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visibility: public
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# Data Science

**repo:** [academic/awesome-datascience](https://github.com/academic/awesome-datascience)  
**category:** [[computer-science|Computer Science]]
**related:** [[python|Python]] · [[machine-learning|Machine Learning]] · [[public-datasets|Public Datasets]] · [[data-engineering|Data Engineering]] · [[streaming|Streaming]]

---

### [Requestly - Free & Open-Source alternative to Postman](https://requestly.com/awesomedatascience)
   [All-in-one platform to Test, Mock and Intercept APIs](https://requestly.com/awesomedatascience)
   <br>
</div>

<hr>

<div align="center"><img src="./assets/head.jpg"></div>

# AWESOME DATA SCIENCE

[![Awesome](https://cdn.jsdelivr.net/gh/sindresorhus/awesome@d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 

Contributions are welcome - see [`CONTRIBUTING.md`](CONTRIBUTING.md).

**An open-source Data Science repository to learn and apply concepts toward solving real- world problems.**

This is a shortcut path to start studying **Data Science**. Just follow the steps to answer the questions, "What is Data Science, and what should I study to learn Data Science?"

<br>

## Sponsors

| Sponsor | Pitch |
| --- | --- |
| --- | Be the first to sponsor! `github@academic.io` |

## Table of Contents

- [What is [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science)?](#what-is-data-science)
- [Where do I Start?](#where-do-i-start)
- [Agents](#agents)
- [Training Resources](#training-resources)
  - [Tutorials](#tutorials)
  - [Free Courses](#free-courses)
  - [Massively Open Online Courses](#moocs)
  - [Intensive Programs](#intensive-programs)
  - [Colleges](#colleges)
- [The [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Toolbox](#the-data-science-toolbox)

  - [Algorithms](#algorithms)
    - [Supervised Learning](#supervised-learning)
    - [Unsupervised Learning](#unsupervised-learning)
    - [Semi-Supervised Learning](#semi-supervised-learning)
    - [Reinforcement Learning](#reinforcement-learning)
    - [Data Mining Algorithms](#data-mining-algorithms)
    - [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Architectures](#deep-learning-architectures)
  - [General [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Packages](#general-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-packages)
  - [Model Evaluation & Monitoring](#model-evaluation--monitoring)
    - [Evidently AI](#evidently-ai)
  - [Deep [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Packages](#deep-learning-packages)
    - [PyTorch Ecosystem](#pytorch-ecosystem)
    - [TensorFlow Ecosystem](#[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)-ecosystem)
    - [Keras Ecosystem](#keras-ecosystem)
  - [Visualization Tools](#visualization-tools)
  - [Miscellaneous Tools](#miscellaneous-tools)
- [Literature and Media](#literature-and-media)
  - [Books](#books)
    - [Book Deals (Affiliated)](#book-deals-affiliated)
  - [Journals, Publications, and Magazines](#journals-publications-and-magazines)
  - [Newsletters](#newsletters)
  - [Bloggers](#bloggers)
  - [Presentations](#presentations)
  - [Podcasts](#podcasts)
  - [YouTube Videos & Channels](#youtube-videos--channels)
- [Socialize](#socialize)
  - [Facebook Accounts](#facebook-accounts)
  - [Twitter Accounts](#twitter-accounts)
  - [Telegram Channels](#telegram-channels)
  - [Slack Communities](#[slack](/@harrisonqian/awesome/wiki/work/slack)-communities)
  - [GitHub Groups](#[github](/@harrisonqian/awesome/wiki/development-environment/github)-groups)
  - [Data Science Competitions](#data-science-competitions)
- [Fun](#fun)
  - [Infographics](#infographics)
  - [Datasets](#datasets)
  - [Comics](#comics)
- [Other [Awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome) Lists](#other-awesome-lists)
  - [Hobby](#hobby)

## What is Data Science?
**[`^        back to top        ^`](#awesome-data-science)**

Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. [Here](https://www.quora.com/Data-Science/What-is-data-science) you can find the biggest question for **Data Science** and hundreds of answers from experts.

| Link | Preview |
| --- | --- |
| [Data Science For Beginners](https://github.com/microsoft/Data-Science-For-Beginners) | Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science. |
| [What is Data Science @ O'reilly](https://www.oreilly.com/ideas/what-is-data-science) | _Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: “here’s a lot of data, what can you make from it?”_ |
| [What is Data Science @ Quora](https://www.quora.com/Data-Science/What-is-data-science) | Data Science is a combination of a number of aspects of Data such as Technology, Algorithm development, and data interference to study the data, analyse it, and find innovative solutions to difficult problems. Basically Data Science is all about Analysing data and driving for business growth by finding creative ways. |
| [The sexiest job of 21st century](https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century) | _Data scientists today are akin to Wall Street “quants” of the 1980s and 1990s. In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies. Then a variety of universities developed master’s programs in financial engineering, which churned out a second generation of talent that was more accessible to mainstream firms. The pattern was repeated later in the 1990s with search engineers, whose rarefied skills soon came to be taught in computer science programs._ |
| [Wikipedia](https://en.wikipedia.org/wiki/Data_science) | _Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data._ |
| [How to Become a Data Scientist](https://www.mastersindatascience.org/careers/data-scientist/) | _Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations._ |
| [a very short history of #datascience](https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/) | _The story of how data scientists became sexy is mostly the story of the coupling of the mature discipline of statistics with a very young one--computer science.  The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms._ |
|[Software Development Resources for Data Scientists](https://www.rstudio.com/blog/software-development-resources-for-data-scientists/)|_Data scientists concentrate on making sense of data through exploratory analysis, statistics, and models. Software developers apply a separate set of knowledge with different tools. Although their focus may seem unrelated, data science teams can benefit from adopting software development best practices. Version control, automated testing, and other dev skills help create reproducible, production-ready code and tools._|
|[Data Scientist Roadmap](https://www.scaler.com/blog/how-to-become-a-data-scientist/)|_Data science is an excellent career choice in today’s data-driven world where approx 328.77 million terabytes of data are generated daily. And this number is only increasing day by day, which in turn increases the demand for skilled data scientists who can utilize this data to drive business growth._|
|[Navigating Your Path to Becoming a Data Scientist](https://www.appliedaicourse.com/blog/how-to-become-a-data-scientist/)|_Data science is one of the most in-demand careers today. With businesses increasingly relying on data to make decisions, the need for skilled data scientists has grown rapidly. Whether it’s tech companies, healthcare organizations, or even government institutions, data scientists play a crucial role in turning raw data into valuable insights. But how do you become a data scientist, especially if you’re just starting out? _|

## Where do I Start?
**[`^        back to top        ^`](#awesome-data-science)**

While not strictly necessary, having a programming language is a crucial skill to be effective as a data scientist. Currently, the most popular language is _Python_, closely followed by _R_. Python is a general-purpose scripting language that sees applications in a wide variety of fields. R is a domain-specific language for statistics, which contains a lot of common statistics tools out of the box.

[Python](https://python.org/) is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as `pip install`), the package manager that comes bundled with Python, and [Anaconda](https://www.anaconda.com) (invoked as `conda install`), a powerful package manager that can install packages for Python, R, and can download executables like Git. 

Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: [Scikit-Learn](https://scikit-learn.org/stable/index.html) is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With [Pandas](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.

When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the [Free courses](#free-courses) we've listed below!

### Beginner Roadmap
If you're just starting out, here's a simple recommended path:

1. **Learn Python** – Start with basics: variables, loops, functions
2. **Learn core libraries** – Pandas, NumPy, Matplotlib, Scikit-Learn
3. **Practice with beginner projects** – Try Titanic survival or house price prediction on Kaggle
4. **Learn Math basics** – Statistics, Linear Algebra, Probability
5. **Move into ML** – Supervised learning → Unsupervised → Deep Learning

## Agents

This section contains agent frameworks and tools that are useful for data science workflows.

### Frameworks
- [ADK-Rust](https://github.com/zavora-ai/adk-rust) - Production-ready AI agent development kit for [Rust](/@harrisonqian/awesome/wiki/programming-languages/rust) with model-agnostic design (Gemini, OpenAI, Anthropic), multiple agent types (LLM, Graph, Workflow), MCP support, and built-in telemetry.

### Tools
- [Frostbyte MCP](https://github.com/OzorOwn/frostbyte-mcp) - MCP server providing 13 data tools for AI agents: real-time crypto prices, IP geolocation, DNS lookups, web scraping to [markdown](/@harrisonqian/awesome/wiki/miscellaneous/markdown), code execution, and screenshots. One API key for 40+ services.
- [Arch Tools](https://archtools.dev) - 61 production-ready AI API tools for [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science) workflows: code analysis, web scraping, NLP, image generation, crypto data, and search. [REST](/@harrisonqian/awesome/wiki/miscellaneous/rest) API and MCP protocol support. [GitHub](https://github.com/Deesmo/Arch-AI-Tools)

### Research & Knowledge Retrieval
- [BGPT MCP](https://bgpt.pro/mcp) - MCP server that gives AI agents access to a [database](/@harrisonqian/awesome/wiki/databases/database) of scientific [papers](/@harrisonqian/awesome/wiki/computer-science/papers) built from raw experimental data extracted from full-text studies. Returns 25+ structured fields per paper including methods, results, sample sizes, and quality scores. [GitHub](https://github.com/connerlambden/bgpt-mcp)

### Workflow
**[`^        back to top        ^`](#awesome-data-science)**
- [sim](https://sim.ai) - Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.

## Training Resources
**[`^        back to top        ^`](#awesome-data-science)**

How do you learn data science? By doing data science, of course! Okay, okay - that might not be particularly helpful when you're first starting out. In this section, we've listed some learning resources, in rough order from least to greatest commitment - [Tutorials](#tutorials), [Massively Open Online Courses (MOOCs)](#moocs), [Intensive Programs](#intensive-programs), and [Colleges](#colleges).

### Tutorials
**[`^        back to top        ^`](#awesome-data-science)**

- [1000 [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Projects](https://cloud.blobcity.com/#/ps/explore) you can run on the browser with IPython.
- [#tidytuesday](https://github.com/rfordatascience/tidytuesday) - A weekly data project aimed at the R ecosystem.
- [Data science your way](https://github.com/jadianes/data-science-your-way)
- [DataCamp Cheatsheets](https://www.datacamp.com/cheat-sheet) Cheatsheets for [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science).
- [PySpark Cheatsheet](https://github.com/kevinschaich/pyspark-cheatsheet)
- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning), [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) and [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) with [Python](/@harrisonqian/awesome/wiki/programming-languages/python) ](https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python)
- [Your Guide to Latent Dirichlet Allocation](https://medium.com/@lettier/how-does-lda-work-ill-explain-using-emoji-108abf40fa7d)
- [Tutorials of source code from the book Genetic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) with [Python](/@harrisonqian/awesome/wiki/programming-languages/python) by Clinton Sheppard](https://github.com/handcraftsman/GeneticAlgorithmsWithPython)
- [Tutorials to get started on signal processing for machine learning](https://github.com/jinglescode/python-signal-processing)
- [Realtime deployment](https://www.microprediction.com/python-1) Tutorial on [Python](/@harrisonqian/awesome/wiki/programming-languages/python) time-series model deployment.
- [Python for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science): A Beginner’s Guide](https://learntocodewith.me/posts/python-for-data-science/)
- [Minimum Viable Study Plan for [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Interviews](https://github.com/khangich/machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-interview)
- [Understand and Know [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Engineering by Building Solid Projects](http://mlzoomcamp.com/)
- [12 free [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) projects to practice [Python](/@harrisonqian/awesome/wiki/programming-languages/python) and Pandas](https://www.datawars.io/articles/12-free-data-science-projects-to-practice-python-and-pandas)
- [Best CV/Resume for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Freshers](https://enhancv.com/resume-examples/data-scientist/)
- [Understand [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Course in Java](https://www.alter-solutions.com/articles/java-data-science)
- [Data [Analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) Interview Questions (Beginner to Advanced)](https://www.appliedaicourse.com/blog/data-analytics-interview-questions/)
- [Top 100+ [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Interview Questions and Answers](https://www.appliedaicourse.com/blog/data-science-interview-questions/)
- [DataDriven - SQL, [Python](/@harrisonqian/awesome/wiki/programming-languages/python), and Data Modeling Interview Questions](https://www.datadriven.io/)

### Free Courses
**[`^        back to top        ^`](#awesome-data-science)**

- [Data Scientist with R](https://www.datacamp.com/tracks/data-scientist-with-r)
- [Data Scientist with Python](https://www.datacamp.com/tracks/data-scientist-with-python)
- [Genetic [Algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) OCW Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-1-introduction-and-scope/)
- [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an [Artificial Intelligence](/@harrisonqian/awesome/wiki/theory/artificial-intelligence) Expert
- [Convex Optimization](https://www.edx.org/course/convex-optimization) - Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
- [Learning from Data](https://home.work.caltech.edu/telecourse.html) - Introduction to [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) covering basic theory, [algorithms](/@harrisonqian/awesome/wiki/theory/algorithms) and applications
- [Kaggle](https://www.kaggle.com/learn) - Learn about [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science), [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning), [Python](/@harrisonqian/awesome/wiki/programming-languages/python) etc
- [ML Observability Fundamentals](https://arize.com/ml-observability-fundamentals/) - Learn how to monitor and root-cause production ML issues.
- [Weights & Biases Effective MLOps: Model Development](https://www.wandb.courses/courses/effective-mlops-model-development) - Free Course and Certification for building an end-to-end machine using W&B
- [Python for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) by Scaler](https://www.scaler.com/topics/course/python-for-data-science/) - This course is designed to empower beginners with the essential skills to excel in today's data-driven world. The comprehensive curriculum will give you a solid foundation in statistics, programming, [data visualization](/@harrisonqian/awesome/wiki/miscellaneous/data-visualization), and [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning).
- [MLSys-NYU-2022](https://github.com/jacopotagliabue/MLSys-NYU-2022/tree/main) - Slides, scripts and materials for the [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) in Finance course at NYU Tandon, 2022.
- [Hands-on Train and Deploy ML](https://github.com/Paulescu/hands-on-train-and-deploy-ml) - A hands-on course to train and deploy a serverless API that predicts crypto prices.
- [LLMOps: Building Real-World Applications With Large Language Models](https://www.comet.com/site/llm-course/) - Learn to build modern software with LLMs using the newest tools and techniques in the field.
- [Prompt Engineering for Vision Models](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/) - Learn to prompt cutting-edge [computer vision](/@harrisonqian/awesome/wiki/computer-science/computer-vision) models with natural language, coordinate points, bounding boxes, segmentation masks, and even other images in this free course from DeepLearning.AI.
- [Data Science Course By IBM](https://skillsbuild.org/students/course-catalog/data-science) - Free resources and learn what [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science) is and how it’s used in different industries.

  
### MOOC's
**[`^        back to top        ^`](#awesome-data-science)**

- [Coursera Introduction to Data Science](https://www.coursera.org/specializations/data-science)
- [Data Science - 9 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/jhu-data-science)
- [Data Mining - 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/data-mining)
- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning)
- [CS 109 Data Science](https://cs109.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/2015/)
- [OpenIntro](https://www.openintro.org/)
- [CS 171 Visualization](https://www.cs171.org/#!index.md)
- [Process Mining: [Data science](/@harrisonqian/awesome/wiki/programming-languages/data-science) in Action](https://www.coursera.org/learn/process-mining)
- [Oxford Deep Learning](https://www.cs.ox.ac.uk/projects/DeepLearn/)
- [Oxford [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu)
- [Oxford Machine Learning](https://www.cs.ox.ac.uk/research/ai_ml/index.html)
- [UBC [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) - video](https://www.cs.ubc.ca/~nando/540-2013/lectures.html)
- [Data Science Specialization](https://github.com/DataScienceSpecialization/courses)
- [Coursera [Big Data](/@harrisonqian/awesome/wiki/big-data/big-data) Specialization](https://www.coursera.org/specializations/big-data)
- [Statistical Thinking for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) and [Analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) by Edx](https://www.edx.org/course/statistical-thinking-for-data-science-and-analytic)
- [Cognitive Class AI by IBM](https://cognitiveclass.ai/)
- [Udacity - Deep Learning](https://www.udacity.com/course/intro-to-[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow)-for-deep-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)--ud187)
- [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion)
- [Microsoft Professional Program for Data Science](https://academy.microsoft.com/en-us/professional-program/tracks/data-science/)
- [COMP3222/COMP6246 - [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Technologies](https://tdgunes.com/COMP6246-2019Fall/)
- [CS 231 - Convolutional Neural Networks for Visual Recognition](https://cs231n.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/)
- [Coursera [Tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow) in practice](https://www.coursera.org/professional-certificates/tensorflow-in-practice)
- [Coursera [Deep Learning](/@harrisonqian/awesome/wiki/computer-science/deep-learning) Specialization](https://www.coursera.org/specializations/deep-learning)
- [365 [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Course](https://365datascience.com/)
- [Coursera Natural Language Processing Specialization](https://www.coursera.org/specializations/natural-language-processing)
- [Coursera GAN Specialization](https://www.coursera.org/specializations/generative-adversarial-networks-gans)
- [Codecademy's Data Science](https://www.codecademy.com/learn/paths/data-science)
- [Linear Algebra](https://ocw.mit.edu/courses/18-06sc-linear-algebra-fall-2011/) - Linear Algebra course by Gilbert Strang
- [A 2020 Vision of Linear Algebra (G. Strang)](https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/)
- [Python for [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/)
- [Data Science: Statistics & Machine Learning](https://www.coursera.org/specializations/data-science-statistics-machine-learning)
- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) Engineering for Production (MLOps)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
- [Recommender Systems Specialization from University of Minnesota](https://www.coursera.org/specializations/recommender-systems) is an intermediate/advanced level specialization focused on Recommender System on the Coursera platform.
- [Stanford [Artificial Intelligence](/@harrisonqian/awesome/wiki/theory/artificial-intelligence) Professional Program](https://online.stanford.edu/programs/artificial-intelligence-professional-program)
- [Data Scientist with Python](https://app.datacamp.com/learn/career-tracks/data-scientist-with-python)
- [Programming with Julia](https://www.udemy.com/course/programming-with-julia/)
- [Scaler [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) & [Machine Learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) Program](https://www.scaler.com/data-science-course/)
- [Data Science Skill Tree](https://labex.io/skilltrees/data-science)
- [Data Science for Beginners - Learn with AI tutor](https://codekidz.ai/lesson-intro/data-science-368dbf)
- [Machine [Learning](/@harrisonqian/awesome/wiki/programming-languages/learning) for Beginners - Learn with AI tutor](https://codekidz.ai/lesson-intro/machine-lear-36abfb)
- [Introduction to Data Science](https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-data-science)
-[Getting Started with Python for Data Science](https://www.codecademy.com/learn/getting-started-with-python-for-data-science) 
- [Google Advanced Data [Analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) Certificate](https://grow.google/data-analytics/) – Professional courses in data analysis, statistics, and [machine learning](/@harrisonqian/awesome/wiki/computer-science/machine-learning) fundamentals.
- [Maschinelle Sprachgebrauchsanalyse - Grundlagen der Korpuslinguistik](https://www.twillo.de/edu-sharing/components/collections?id=e6ce03ae-4660-49b0-be10-dcc92e71e796) - course material on text-mining / corpus-[linguistics](/@harrisonqian/awesome/wiki/computer-science/linguistics) *in German* funded by the federal state of North Rhine-Westphalia
- [Programmieren für Germanist*innen](https://www.twillo.de/edu-sharing/components/collections?id=16bac749-f10e-483f-9020-5d6365b4e092) - course material: programming in [python](/@harrisonqian/awesome/wiki/programming-languages/python) *in German* for [digital humanities](/@harrisonqian/awesome/wiki/health-and-social-science/digital-humanities) - funded by the federal state of North Rhine-Westphalia

### Intensive Programs
**[`^        back to top        ^`](#awesome-data-science)**

- [S2DS](https://www.s2ds.org/)
- [WorldQuant University Applied [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) Lab](https://www.wqu.edu/adsl)

### Colleges
**[`^        back to top        ^`](#awesome-data-science)**

- [A list of colleges and universities offering degrees in [data science](/@harrisonqian/awesome/wiki/programming-languages/data-science).](https://github.com/ryanswanstrom/awesome-datascience-colleges)
- [Data Science Degree @ Berkeley](https://ischoolonline.berkeley.edu/data-science/)
- [Data Science Degree @ UVA](https://datascience.virginia.edu/)
- [Data Science Degree @ Wisconsin](https://datasciencedegree.wisconsin.edu/)
- [BS in [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) & Applications](https://study.iitm.ac.in/ds/)
- [MS in Computer Information Systems @ Boston University](https://www.bu.edu/online/programs/graduate-programs/computer-information-systems-masters-degree/)
- [MS in Business [Analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) @ ASU Online](https://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/)
- [MS in Applied [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) @ Syracuse](https://ischool.syr.edu/academics/applied-data-science-masters-degree/)
- [M.S. Management & [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) @ Leuphana](https://www.leuphana.de/en/graduate-school/masters-programmes/management-data-science.html)
- [Master of [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) @ Melbourne University](https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/#overview)
- [Msc in [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) @ The University of Edinburgh](https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=902)
- [Master of Management [Analytics](/@harrisonqian/awesome/wiki/miscellaneous/analytics) @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php)
- [Master of [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) @ Illinois Institute of Technology](https://www.iit.edu/academics/programs/data-science-mas)
- [Master of Applied [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science)
- [Master [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) and [Artificial Intelligence](/@harrisonqian/awesome/wiki/theory/artificial-intelligence) @ Eindhoven University of Technology](https://www.tue.nl/en/education/graduate-school/master-data-science-and-artificial-intelligence/)
- [Master's Degree in [Data Science](/@harrisonqian/awesome/wiki/programming-languages/data-science) and Computer Engineering @ University of Granada](https://masteres.ugr.es/datcom/)

## The Data Science Toolbox
**[`^        back to top        ^`](#awesome-data-science)**

This section is a collection of packages, tools, algorithms, and other useful items in the data science world.

### Algorithms
**[`^        back to top        ^`](#awesome-data-science)**

These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.

#### Three kinds of Machine Learning Systems

- Based on training with human supervision
- Based on [learning](/@harrisonqian/awesome/wiki/programming-languages/learning) incrementally on fly
- Based on data points comparison and pattern detection

### Comparison
- [datacompy](https://github.com/capitalone/datacompy) - DataComPy is a package to compare two Pandas DataFrames.

#### Supervised Learning

- [Regression](https://en.wikipedia.org/wiki/Regression)
- [Linear Regression](https://en.wikipedia.org/wiki/Linear_regression)
- [Ordinary Least Squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
- [Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression)
- [Stepwise Regression](https://en.wikipedia.org/wiki/Stepwise_regression)
- [Multivariate Adaptive Regression Splines](https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_spline)
- [Softmax Regression](https://d2l.ai/chapter_linear-classification/softmax-regression.html)
- [Locally Estimated Scatterplot Smoothing](https://en.wikipedia.org/wiki/Local_regression)
- Classification
  - [k-nearest neighbor](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
  - [Support Vector Machines](https://en.wikipedia.org/wiki/Support_vector_machine)
  - [Decision Trees](https://en.wikipedia.org/wiki/Decision_tree)
  - [ID3 algorithm](https://en.wikipedia.org/wiki/ID3_algorithm)
  - [C4.5 algorithm](https://en.wikipedia.org/wiki/C4.5_algorithm)
- [Ensemble Learning](https://scikit-learn.org/stable/modules/ensemble.html)
  - [Boosting](https://en.wikipedia.org/wiki/Boosting_(machine_learning))
  - [Stacking](https://machinelearningmastery.com/stacking-ensemble-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-with-python)
  - [Bagging](https://en.wikipedia.org/wiki/Bootstrap_aggregating)
  - [Random Forest](https://en.wikipedia.org/wiki/Random_forest)
  - [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)

#### Unsupervised Learning
- [Clustering](https://scikit-learn.org/stable/modules/clustering.html#clustering)
  - [Hierchical clustering](https://scikit-learn.org/stable/modules/clustering.html#hierarchical-clustering)
  - [k-means](https://scikit-learn.org/stable/modules/clustering.html#k-means)
  - [Density-based clustering](https://scikit-learn.org/stable/modules/clustering.html#dbscan)
  - [Fuzzy clustering](https://en.wikipedia.org/wiki/Fuzzy_clustering)
  - [Mixture models](https://en.wikipedia.org/wiki/Mixture_model)
- [Dimension Reduction](https://en.wikipedia.org/wiki/Dimensionality_reduction)
  - [Principal Component Analysis (PCA)](https://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)
  - [t-SNE; t-distributed Stochastic Neighbor Embedding](https://scikit-learn.org/stable/modules/manifold.html#t-distributed-stochastic-neighbor-embedding-tsne)
  - [Factor Analysis](https://scikit-learn.org/stable/modules/decomposition.html#factor-analysis)
  - [Latent Dirichlet Allocation (LDA)](https://scikit-learn.org/stable/modules/decomposition.html#latent-dirichlet-allocation-lda)
- [Neural Networks](https://en.wikipedia.org/wiki/Neural_network)
- [Self-organizing map](https://en.wikipedia.org/wiki/Self-organizing_map)
- [Adaptive resonance theory](https://en.wikipedia.org/wiki/Adaptive_resonance_theory)
- [Hidden Markov Models (HMM)](https://en.wikipedia.org/wiki/Hidden_Markov_model)

#### Semi-Supervised Learning

- S3VM
- [Clustering](https://en.wikipedia.org/wiki/Weak_supervision#Cluster_assumption)
- [Generative models](https://en.wikipedia.org/wiki/Weak_supervision#Generative_models)
- [Low-density separation](https://en.wikipedia.org/wiki/Weak_supervision#Low-density_separation)
- [Laplacian regularization](https://en.wikipedia.org/wiki/Weak_supervision#Laplacian_regularization)
- [Heuristic approaches](https://en.wikipedia.org/wiki/Weak_supervision#Heuristic_approaches)

#### Reinforcement Learning

- [Q Learning](https://en.wikipedia.org/wiki/Q-learning)
- [SARSA (State-Action-Reward-State-Action) algorithm](https://en.wikipedia.org/wiki/State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action)
- [Temporal difference learning](https://en.wikipedia.org/wiki/Temporal_difference_learning#:~:text=Temporal%20difference%20(TD)%20learning%20refers,estimate%20of%20the%20value%20function.)

#### Data Mining Algorithms

- [C4.5](https://en.wikipedia.org/wiki/C4.5_algorithm)
- [k-Means](https://en.wikipedia.org/wiki/K-means_clustering)
- [SVM (Support Vector Machine)](https://en.wikipedia.org/wiki/Support_vector_machine)
- [Apriori](https://en.wikipedia.org/wiki/Apriori_algorithm)
- [EM (Expectation-Maximization)](https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
- [PageRank](https://en.wikipedia.org/wiki/PageRank)
- [AdaBoost](https://en.wikipedia.org/wiki/AdaBoost)
- [KNN (K-Nearest Neighbors)](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm)
- [Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
- [CART (Classification and Regression Trees)](https://en.wikipedia.org/wiki/Decision_tree_learning)
#### Modern Data Mining Algorithms

- [XGBoost (Extreme Gradient Boosting)](https://en.wikipedia.org/wiki/XGBoost)
- [LightGBM (Light Gradient Boosting Machine)](https://en.wikipedia.org/wiki/LightGBM)
- [CatBoost](https://catboost.ai/)
- [HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise)](https://en.wikipedia.org/wiki/DBSCAN#HDBSCAN)
- [FP-Growth (Frequent Pattern Growth Algorithm)](https://en.wikipedia.org/wiki/Association_rule_learning#FP-growth_algorithm)
- [Isolation Forest](https://en.wikipedia.org/wiki/Isolation_forest)
- [Deep Embedded Clustering (DEC)](https://arxiv.org/abs/1511.06335)
- [TPU (Top-k Periodic and High-Utility Patterns)](https://arxiv.org/abs/2509.15732)
- [Context-Aware Rule Mining (Transformer-Based Framework)](https://arxiv.org/abs/2503.11125)

#### Deep Learning architectures

- [Multilayer Perceptron](https://en.wikipedia.org/wiki/Multilayer_perceptron)
- [Convolutional Neural Network (CNN)](https://en.wikipedia.org/wiki/Convolutional_neural_network)
- [Recurrent Neural Network (RNN)](https://en.wikipedia.org/wiki/Recurrent_neural_network)
- [Boltzmann Machines](https://en.wikipedia.org/wiki/Boltzmann_machine)
- [Autoencoder](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/tutorials/generative/autoencoder)
- [Generative Adversarial Network (GAN)](https://developers.google.com/machine-learning/gan/gan_structure)
- [Self-Organized Maps](https://en.wikipedia.org/wiki/Self-organizing_map)
- [Transformer](https://www.[tensorflow](/@harrisonqian/awesome/wiki/computer-science/tensorflow).org/text/tutorials/transformer)
- [Conditional Random Field (CRF)](https://towardsdatascience.com/conditional-random-fields-explained-e5b8256da776)
- [ML System Designs)](https://www.evidentlyai.com/ml-system-design)

### General Machine Learning Packages
**[`^        back to top        ^`](#awesome-data-science)**

* [scikit-learn](https://scikit-learn.org/)
* [scikit-multilearn](https://github.com/scikit-multilearn/scikit-multilearn)
* [sklearn-expertsys](https://github.com/tmadl/sklearn-expertsys)
* [scikit-feature](https://github.com/jundongl/scikit-feature)
* [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate)
* [seqlearn](https://github.com/larsmans/seqlearn)
* [sklearn-bayes](https://github.com/AmazaspShumik/sklearn-bayes)
* [sklearn-crfsuite](https://github.com/TeamHG-Memex/sklearn-crfsuite)
* [sklearn-deap](https://github.com/rsteca/sklearn-deap)
* [sigopt_sklearn](https://github.com/sigopt/sigopt-sklearn)
* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation)
* [scikit-image](https://github.com/scikit-image/scikit-image)
* [scikit-opt](https://github.com/guofei9987/scikit-opt)
* [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs)
* [feature-engine](https://feature-engine.trainindata.com/)
* [pystruct](https://github.com/pystruct/pystruct)
* [Shogun](https://www.shogun-toolbox.org/)
* [xLearn](https://github.com/aksnzhy/xlearn)
* [cuML](https://github.com/rapidsai/cuml)
* [causalml](https://github.com/uber/causalml)
* [mlpack](https://github.com/mlpack/mlpack)
* [MLxtend](https://github.com/rasbt/mlxtend)
* [modAL](https://github.com/modAL-python/modAL)
* [Sparkit-learn](https://github.com/lensacom/sparkit-learn)
* [hyperlearn](https://github.com/danielhanchen/hyperlearn)
* [dlib](https://github.com/davisking/dlib)
* [imodels](https://github.com/csinva/imodels)
* [jSciPy](https://github.com/hissain/jscipy) - A [Java](/@harrisonqian/awesome/wiki/programming-languages/java) port of SciPy's signal processing module, offering filters, transformations, and other [scientific computing](/@harrisonqian/awesome/wiki/miscellaneous/scientific-computing) utilities.
* [RuleFit](https://github.com/christophM/rulefit)
* [pyGAM](https://github.com/dswah/pyGAM)
* [Deepchecks](https://github.com/deepchecks/deepchecks)
* [scikit-survival](https://scikit-survival.readthedocs.io/en/stable)
* [interpretable](https://pypi.org/project/interpretable)
* [XGBoost](https://github.com/dmlc/xgboost)
* [LightGBM](https://github.com/microsoft/LightGBM)
* [CatBoost](https://github.com/catboost/catboost)
* [PerpetualBooster](https://github.com/perpetual-ml/perpetual)
* [JAX](https://github.com/google/jax)

### Deep Learning Packages

#### PyTorch Ecosystem
* [PyTorch](https://github.com/pytorch/pytorch)
* [torchvision](https://github.com/pytorch/vision)
* [torchtext](https://github.com/pytorch/text)
* [torchaudio](https://github.com/pytorch/audio)
* [ignite](https://github.com/pytorch/ignite)
* [PyTorchNet](https://github.com/pytorch/tnt)
* [PyToune](https://github.com/GRAAL-Research/poutyne)
* [skorch](https://github.com/skorch-dev/skorch)
* [PyVarInf](https://github.com/ctallec/pyvarinf)
* [pytorch_geometric](https://github.com/pyg-team/pytorch_geometric)
* [GPyTorch](https://github.com/cornellius-gp/gpytorch)
* [pyro](https://github.com/pyro-ppl/pyro)
* [Catalyst](https://github.com/catalyst-team/catalyst)
* [pytorch_tabular](https://github.com/manujosephv/pytorch_tabular)
* [Yolov3](https://github.com/ultralytics/yolov3)
* [Yolov5](https://github.com/ultralytics/yolov5)
* [Yolov8](https://github.com/ultralytics/ultralytics)

#### TensorFlow Ecosystem
* [TensorFlow](https://github.com/tensorflow/tensorflow)
* [TensorLayer](https://github.com/tensorlayer/TensorLayer)
* [TFLearn](https://github.com/tflearn/tflearn)
* [Sonnet](https://github.com/deepmind/sonnet)
* [tensorpack](https://github.com/tensorpack/tensorpack)
* [TRFL](https://github.com/deepmind/trfl)
* [Polyaxon](https://github.com/polyaxon/polyaxon)
* [NeuPy](https://github.com/itdxer/neupy)
* [tfdeploy](https://github.com/riga/tfdeploy)
* [tensorflow-upstream](https://github.com/ROCmSoftwarePlatform/tensorflow-upstream)
* [TensorFlow Fold](https://github.com/tensorflow/fold)
* [tensorlm](https://github.com/batzner/tensorlm)
* [TensorLight](https://github.com/bsautermeister/tensorlight)
* [Mesh TensorFlow](https://github.com/tensorflow/mesh)
* [Ludwig](https://github.com/ludwig-ai/ludwig)
* [TF-Agents](https://github.com/tensorflow/agents)
* [TensorForce](https://github.com/tensorforce/tensorforce)

#### Keras Ecosystem

* [Keras](https://keras.io)
* [keras-contrib](https://github.com/keras-team/keras-contrib)
* [Hyperas](https://github.com/maxpumperla/hyperas)
* [Elephas](https://github.com/maxpumperla/elephas)
* [Hera](https://github.com/keplr-io/hera)
* [Spektral](https://github.com/danielegrattarola/spektral)
* [qkeras](https://github.com/google/qkeras)
* [keras-rl](https://github.com/keras-rl/keras-rl)
* [Talos](https://github.com/autonomio/talos)

#### Visualization Tools
**[`^        back to top        ^`](#awesome-data-science)**

- [altair](https://altair-viz.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/)
- [amcharts](https://www.amcharts.com/)
- [anychart](https://www.anychart.com/)
- [bokeh](https://bokeh.org/)
- [Comet](https://www.comet.com/site/products/ml-experiment-tracking/?utm_source=[awesome](/@harrisonqian/awesome/wiki/miscellaneous/awesome)-datascience)
- [slemma](https://slemma.com/)
- [cartodb](https://cartodb.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/odyssey.js/)
- [Cube](https://square.[github](/@harrisonqian/awesome/wiki/development-environment/github).io/cube/)
- [d3plus](https://d3plus.org/)
- [Data-Driven Documents(D3js)](https://d3js.org/)
- [dygraphs](https://dygraphs.com/)
- [exhibit](https://www.simile-widgets.org/exhibit/)
- [gephi](https://gephi.org/)
- [ggplot2](https://ggplot2.tidyverse.org/)
- [Glue](http://docs.glueviz.org/en/latest/index.html)
- [Google Chart Gallery](https://developers.google.com/chart/interactive/docs/gallery)
- [Highcharts](https://www.highcharts.com/)
- [import.io](https://www.import.io/)
- [Matplotlib](https://matplotlib.org/)
- [nvd3](https://nvd3.org/)
- [Netron](https://github.com/lutzroeder/netron)
- [Openrefine](https://openrefine.org/)
- [plot.ly](https://plot.ly/)
- [raw](https://rawgraphs.io)
- [Resseract Lite](https://github.com/abistarun/resseract-lite)
- [Seaborn](https://seaborn.pydata.org/)
- [techanjs](https://techanjs.org/)
- [Timeline](https://timeline.knightlab.com/)
- [variancecharts](https://variancecharts.com/index.html)
- [vida](https://vida.io/)
- [vizzu](https://github.com/vizzuhq/vizzu-lib)
- [Wrangler](http://vis.stanford.edu/wrangler/)
- [r2d3](http://www.r2d3.us/visual-intro-to-machine-[learning](/@harrisonqian/awesome/wiki/programming-languages/learning)-part-1/)
- [NetworkX](https://networkx.org/)

---
*truncated — [full list on GitHub](https://github.com/academic/awesome-datascience)*
