Data Science

repo: academic/awesome-datascience
category: Computer Science related: Python · Machine Learning · Public Datasets · Data Engineering · Streaming

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AWESOME DATA SCIENCE

Contributions are welcome - see 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?"

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Table of Contents

• [What is Data Science?](#what-is-data-science)

• Where do I Start?

• Agents

• Training Resources

  • Tutorials
  • Free Courses
  • Massively Open Online Courses
  • Intensive Programs
  • Colleges

• [The Data Science Toolbox](#the-data-science-toolbox)

  • Algorithms
    • Supervised Learning
    • Unsupervised Learning
    • Semi-Supervised Learning
    • Reinforcement Learning
    • Data Mining Algorithms
    • [Deep Learning Architectures](#deep-learning-architectures)
  • [General Machine Learning Packages](#general-machine-learning-packages)
  • Model Evaluation & Monitoring
    • Evidently AI
  • [Deep Learning Packages](#deep-learning-packages)
    • PyTorch Ecosystem
    • TensorFlow Ecosystem
    • Keras Ecosystem
  • Visualization Tools
  • Miscellaneous Tools

• Literature and Media

  • Books
    • Book Deals (Affiliated)
  • Journals, Publications, and Magazines
  • Newsletters
  • Bloggers
  • Presentations
  • Podcasts
  • YouTube Videos & Channels

• Socialize

  • Facebook Accounts
  • Twitter Accounts
  • Telegram Channels
  • Slack Communities
  • GitHub Groups
  • Data Science Competitions

• Fun

  • Infographics
  • Datasets
  • Comics

• [Other Awesome Lists](#other-awesome-lists)

  • Hobby

What is Data Science?

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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 you can find the biggest question for Data Science and hundreds of answers from experts.

Where do I Start?

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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 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 (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 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, one can collect and analyze their data into a convenient table format. Numpy provides very fast tooling for mathematical operations, with a focus on vectors and matrices. Seaborn, itself based on the Matplotlib 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 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 - Production-ready AI agent development kit for Rust with model-agnostic design (Gemini, OpenAI, Anthropic), multiple agent types (LLM, Graph, Workflow), MCP support, and built-in telemetry.

Tools

• Frostbyte MCP - MCP server providing 13 data tools for AI agents: real-time crypto prices, IP geolocation, DNS lookups, web scraping to markdown, code execution, and screenshots. One API key for 40+ services.
• Arch Tools - 61 production-ready AI API tools for data science workflows: code analysis, web scraping, NLP, image generation, crypto data, and search. REST API and MCP protocol support. GitHub

Research & Knowledge Retrieval

• BGPT MCP - MCP server that gives AI agents access to a database of scientific 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

Workflow

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• sim - Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.

Training Resources

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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, Massively Open Online Courses (MOOCs), Intensive Programs, and Colleges.

Tutorials

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

Free Courses

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• Data Scientist with R
• Data Scientist with Python
• [Genetic 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 - Roadmap to becoming an Artificial Intelligence Expert
• 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 - Introduction to machine learning covering basic theory, algorithms and applications
• Kaggle - Learn about Data Science, Machine Learning, Python etc
• ML Observability Fundamentals - Learn how to monitor and root-cause production ML issues.
• Weights & Biases Effective MLOps: Model Development - Free Course and Certification for building an end-to-end machine using W&B
• [Python for 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, and machine learning.
• MLSys-NYU-2022 - Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
• 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 - Learn to build modern software with LLMs using the newest tools and techniques in the field.
• Prompt Engineering for Vision Models - Learn to prompt cutting-edge 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 - Free resources and learn what data science is and how it’s used in different industries.

MOOC's

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• Coursera Introduction to Data Science
• Data Science - 9 Steps Courses, A Specialization on Coursera
• Data Mining - 5 Steps Courses, A Specialization on Coursera
• [Machine Learning – 5 Steps Courses, A Specialization on Coursera](https://www.coursera.org/specializations/machine-learning)
• CS 109 Data Science
• OpenIntro
• CS 171 Visualization
• [Process Mining: Data science in Action](https://www.coursera.org/learn/process-mining)
• Oxford Deep Learning
• [Oxford Deep Learning - video](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu)
• Oxford Machine Learning
• [UBC Machine Learning - video](https://www.cs.ubc.ca/~nando/540-2013/lectures.html)
• Data Science Specialization
• [Coursera Big Data Specialization](https://www.coursera.org/specializations/big-data)
• [Statistical Thinking for Data Science and Analytics by Edx](https://www.edx.org/course/statistical-thinking-for-data-science-and-analytic)
• Cognitive Class AI by IBM
• Udacity - Deep Learning
• Keras in Motion
• Microsoft Professional Program for Data Science
• [COMP3222/COMP6246 - Machine Learning Technologies](https://tdgunes.com/COMP6246-2019Fall/)
• CS 231 - Convolutional Neural Networks for Visual Recognition
• [Coursera Tensorflow in practice](https://www.coursera.org/professional-certificates/tensorflow-in-practice)
• [Coursera Deep Learning Specialization](https://www.coursera.org/specializations/deep-learning)
• [365 Data Science Course](https://365datascience.com/)
• Coursera Natural Language Processing Specialization
• Coursera GAN Specialization
• Codecademy's Data Science
• Linear Algebra - Linear Algebra course by Gilbert Strang
• A 2020 Vision of Linear Algebra (G. Strang)
• [Python for Data Science Foundation Course](https://intellipaat.com/academy/course/python-for-data-science-free-training/)
• Data Science: Statistics & Machine Learning
• [Machine Learning Engineering for Production (MLOps)](https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops)
• Recommender Systems Specialization from University of Minnesota is an intermediate/advanced level specialization focused on Recommender System on the Coursera platform.
• [Stanford Artificial Intelligence Professional Program](https://online.stanford.edu/programs/artificial-intelligence-professional-program)
• Data Scientist with Python
• Programming with Julia
• [Scaler Data Science & Machine Learning Program](https://www.scaler.com/data-science-course/)
• Data Science Skill Tree
• Data Science for Beginners - Learn with AI tutor
• [Machine Learning for Beginners - Learn with AI tutor](https://codekidz.ai/lesson-intro/machine-lear-36abfb)
• Introduction to Data Science -Getting Started with Python for Data Science
• [Google Advanced Data Analytics Certificate](https://grow.google/data-analytics/) – Professional courses in data analysis, statistics, and machine learning fundamentals.
• Maschinelle Sprachgebrauchsanalyse - Grundlagen der Korpuslinguistik - course material on text-mining / corpus-linguistics in German funded by the federal state of North Rhine-Westphalia
• Programmieren für Germanist*innen - course material: programming in python in German for digital humanities - funded by the federal state of North Rhine-Westphalia

Intensive Programs

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• S2DS
• [WorldQuant University Applied Data Science Lab](https://www.wqu.edu/adsl)

Colleges

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• [A list of colleges and universities offering degrees in data science.](https://github.com/ryanswanstrom/awesome-datascience-colleges)
• Data Science Degree @ Berkeley
• Data Science Degree @ UVA
• Data Science Degree @ Wisconsin
• [BS in Data Science & Applications](https://study.iitm.ac.in/ds/)
• MS in Computer Information Systems @ Boston University
• [MS in Business Analytics @ ASU Online](https://asuonline.asu.edu/online-degree-programs/graduate/master-science-business-analytics/)
• [MS in Applied Data Science @ Syracuse](https://ischool.syr.edu/academics/applied-data-science-masters-degree/)
• [M.S. Management & Data Science @ Leuphana](https://www.leuphana.de/en/graduate-school/masters-programmes/management-data-science.html)
• [Master of Data Science @ Melbourne University](https://study.unimelb.edu.au/find/courses/graduate/master-of-data-science/#overview)
• [Msc in 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 @ Queen's University](https://smith.queensu.ca/grad_studies/mma/index.php)
• [Master of Data Science @ Illinois Institute of Technology](https://www.iit.edu/academics/programs/data-science-mas)
• [Master of Applied Data Science @ The University of Michigan](https://www.si.umich.edu/programs/master-applied-data-science)
• [Master Data Science and 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 and Computer Engineering @ University of Granada](https://masteres.ugr.es/datcom/)

The Data Science Toolbox

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

Algorithms

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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 incrementally on fly
• Based on data points comparison and pattern detection

Comparison

• datacompy - DataComPy is a package to compare two Pandas DataFrames.

Supervised Learning

• Regression
• Linear Regression
• Ordinary Least Squares
• Logistic Regression
• Stepwise Regression
• Multivariate Adaptive Regression Splines
• Softmax Regression
• Locally Estimated Scatterplot Smoothing
• Classification
  • k-nearest neighbor
  • Support Vector Machines
  • Decision Trees
  • ID3 algorithm
  • C4.5 algorithm
• Ensemble Learning
  • Boosting
  • Stacking
  • Bagging
  • Random Forest
  • AdaBoost

Unsupervised Learning

• Clustering
  • Hierchical clustering
  • k-means
  • Density-based clustering
  • Fuzzy clustering
  • Mixture models
• Dimension Reduction
  • Principal Component Analysis (PCA)
  • t-SNE; t-distributed Stochastic Neighbor Embedding
  • Factor Analysis
  • Latent Dirichlet Allocation (LDA)
• Neural Networks
• Self-organizing map
• Adaptive resonance theory
• Hidden Markov Models (HMM)

Semi-Supervised Learning

• S3VM
• Clustering
• Generative models
• Low-density separation
• Laplacian regularization
• Heuristic approaches

Reinforcement Learning

• Q Learning
• SARSA (State-Action-Reward-State-Action) algorithm
• Temporal difference learning

Data Mining Algorithms

• C4.5
• k-Means
• SVM (Support Vector Machine)
• Apriori
• EM (Expectation-Maximization)
• PageRank
• AdaBoost
• KNN (K-Nearest Neighbors)
• Naive Bayes
• CART (Classification and Regression Trees)

Modern Data Mining Algorithms

• XGBoost (Extreme Gradient Boosting)
• LightGBM (Light Gradient Boosting Machine)
• CatBoost
• HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise)
• FP-Growth (Frequent Pattern Growth Algorithm)
• Isolation Forest
• Deep Embedded Clustering (DEC)
• TPU (Top-k Periodic and High-Utility Patterns)
• Context-Aware Rule Mining (Transformer-Based Framework)

Deep Learning architectures

• Multilayer Perceptron
• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• Boltzmann Machines
• Autoencoder
• Generative Adversarial Network (GAN)
• Self-Organized Maps
• Transformer
• Conditional Random Field (CRF)
• ML System Designs)

General Machine Learning Packages

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• scikit-learn
• scikit-multilearn
• sklearn-expertsys
• scikit-feature
• scikit-rebate
• seqlearn
• sklearn-bayes
• sklearn-crfsuite
• sklearn-deap
• sigopt_sklearn
• sklearn-evaluation
• scikit-image
• scikit-opt
• scikit-posthocs
• feature-engine
• pystruct
• Shogun
• xLearn
• cuML
• causalml
• mlpack
• MLxtend
• modAL
• Sparkit-learn
• hyperlearn
• dlib
• imodels
• jSciPy - A Java port of SciPy's signal processing module, offering filters, transformations, and other scientific computing utilities.
• RuleFit
• pyGAM
• Deepchecks
• scikit-survival
• interpretable
• XGBoost
• LightGBM
• CatBoost
• PerpetualBooster
• JAX

Deep Learning Packages

PyTorch Ecosystem

• PyTorch
• torchvision
• torchtext
• torchaudio
• ignite
• PyTorchNet
• PyToune
• skorch
• PyVarInf
• pytorch_geometric
• GPyTorch
• pyro
• Catalyst
• pytorch_tabular
• Yolov3
• Yolov5
• Yolov8

TensorFlow Ecosystem

• TensorFlow
• TensorLayer
• TFLearn
• Sonnet
• tensorpack
• TRFL
• Polyaxon
• NeuPy
• tfdeploy
• tensorflow-upstream
• TensorFlow Fold
• tensorlm
• TensorLight
• Mesh TensorFlow
• Ludwig
• TF-Agents
• TensorForce

Keras Ecosystem

• Keras
• keras-contrib
• Hyperas
• Elephas
• Hera
• Spektral
• qkeras
• keras-rl
• Talos

Visualization Tools

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• altair
• amcharts
• anychart
• bokeh
• Comet
• slemma
• cartodb
• Cube
• d3plus
• Data-Driven Documents(D3js)
• dygraphs
• exhibit
• gephi
• ggplot2
• Glue
• Google Chart Gallery
• Highcharts
• import.io
• Matplotlib
• nvd3
• Netron
• Openrefine
• plot.ly
• raw
• Resseract Lite
• Seaborn
• techanjs
• Timeline
• variancecharts
• vida
• vizzu
• Wrangler
• r2d3
• NetworkX

truncated — full list on GitHub