc) + [Pre-recorded Lectures 2022](https://www.youtube.com/playlist?list=PLDcUM9US4XdMROZ57-OIRtIK0aOynbgZN) - [Material 2022](https://github.com/rmcelreath/stat_rethinking_2022) - [Richard McElreath](https://xcelab.net/rm/)'s lectures from Leipzig University - [PyMC codes](https://github.com/pymc-devs/pymc-resources/tree/main/Rethinking_2)
- [Bayes Rules!](https://www.bayesrulesbook.com/) - An Introduction to Applied Bayesian Modeling - Alicia A. Johnson, Miles Q. Ott, and  Mine Dogucu - [PyMC codes](https://github.com/pymc-devs/pymc-resources/tree/main/Bayes_Rules) - Jim Albert and Jingchen Hu - GitBook
- [Probability and Bayesian Modeling](https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html) -
- [Probabilistic Machine Learning](https://probml.github.io/pml-book/) - a book series by Kevin Murphy + [GitHub materials](https://github.com/probml)
- [Statistical Thinking for the 21st Century](https://statsthinking21.github.io/statsthinking21-core-site/) - Russell A. Poldrack's GitBook
- [Bayesian Thinking](https://statswithr.github.io/book/) - A Companion to the Statistics with R Course
- [Causal Inference for The Brave and True](https://matheusfacure.github.io/python-causality-handbook/landing-page.html)
- [Probability Distribution Explorer](https://distribution-explorer.github.io/index.html#) - Probability distributions and the stories behind

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###Extras: Convex Optimization
Optimization is at the core of many algorithms. Boyd and Vandenberghe's *Convex Optimization* is a staple, and the courses from Stanford provide a deeper understanding.

- [Convex Optimization](https://web.stanford.edu/~boyd/cvxbook/) – Boyd and Vandenberghe
- [EE364A - Convex Optimization I](https://see.stanford.edu/Course/EE364A) & [II](https://see.stanford.edu/Course/EE364B) - Stephen P. Boyd - Stanford University 



###Podcasts
For those on-the-go, podcasts like *Learning Bayesian Statistics* and *Linear Digressions* provide insights into data science trends, methodologies, and applications.

- [Learning Bayesian Statistics](https://podcasts.apple.com/dk/podcast/learning-bayesian-statistics/id1483485062) - Up-to-date dialogue on Bayesian inference
- [Linear Digressions](http://lineardigressions.com/) -  Host: Katie Malone & Ben Jaffe - Ended 2020 ML dialogue


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This curated list has been my one-stop guide collected when I was a beginner and seasoned professional in data science. Whether you're a visual learner, an avid reader, or someone who learns by doing, there's something here for everyone. Dive in and let the exploration begin!

If you have a resource worth adding, fire it on the comments.

Until then, keep on learning …