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?"
$ brew tap academic/tap
$ brew install academic
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.
| Link | Preview |
|---|---|
| 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 | _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? _ |
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!
If you're just starting out, here's a simple recommended path:
This section contains agent frameworks and tools that are useful for data science workflows.
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.
Agent framework for chatting with data, turning natural language into SQL, transformation pipelines and visualizations. Outputs are declarative specs that can be inspected, edited, reopened in a notebook or composed into a dashboard.
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.
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](https://github.com/Deesmo/Arch-AI-Tools)
Search engine for AI agents that indexes 9,000+ AI tools and APIs, scoring each on agentic readiness (llms.txt, OpenAPI, MCP, ai-plugin.json). REST API and MCP server for programmatic tool discovery. [GitHub](https://github.com/unitedideas/nothumansearch)
AI crypto trading framework using LightGBM + XGBoost ensemble with 72 ML features. 70.9% walk-forward validated accuracy on out-of-sample data. Supports Bybit and Binance. MIT licensed, available on [PyPI](https://pypi.org/project/deepalpha-bot/).
Local AI agent for generating publication-ready scientific papers with real arXiv citations, IMRaD structure, and tribunal scoring. Runs 100% offline via Ollama with 4B-9B models. MIT licensed. [HuggingFace](https://huggingface.co/Agnuxo/CAJAL-9B-P2PCLAW)
Open-source LLM and agent evaluation framework with 50+ metrics, LLM-as-Judge augmentation, and guardrail scanners (jailbreak, PII, prompt-injection). Useful for scoring RAG outputs, agent trajectories, and function-calling behavior in data-science workflows.
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](https://github.com/connerlambden/bgpt-mcp)
Open-source Python library and MCP server to benchmark document chunking strategies for RAG, score retrieval quality, and recommend configurations for a corpus.
Daily-updated skill and CLI for deterministic retrieval across arXiv, PubMed/PMC, and supported US policy corpora.
x402 payment gateway with 23 Research & Reference endpoints for AI agents: Wikipedia, arXiv, PubMed, Wikidata, academic citation lookup, entity extraction, and more. Pay-per-call in USDC on Base & Solana — no API keys or subscriptions. Also serves 150+ endpoints across 39 categories including geospatial, AI inference, DeFi, and compute. [GitHub](https://github.com/plagtech)
AI literature search, document translation, and deep-research workspace for researchers.
Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.
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.
A weekly data project aimed at the R ecosystem.
Free cross-platform search engine indexing 50,000+ tutorials from Udemy, Skillshare, Pluralsight, and other major learning platforms across 45+ categories.
Interactive calculator that visualizes the step-by-step manual math behind machine learning algorithms for exam prep.
A developer handbook on designing and building effective AI agents.
A straightforward method for training your LLM, from downloading data to generating text.
Open Source Society University
Roadmap to becoming an Artificial Intelligence Expert
Convex Optimization (basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory...)
Introduction to machine learning covering basic theory, algorithms and applications
Learn about Data Science, Machine Learning, Python etc
Learn how to monitor and root-cause production ML issues.
Free Course and Certification for building an end-to-end machine using W&B
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.
Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
A hands-on course to train and deploy a serverless API that predicts crypto prices.
Learn to build modern software with LLMs using the newest tools and techniques in the field.
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.
Free resources and learn what data science is and how it’s used in different industries.
A free video series by Andrej Karpathy covering neural networks from scratch — backpropagation, makemore, GPT, and more.
Linear Algebra course by Gilbert Strang
course material on text-mining / corpus-linguistics *in German* funded by the federal state of North Rhine-Westphalia
course material: programming in python *in German* for digital humanities - funded by the federal state of North Rhine-Westphalia
This section is a collection of packages, tools, algorithms, and other useful items in the data science world.
These are some Machine Learning and Data Mining algorithms and models help you to understand your data and derive meaning from it.
DataComPy is a package to compare two Pandas DataFrames.
Free online meta-analysis platform with 11 interactive D3.js statistical charts (forest plot, funnel plot, Galbraith, L'Abbé, Baujat, etc.), 5 effect size measures, AI literature screening, and publication-ready report export. [github.com](https://github.com/TerryFYL/metareview)
Interactive notebook-based tool to visualize the forward pass of any PyTorch model.
| Link | Description |
|---|---|
| The Data Science Lifecycle Process | The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo |
| Data Science Lifecycle Template Repo | Template repository for data science lifecycle project |
| TabGAN | Synthetic tabular data generation using GANs, Diffusion Models, and LLMs with adversarial filtering and privacy metrics. |
| RexMex | A general purpose recommender metrics library for fair evaluation. |
| ChemicalX | A PyTorch based deep learning library for drug pair scoring. |
| FileShot.io | Secure zero-knowledge encrypted file sharing (AES-256-GCM in-browser). No account required, MIT licensed, self-hostable, optional link expiry. |
| CorpusExplorer | Software for corpus linguists and text/data mining enthusiasts. Build your own corpora in over 60 languages. Use over 50 tools/visualizations. |
| PyTorch Geometric Temporal | Representation learning on dynamic graphs. |
| Little Ball of Fur | A graph sampling library for NetworkX with a Scikit-Learn like API. |
| Karate Club | An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API. |
| ML Workspace | All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code) |
| xonsh shell | A Python-powered shell that enables integration, management and orchestration of data science libraries mostly written in Python, allowing you to build pipelines, code and command-based workflows. It can also be used as a kernel for Jupyter Notebook. |
| Neptune.ai | Community-friendly platform supporting data scientists in creating and sharing machine learning models. Neptune facilitates teamwork, infrastructure management, models comparison and reproducibility. |
| steppy | Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces very simple interface that enables clean machine learning pipeline design. |
| steppy-toolkit | Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective. |
| Datalab from Google | easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively. |
| Hortonworks Sandbox | is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials. |
| R | is a free software environment for statistical computing and graphics. |
| Tidyverse | is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. |
| RStudio | IDE – powerful user interface for R. It’s free and open source, and works on Windows, Mac, and Linux. |
| Python - Pandas - Anaconda | Completely free enterprise-ready Python distribution for large-scale data processing, predictive analytics, and scientific computing |
| Pandas GUI | Pandas GUI |
| NuriStat | Free open-source SPSS alternative — menu-driven desktop statistics (t-tests, ANOVA, regression, survival analysis, ROC) with SPSS .sav import/export |
| Polars | Fast DataFrame library for Rust and Python, designed as a faster alternative to Pandas |
| CiteMe | free academic citation generator with a built-in reference checker that flags fabricated or hallucinated references. Searches 11+ scholarly databases (OpenAlex, PubMed, Semantic Scholar, CrossRef, SciELO), formats 40+ citation styles, and offers a public API. No sign-up; available in English, Spanish, Portuguese, French, and German. |
| Scikit-Learn | Machine Learning in Python |
| NumPy | NumPy is fundamental for scientific computing with Python. It supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. |
| Vaex | Vaex is a Python library that allows you to visualize large datasets and calculate statistics at high speeds. |
| SciPy | SciPy works with NumPy arrays and provides efficient routines for numerical integration and optimization. |
| Data Science Toolbox | Coursera Course |
| Data Science Toolbox | Blog |
| Wolfram Data Science Platform | Take numerical, textual, image, GIS or other data and give it the Wolfram treatment, carrying out a full spectrum of data science analysis and visualization and automatically generate rich interactive reports—all powered by the revolutionary knowledge-based Wolfram Language. |
| Datadog | Solutions, code, and devops for high-scale data science. |
| Variance | Build powerful data visualizations for the web without writing JavaScript |
| Kite Development Kit | The Kite Software Development Kit (Apache License, Version 2.0), or Kite for short, is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem. |
| Domino Data Labs | Run, scale, share, and deploy your models — without any infrastructure or setup. |
| Apache Flink | A platform for efficient, distributed, general-purpose data processing. |
| Apache Hama | Apache Hama is an Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. |
| Weka | Weka is a collection of machine learning algorithms for data mining tasks. |
| Octave | GNU Octave is a high-level interpreted language, primarily intended for numerical computations.(Free Matlab) |
| Apache Spark | Lightning-fast cluster computing |
| Hydrosphere Mist | a service for exposing Apache Spark analytics jobs and machine learning models as realtime, batch or reactive web services. |
| Data Mechanics | A data science and engineering platform making Apache Spark more developer-friendly and cost-effective. |
| Caffe | Deep Learning Framework |
| Torch | A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT |
| Nervana's python based Deep Learning Framework | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware. |
| Skale | High performance distributed data processing in NodeJS |
| Aerosolve | A machine learning package built for humans. |
| Intel framework | Intel® Deep Learning Framework |
| Datawrapper | An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at github.com |
| Tensor Flow | TensorFlow is an Open Source Software Library for Machine Intelligence |
| Natural Language Toolkit | An introductory yet powerful toolkit for natural language processing and classification |
| FunASR | Industrial-grade speech recognition toolkit supporting 50+ languages with built-in VAD, punctuation, speaker diarization, and emotion detection. OpenAI-compatible API server included. |
| Annotation Lab | Free End-to-End No-Code platform for text annotation and DL model training/tuning. Out-of-the-box support for Named Entity Recognition, Classification, Relation extraction and Assertion Status Spark NLP models. Unlimited support for users, teams, projects, documents. |
| nlp-toolkit for node.js | This module covers some basic nlp principles and implementations. The main focus is performance. When we deal with sample or training data in nlp, we quickly run out of memory. Therefore every implementation in this module is written as stream to only hold that data in memory that is currently processed at any step. |
| Julia | high-level, high-performance dynamic programming language for technical computing |
| IJulia | a Julia-language backend combined with the Jupyter interactive environment |
| Apache Zeppelin | Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more |
| Featuretools | An open source framework for automated feature engineering written in python |
| Optimus | Cleansing, pre-processing, feature engineering, exploratory data analysis and easy ML with PySpark backend. |
| Albumentations | А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, and detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops. |
| DVC | An open-source data science version control system. It helps track, organize and make data science projects reproducible. In its very basic scenario it helps version control and share large data and model files. |
| Lambdo | is a workflow engine that significantly simplifies data analysis by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population and column evaluation. |
| Feast | A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving. |
| Polyaxon | A platform for reproducible and scalable machine learning and deep learning. |
| UBIAI | Easy-to-use text annotation tool for teams with most comprehensive auto-annotation features. Supports NER, relations and document classification as well as OCR annotation for invoice labeling |
| Trains | Auto-Magical Experiment Manager, Version Control & DevOps for AI |
| Hopsworks | Open-source data-intensive machine learning platform with a feature store. Ingest and manage features for both online (MySQL Cluster) and offline (Apache Hive) access, train and serve models at scale. |
| MindsDB | MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code. |
| Lightwood | A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with an objective to build predictive models with one line of code. |
| AWS Data Wrangler | An open-source Python package that extends the power of Pandas library to AWS connecting DataFrames and AWS data related services (Amazon Redshift, AWS Glue, Amazon Athena, Amazon EMR, etc). |
| Amazon Rekognition | AWS Rekognition is a service that lets developers working with Amazon Web Services add image analysis to their applications. Catalog assets, automate workflows, and extract meaning from your media and applications. |
| Amazon Textract | Automatically extract printed text, handwriting, and data from any document. |
| Amazon Lookout for Vision | Spot product defects using computer vision to automate quality inspection. Identify missing product components, vehicle and structure damage, and irregularities for comprehensive quality control. |
| Amazon CodeGuru | Automate code reviews and optimize application performance with ML-powered recommendations. |
| CML | An open source toolkit for using continuous integration in data science projects. Automatically train and test models in production-like environments with GitHub Actions & GitLab CI, and autogenerate visual reports on pull/merge requests. |
| Dask | An open source Python library to painlessly transition your analytics code to distributed computing systems (Big Data) |
| DuckDB | An in-process SQL OLAP database management system |
| Statsmodels | A Python-based inferential statistics, hypothesis testing and regression framework |
| Gensim | An open-source library for topic modeling of natural language text |
| spaCy | A performant natural language processing toolkit |
| Grid Studio | Grid studio is a web-based spreadsheet application with full integration of the Python programming language. |
| Python Data Science Handbook | Python Data Science Handbook: full text in Jupyter Notebooks |
| Shapley | A data-driven framework to quantify the value of classifiers in a machine learning ensemble. |
| DAGsHub | A platform built on open source tools for data, model and pipeline management. |
| Deepnote | A new kind of data science notebook. Jupyter-compatible, with real-time collaboration and running in the cloud. |
| Valohai | An MLOps platform that handles machine orchestration, automatic reproducibility and deployment. |
| PyMC3 | A Python Library for Probabalistic Programming (Bayesian Inference and Machine Learning) |
| PyStan | Python interface to Stan (Bayesian inference and modeling) |
| hmmlearn | Unsupervised learning and inference of Hidden Markov Models |
| Chaos Genius | ML powered analytics engine for outlier/anomaly detection and root cause analysis |
| Nimblebox | A full-stack MLOps platform designed to help data scientists and machine learning practitioners around the world discover, create, and launch multi-cloud apps from their web browser. |
| Towhee | A Python library that helps you encode your unstructured data into embeddings. |
| LineaPy | Ever been frustrated with cleaning up long, messy Jupyter notebooks? With LineaPy, an open source Python library, it takes as little as two lines of code to transform messy development code into production pipelines. |
| envd | 🏕️ machine learning development environment for data science and AI/ML engineering teams |
| Explore Data Science Libraries | A search engine 🔎 tool to discover & find a curated list of popular & new libraries, top authors, trending project kits, discussions, tutorials & learning resources |
| MLEM | 🐶 Version and deploy your ML models following GitOps principles |
| MLflow | MLOps framework for managing ML models across their full lifecycle |
| cleanlab | Python library for data-centric AI and automatically detecting various issues in ML datasets |
| AutoGluon | AutoML to easily produce accurate predictions for image, text, tabular, time-series, and multi-modal data |
| Arize AI | Arize AI community tier observability tool for monitoring machine learning models in production and root-causing issues such as data quality and performance drift. |
| Aureo.io | Aureo.io is a low-code platform that focuses on building artificial intelligence. It provides users with the capability to create pipelines, automations and integrate them with artificial intelligence models – all with their basic data. |
| ERD Lab | Free cloud based entity relationship diagram (ERD) tool made for developers. |
| Arize-Phoenix | MLOps in a notebook - uncover insights, surface problems, monitor, and fine tune your models. |
| Comet | An MLOps platform with experiment tracking, model production management, a model registry, and full data lineage to support your ML workflow from training straight through to production. |
| Opik | Evaluate, test, and ship LLM applications across your dev and production lifecycles. |
| Synthical | AI-powered collaborative environment for research. Find relevant papers, create collections to manage bibliography, and summarize content — all in one place |
| teeplot | Workflow tool to automatically organize data visualization output |
| Streamlit | App framework for Machine Learning and Data Science projects |
| Gradio | Create customizable UI components around machine learning models |
| Weights & Biases | Experiment tracking, dataset versioning, and model management |
| DVC | Open-source version control system for machine learning projects |
| Optuna | Automatic hyperparameter optimization software framework |
| Ray Tune | Scalable hyperparameter tuning library |
| Apache Airflow | Platform to programmatically author, schedule, and monitor workflows |
| Prefect | Workflow management system for modern data stacks |
| Kedro | Open-source Python framework for creating reproducible, maintainable data science code |
| Hamilton | Lightweight library to author and manage reliable data transformations |
| SHAP | Game theoretic approach to explain the output of any machine learning model |
| InterpretML | InterpretML implements the Explainable Boosting Machine (EBM), a modern, fully interpretable machine learning model based on Generalized Additive Models (GAMs). This open-source package also provides visualization tools for EBMs, other glass-box models, and black-box explanations |
| LIME | Explaining the predictions of any machine learning classifier |
| flyte | Workflow automation platform for machine learning |
| dbt | Data build tool |
| zasper | Supercharged IDE for Data Science |
| skrub | A Python library to ease preprocessing and feature engineering for tabular machine learning |
| Codeflash | Ship Blazing-Fast Python Code — Every Time |
| Hugging Face | Popular open platform for sharing ML models, datasets, and collaborating on NLP and generative AI projects. |
| Chinese-Elite | An open-source project that automatically maps relationship networks by parsing public data using LLMs and visualizes it as an interactive graph. |
| Desbordante | An open-source data profiler specifically focused on discovery and validation of complex patterns, such as numerical association rules, differential dependencies, denial constraints, and more. |
| dna-claude-analysis | Personal genome analysis toolkit with Python scripts analyzing raw DNA data across 17 categories (health risks, ancestry, pharmacogenomics, nutrition, psychology, and more) and generating a terminal-style single-page HTML visualization. |
| RunMat | Fast MATLAB-syntax runtime with automatic CPU/GPU execution and fused array kernels. |
| Turbostream | A terminal UI for experimenting with custom rule engines and selective LLM analysis on real-time data streams, without worrying about streaming infra or backpressure. |
| WFGY ProblemMap | Open source “failure atlas” of 16 recurring issues in LLM and RAG pipelines, with observable symptoms and suggested fixes for data science teams. |
| Deploybase | Track real-time GPU and LLM pricing across all cloud and inference providers. |
| DeepAnalyze | An agentic LLM for autonomous data science, which can autonomously complete a wide range of data science tasks without human intervention. |
| Disco | Superhuman exploratory data analysis. Finds the feature interactions and subgroup effects in tabular data that LLMs and manual exploration miss — with p-values, effect sizes, and literature citations. Free for public data. |
| AI for Database | Chat with your database in natural language — no SQL needed. Get instant insights, build self-refreshing dashboards, and trigger automated workflows based on database changes. |
| Crypto Pump Scanner | AI-powered cryptocurrency trading bot with LSTM neural network (84.6% accuracy). Real-time pump detection, walk-forward validated models, multi-exchange support (Bybit, Binance, OKX, Gate.io). Open source. |
| Future AGI | Open-source platform to simulate, evaluate, trace, guardrail, route, and optimize LLM and AI agent apps in one feedback loop, so agents don't just get monitored, they self-improve. Self-hostable. Apache-2.0. |
This section includes some additional reading material, channels to watch, and talks to listen to.
Early Access
free eBook sampler
free eBook sampler
Early access
free e-book comprehended by an online course
Early access
Free Download
Free Download
Free Download
Free Download
Convex Optimization book by Stephen Boyd - Free Download
Early Access
Early access
Early Access
Early Access
Early Access
Early Access
Early Access
Early Access
Free GitHub version
Early Access
Early Access
Download Page
Free html page
German book about applied data science
International Conference on Machine Learning
The Genetic and Evolutionary Computation Conference (GECCO)
an international journal devoted to applications of statistical methods at large
Like Hacker News, but for data
Data Science related publications on medium
AI industry research and analysis with papers on AI pricing, enterprise adoption, and evaluation frameworks.
Curated AI intelligence briefing from industry leaders covering models, funding, policy, and applications. 3x/week since 2017, 40K+ subscribers.
Wes McKinney Archives.
Mining The Social Web.
Greg Reda Personal Blog
Recurse Center alumna
Personal Web Page
Personal Web Page
Personal Web Page
Personal Web Page
Personal Blog
Personal Blog
AllThings Data Sciene
Tech Blog on Master Data Management And Every Buzz Surrounding It
The Open Source Data Science Masters
Data Science Questions and Answers from experts
Personal Blog
Weekly News Blog
Data Science Blog
R Bloggers
Personal Blog
Magnus Notitia
Thoughts on Statistical Computing and Visualization
Learning To Be A Data Scientist
[The File Drawer](https://chris-said.io/) - Chris Said's science blog
Visualization and Statistics
A Machine Learning Craftsmanship Blog
Handbook and recipes for data-driven solutions of real-world problems
A blog on the newly emerging data economy
A blog with resources for data science learners
A full-fledged website about data science and analytics study material.
Focused on Web Analytics.
Data science tutorials for beginners!
Blog for understanding Neural Networks!
Blog for NLP and transfer learning!
Dedicated to clear explanations of machine learning!
Data Science and AI notes
Data Science with Esoteric programming languages
Blog for Evolutionary Algorithms
Review and extract key concepts from academic papers
Data Science notebooks
Data science blog
ML Engineering, MLOps, and the use of ML in startups
Data science blog
ML,DL,Data Science blog
Data Science with Python
ML, DL and Data Science
ML, DL and Data Science
In-depth articles on AI, machine learning, and data science concepts with practical applications.
Educational content on software development, AI, and career growth in tech.
Mlu is developed amazon to help people in ml space you can learn everything from basics here with live diagrams
ML, DL and Data Science - with a focus on text-/data-mining
Data Science Education
| Preview | Description |
|---|---|
| Key differences of a data scientist vs. data engineer | |
| A visual guide to Becoming a Data Scientist in 8 Steps by DataCamp (img) | |
| Mindmap on required skills (img) | |
| Swami Chandrasekaran made a Curriculum via Metro map. | |
| by @kzawadz via twitter | |
| By Data Science Central | |
| Data Science Wars: R vs Python | |
| How to select statistical or machine learning techniques | |
| Choosing the Right Estimator | |
| The Data Science Industry: Who Does What | |
| Data Science |
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| Different Data Science Skills and Roles from Springboard | |
| A simple and friendly way of teaching your non-data scientist/non-statistician colleagues how to avoid mistakes with data. From Geckoboard's Data Literacy Lessons. |
Specific datasets for aircraft and Automatic Dependent Surveillance-Broadcast (ADS-B) sources.
Curated open dataset of 100+ Chinese teas with category, origin, caffeine level, flavor notes, oxidation, and brewing parameters. Available as JSON and CSV.
Lifetime return-on-investment estimates for 29,700 US bachelor's programs across 3,392 colleges, built from FREOPP, IPEDS, and BEA regional price data. 5 CSVs with data dictionary, CC BY 4.0, Zenodo DOI.
Structured dataset tracking 92 AI-attributed workforce reduction events affecting 453,748 workers across 12 countries and 11 sectors. JSON and CSV formats. CC-BY-4.0 licensed.
Public packaging product dataset generated from 1,000 exact-spec SKU records, with downloadable CSV and JSON files for ecommerce fulfillment and warehouse analysis.
The home of the U.S. Government's open data
Navigate the world of public data - Quickly search and analyze billions of public records published by governments, companies and organizations.
Nasdaq Data Link A premier source for financial, economic and alternative datasets.
Free AI-powered tool that scores U.S. congressional STOCK Act trade disclosures by significance. Machine-scored signals from 537 lawmakers's public trade filings.
English dataset of Tokyo crime statistics across 5,078 neighborhoods × 7 years (36,222 records, 2018-2024), sourced from Tokyo Metropolitan Police open data. Includes interactive crime map, safety grading, and cost-of-living index. CC BY licensed.
A 30-metro composite ranking of how much of a $400K household income gets consumed by housing, taxes, childcare, healthcare, and transport. Open methodology, free, no email gate.
Open-data platform for Brazilian crime statistics. Neighborhood-level in Rio Grande do Sul (2.99M incidents across 79,024 neighborhoods, 2022–2025), municipality-level for MG and RJ, plus national PRF highway and DATASUS interpersonal-violence data. Free REST API, CSV/Parquet, daily updates, CC BY 4.0.
Filtered subset of NHTSA Fatality Analysis Reporting System covering 33,898 fatal crashes involving medium and heavy commercial trucks across all 50 US states, 2018-2024. Includes interactive [Vision Zero Report Card](https://accidentlawyerreview.com/research/vision-zero-report-card/) comparing 19 cities, reproducible Python pipeline on [GitHub](https://github.com/MarvinBregiosa/vision-zero-fars), and HuggingFace mirror. Permanent DOI, CC BY 4.0.
Structured reference dataset of 156 peptide and peptide-adjacent compounds, each with a regulatory status bucket, category, route, half-life, molecular weight, CAS number, reference count, and PubChem/DrugBank/Wikidata IDs. CSV and JSON, no login, CC BY 4.0.
contains data sets good for machine learning
provides a variety of data free of charge for uses that are freely available to the general public. Click on a data set below to learn more
Institute for Health Metrics and Evaluation - a catalog of health and demographic datasets from around the world and including IHME results
an open source tool for running arbitrary queries against public data from the Stack Exchange network.
An expanding dataset of historical job postings from Luxembourg from 2020 to today. Free with 250k+ job postings hosted on AWS Data Exchange.
Financial datasets (stock market data, financial statements, sustainability data, and more).
AI-powered multi-market stock analysis with transparent BDE scoring across 73 stocks (US/HK/A-share). EU AI Act Art.50 compliant. MIT license.
Free corpora (over 6 billion tokens) mostly German (both historically and in contemporary German).
CLARIN is a European repository for scientific datasets.
Global Biodiversity Information Facility: 2.4B+ species occurrence records. Free, open API for ecological modeling and ML research.
UN FAO statistics on food production, trade, land use, and emissions for 245+ countries. Free API and bulk download.
Free platform archiving 6B+ animal movement records from GPS and satellite telemetry. Open REST API, useful for spatiotemporal modeling and trajectory ML.
Open structured data on 1.9M+ species, including traits, classification, and media. Free API and bulk downloads for biodiversity and species-classification tasks.
The world's most comprehensive authoritative data source knowledge base. 210+ curated sources from governments, international organizations, and research institutions. MCP integration for AI agents. MIT licensed.
Python package for one-line access to 38 open research datasets from Latin America (health, neuroscience, mental health, economics). pip install latamdata-py.
Free ZIP-level environmental safety data for 42,000+ US ZIP codes: water quality, air quality, PFAS contamination, radon, lead, flood risk, and 11 more verticals. Public REST API, npm/PyPI packages, CC BY 4.0.
Real-time news corpus with structured bias features across 15+ dimensions (3.2M+ articles, 5,000+ sources), live financial market data (stocks, ETFs, crypto) with AI-generated analysis, ML options pricing with probability metrics and full Greeks, historical options chain data for quantitative research; available via MCP server or REST API.
Evidence-graded dietary-supplement dataset covering dosing, bioavailability by form, drug-nutrient interactions, NHANES deficiency prevalence, FDA FAERS adverse-event signals, and cost-per-effective-dose, with every clinical claim citing a PubMed PMID. CC BY 4.0, DOI 10.57967/hf/9356.
500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design
A curated list of data analysis tools, libraries and resources.
A curated list of open-source tools for systematic reviews, meta-analysis, and evidence synthesis.
Job board focused on AI/ML engineering roles with 5,400+ listings and a free REST API.
