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🚀 500+ curated resources for Data Analysis & Data Science: Python, SQL, Statistics, ML, AI, Visualization, Cheatsheets, Roadmaps, Interview Prep. For beginners and experts

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Awesome Data Analysis

500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more. **📖 For comfortable reading:** Web version **🌱 Want to improve?** or 🌟 Join us in making data analysis more efficient! Maintained with ❤️ ---

📑 Contents

🏆 Awesome Data Science Repositories

Curated collections of high-quality repos for inspiration and learning. - - A curated list of courses, books, tools, and resources for data science. - - Microsoft's data science curriculum. - - Society University's self-study path. - - Carefully curated links for data science resources in one place. - - A collection of articles, videos, and code related to data science. - - Resources for data analysis using Python. ⬆ back to contents ---

🗺️ Roadmaps

Step-by-step guides and skill trees to master data science and analytics. - Data Analyst Roadmap - Structured learning path for analysts. - - Comprehensive roadmap for data science. - - A comprehensive and updated roadmap for learning data science with modern tools and technologies. - - 66-day data analytics learning challenge. - - 8-week program for analysts at all levels. - - Tutorials for the data science roadmap. - - Guide to becoming a data analyst from scratch. ⬆ back to contents ---

🐍 Python

Resources

A collection of resources for learning and mastering Python programming. - - An opinionated list of awesome Python frameworks, libraries, software, and resources. - - A 30-day programming challenge to learn the Python programming language. - Real Python Tutorials - Tutorials on Python from Real Python. - - A curated list of Python resources for data science. - - Full text of the "Python Data Science Handbook" in Jupyter Notebooks. - - 120+ interactive Python coding interview challenges. - - Clean Code concepts adapted for Python. - - A ranked list of awesome Python libraries and tools. - GeeksforGeeks Python - Python tutorial from GeeksforGeeks. - W3Schools Python - A beginner-friendly tutorial and reference for the Python programming language. - - This repository helps you understand Python from scratch. - - Jupyter notebooks and other resources for Think Python by Allen Downey. ⬆ back to contents ---

Data Manipulation with Pandas and Numpy

Tutorials and best practices for working with Pandas and Numpy. - - A curated list of resources for using the Pandas library. - - A collection of data puzzles to practice your Pandas skills. - Pandas Tutor - Visualize Pandas operations step-by-step (perfect for beginners). - - Exercises designed to help you improve your Pandas skills. - - A cookbook with various recipes for using Pandas effectively. - - Materials for following along with Hands-On Data Analysis with Pandas. - - A series focused on writing effective and idiomatic Pandas code. - - An open-access book on vectorization and efficient numerical computing with NumPy. - - A collection of 100 exercises to master the NumPy library for scientific computing. ⬆ back to contents ---

Useful Python Tools for Data Analysis

A collection of Python libraries for efficient data manipulation, cleaning, visualization, validation, and analysis.

Data Processing & Transformation

- - Powerful Python library for data analysis and manipulation with flexible data structures. - - Fundamental package for scientific computing in Python with multidimensional array support. - - Data type correction and automatic DataFrame cleaning. - - High-performance Python library for lazy Out-of-Core DataFrames. - - Multithreaded, vectorized query engine for DataFrames. - - Unified interface for Pandas, Spark, and Dask. - - Fuzzy string matching (Levenshtein distance). - - Extensions for standard Python datetime features. - - Enhanced work with dates and times. - - Alternative to datetime with timezone support. - - Parallel computing for arrays and DataFrames. - - Speeds up Pandas by distributing computations. - - Parallel operations for pandas DataFrames. - - Python tool for automatically cleaning and preparing datasets. - - Add custom methods to Pandas. - - Reads data from various online sources into pandas DataFrames. - - Bridge between Pandas and Scikit-learn. - - A NumPy-compatible array library accelerated by NVIDIA CUDA for high-performance computing. - - A JIT compiler that translates a subset of Python and NumPy code into fast machine code. - - Type stubs for pandas, improves IDE autocompletion. - - ETL tool for data cleaning and transformation. ⬆ back to contents ---

Automated EDA and Visualization Tools

- - Automatic data visualization in 1 line of code. - - Automatic EDA with dataset comparison. - - Automatic DataFrame visualization in Jupyter. - - Data quality profiling & exploratory data analysis. - - Visualize missing data patterns. - - Low-code toolkit for building data visualization apps. - - Visual diagnostic tools for machine learning. - - Create awesome display tables using Python. - - Create beautiful plots of data maps. - - Quickly and accurately render even the largest data. - - Conversational data analysis using LLMs and RAG. - - Jupyter extensions for faster code writing. - - Interactive GUI for data analysis in a browser. - - GUI for viewing and filtering DataFrames. - - Interactive UIs for visual analysis of DataFrames. - - Interactive grid for DataFrames in Jupyter. - - Interactive PivotTable.js tables in Jupyter. ⬆ back to contents ---

Data Quality & Validation

- - Outlier and anomaly detection. - - Outlier, adversarial and drift detection. - - Data validation through declarative schemas. - - Data validation through schemas. - - Data validation using Python type annotations. - - Automate EDA: preprocessing, feature engineering, visualization. - - Data validation and testing. ⬆ back to contents ---

Feature Engineering & Selection

- - Automated feature engineering. - - Feature engineering with Scikit-Learn compatibility. - - Multivariate exploratory data analysis (PCA, CA, MCA). - - Figures out the distribution your data comes from. - - Tool for dimensionality reduction of machine learning datasets. - - Extensive collection of categorical variable encoders. - - Handling imbalanced datasets. ⬆ back to contents ---

Specialized Data Tools

- - A GPU DataFrame library for loading, joining, and aggregating data. - - Generates fake data for testing. - - Generates realistic test data. - - Geocoding addresses and calculating distances. - - Spatial analysis functions. - - Beautiful visualizations of language differences among document types. - - A library for creating and manipulating graphs and networks, with bindings for multiple languages. - - A lightweight pipelining library for Python, particularly useful for saving and loading large NumPy arrays. - - A library that provides an easy interface to read and write a wide range of image data. - - Text preprocessing, representation and visualization. - - Geographic data operations with pandas. - - Network analysis and graph theory. - - Python library to detect the character encoding of text and files. ⬆ back to contents ---

🗃️ SQL & Databases

Resources

SQL tutorials and database design principles. - SQLZoo - SQL Tutorial - Interactive SQL tutorial. - SQL Bolt - Learn SQL - Learn SQL through interactive lessons. - SQL Tutorial - Comprehensive SQL tutorial resource. - SQL Tutorial by W3Schools. - Comprehensive SQL tutorial. - PostgreSQL Tutorial by W3Resource - Tutorial for PostgreSQL. - MySQL Tutorial by W3Resource - Tutorial for MySQL. - MongoDB Tutorial by W3Resource - Tutorial for MongoDB. - EverSQL - AI-powered SQL query optimization and database observability tool. - - Educational resources on database internals, distributed systems, and storage. - - A curated list of awesome PostgreSQL software, libraries, tools and resources. - - A curated list of awesome MySQL software, libraries, tools and resources. - - A curated list of awesome ClickHouse software. - - A curated list of awesome MongoDB resources, libraries, tools, and applications. - - Curated tools, resources, and extensions for DuckDB analytical database. - - A curated list of awesome tools for SQLAlchemy. - - List of tools and techniques for working with relational databases. - AnimateSQL - Interactive tool that visualizes the step-by-step execution of SQL queries. - - Useful SQL techniques and optimizations for data analysis. - Practice Window Functions - Free interactive SQL tutorial site focused on mastering window functions through 80+ hands-on problems with hints and solutions. ⬆ back to contents ---

Databases

Popular database systems for a variety of use cases and data models. - – Embedded, file‑based SQL engine, the most used database in the world. - – Advanced open‑source relational database with extensibility and SQL compliance. - – Widely used relational database management system (Oracle’s community version). - – Community developed fork of MySQL with enhanced features. - – In‑process analytical database designed for fast OLAP queries. - – Columnar database for real‑time analytics on large datasets. - - A mature MPP database evolved from Greenplum with a newer PostgreSQL kernel. - – Time‑series SQL database built on PostgreSQL. - – Time‑series database for IoT, vehicles, and industrial monitoring. - – Document database with a flexible, JSON‑like data model. - – Highly scalable, distributed NoSQL database for large‑scale deployments. - – In‑memory data structure store used as database, cache, and message broker. - – Distributed SQL database with strong consistency and horizontal scaling. - – Native graph database for connected data and relationships. - – Purpose‑built time‑series database for metrics and events. - – Drop‑in Cassandra alternative written in C++ for higher performance. - – Distributed, scalable, big data store modeled after Google's Bigtable, running on top of HDFS. ⬆ back to contents ---

Tools

A collection of libraries and drivers for seamless database access and interaction. - - Python library for ODBC database access. - - SQL toolkit and ORM for Python. - - PostgreSQL database adapter. - - MySQL driver for Python. - - ORM for Python with dynamic query generation. - - Official MongoDB driver for Python. - - A tool for exploring SQLite databases and visualizing the results of your queries. - - A high quality, visual, tool to create, design, and edit database files compatible with SQLite. - - A free universal database tool and SQL client for developers, SQL programmers, and administrators. - - A modern, easy-to-use SQL client and database manager with a clean, cross-platform interface. - DBConvert Streams - Database IDE for exploring, migrating, and syncing PostgreSQL, MySQL, files, and S3. - - A modular SQL linter and auto-formatter designed to enforce consistent style and catch errors in SQL code. - - A pure-Python MySQL client library for interacting with MySQL databases from Python applications. - - An AI-powered tool for generating SQL queries from natural language questions. - - A chat-based SQL client that allows you to query databases using natural language conversations. - - SQL queries to databases via Python syntax. - - JSON-like interface for working with SQL databases. - - A no-dependency SQL parser, transpiler, and optimizer for Python. ⬆ back to contents ---

📊 Data Visualization

Resources

Color theory, chart selection guides, and storytelling tips. - - A guide to choosing the right visualization based on your data. - - A curated list of awesome data visualization libraries, tools, and resources. - - Interactive notebooks designed to teach data visualization concepts. - - Guide to creating effective scientific visualizations and plots. - The Python Graph Gallery - A collection of Python graph examples for data visualization. - FlowingData - Insights on data analysis and visualization. - Data Visualization Catalogue - A comprehensive catalog of data visualization types. - Data Viz Project - A resource for selecting suitable visualizations. - Chartopedia - A guide to help you select the appropriate chart types. - DataForVisualization - Tutorials and insights on data visualization techniques. - Truth & Beauty - Exploration of the aesthetics of data visualization. - Cedric Scherer's DataViz Resources - A collection of top data visualization resources and inspiration. - Information is Beautiful - A site dedicated to visualizations that make complex ideas clear and engaging. - Plottie - A vast library of scientific plots for visualization inspiration and ideas. - - A collection of bad data visualization practices and better alternatives. - Natural Colours - A digital archive of historical color systems and pigments. - Colorgorical - Resource for generating categorical color palettes using perceptual principles. ⬆ back to contents ---

Tools

Libraries for static, interactive, and 3D visualizations. - Matplotlib - A comprehensive library for creating static, animated, and interactive visualizations in Python. - Seaborn - A statistical data visualization library based on Matplotlib. - Plotly - A library for creating interactive plots and dashboards. - - A declarative statistical visualization library for Python. - Bokeh - A library for creating interactive visualizations for modern web browsers. - HoloViews - A tool for building complex visualizations easily. - Geopandas - An extension of Pandas for geospatial data. - Folium - A library for visualizing data on interactive maps. - Pygal - A Python SVG charting library. - Plotnine - A grammar of graphics for Python. - - A plotting library for IPython/Jupyter notebooks. - - A large (+2500) collection of color maps for Python. - - A WebGL-powered framework for visual exploratory data analysis of large datasets. - - Contextily: add background basemaps to your plots in GeoPandas. - - A package to easily download, model, analyze, and visualize street networks from OpenStreetMap. - - A powerful, interactive charting and visualization library for browser-based applications. - - A high-performance interactive 2D/3D data visualization library leveraging the power of OpenGL. - - A Python library for scientific visualization that is fast, scalable and beautiful, based on OpenGL. - - Bokeh plotting backend for Pandas. - - Free, , cross-platform geographic information system (GIS). - Flourish - Platform for creating interactive data visualizations and stories without coding. ⬆ back to contents ---

📈 Dashboards & BI

Resources

Ttutorials for building and enhancing dashboards and visualizations using various tools and frameworks. - - A collection of outstanding dashboard and visualization resources. - - Showcase of community-built Streamlit applications. - - Comprehensive resources for Dash users. - - Resources and support for Panel users. - - Curated list of Streamlit resources and components. - - Production-ready Dash apps. - geeksforgeeks - Tableau Tutorial - Comprehensive tutorial on Tableau. - geeksforgeeks - Power BI Tutorial - Detailed tutorial on Power BI. - Tableau Public Gallery - A curated collection of real-world interactive dashboards to inspire and learn from. ⬆ back to contents ---

Tools

Frameworks for building custom dashboard solutions. - - Framework for creating interactive web applications. - - Simplified framework for building data applications. - - Python library for creating custom interactive web apps and dashboards. - - Tool for creating and sharing machine learning applications. - - A powerful data visualization and dashboarding tool for OpenSearch data, forked from Kibana. - - A library for building draggable, resizable responsive dashboard layouts. - - A React library to build dashboards fast with pre-built components for charts, KPIs, and more. - - An platform to build and deploy internal tools, admin panels, and CRUD apps quickly. - - A Python library for generating Grafana dashboards configuration as code. - - A Python framework for rapidly building and deploying realtime web apps and dashboards for AI and analytics. - - Python version of the popular R Shiny framework. - - Turn Jupyter notebooks into standalone web applications. - - Full-stack Python framework for building web apps. - - Python library for building web applications and interactive dashboards. - - Business intelligence platform that uses SQL and Markdown for reports. ⬆ back to contents ---

Software

A list of leading tools and platforms for data visualization and dashboard creation. - Tableau - Leading data visualization software. - Microsoft Power BI - Business analytics tool for visualizing data. - QlikView - Tool for data visualization and business intelligence. - Metabase - User-friendly BI tool. - Apache Superset - data exploration and visualization platform. - Preset - A platform for modern business intelligence, providing a hosted version of Apache Superset. - - The simplest way to get analytics and business intelligence for everyone in your company. - - Tool for visualizing and sharing data insights. - Grafana - Dashboarding and monitoring tool. - - User-friendly chart and map creation tool. - ChartBlocks - Online chart creation platform. - Infogram - Tool for creating infographics and visual content. - Google Data Studio - Free tool for creating interactive dashboards and reports. - - Next-generation automated data exploratory analysis and visualization platform. - - The official visualization and dashboarding tool for the Elastic Stack (Elasticsearch, Logstash, Beats). ⬆ back to contents ---

🕸️ Web Scraping & Crawling

Resources

A collection of valuable resources, tutorials, and libraries for web scraping with Python. - - List of libraries, tools, and APIs for web scraping and data processing. - - Code samples from the book "Web Scraping with Python". - - Tutorial for scraping streaming sites. - - An open project repository sharing knowledge and experiences about web scraping with Python. ⬆ back to contents ---

Tools

A list of libraries and tools for web scraping. - - A simple, yet elegant, HTTP library for Python. - BeautifulSoup - A library for parsing HTML and XML documents. - - A tool for automating web applications for testing purposes. - Scrapy - An and collaborative web crawling framework for Python. - - A library for browser automation and web scraping. - - Distributed Crawler Management Framework based on Scrapy, Scrapyd, Django, and Vue.js. - - A smart, automatic, fast, and lightweight web scraper for Python. - - A library to parse feeds in Python. - - A Python & command-line tool to gather text and metadata on the web. - - A tiny command-line utility to download media contents (videos, audios, images) from the web. - - A Python library for automating interaction with websites. - - A Python scraper based on AI. - - A social networking service scraper in Python. - - A web scraping system that lets you declaratively describe what data to extract using a simple query language. - - A Python framework for building web scraping apps, providing a high-level API for asynchronous requests. - - Python version of the Playwright browser automation library. - - A jQuery-like library for parsing HTML documents in Python. - - High-level Selenium wrapper for easier web automation. - - A framework for building web scrapers and crawlers. - - Advanced web crawling framework designed for AI and data extraction tasks. ⬆ back to contents ---

🔢 Mathematics

A collection of resources for learning mathematics, particularly in the context of data science and machine learning. - - A curated list of mathematics resources, books, and online courses. - - Comprehensive resource for mathematics in machine learning. - 3Blue1Brown - Visual explanations of mathematical concepts through animated videos. - Immersive Linear Algebra - Interactive resource for understanding linear algebra. - - Resource for learning statistics and mathematics for data science. - - Collection of Python scripts and notebooks for statistics and mathematics. - - Resource for learning linear algebra computationally. ⬆ back to contents ---

🎲 Statistics & Probability

Resources

A selection of resources focused on statistics and probability, including tutorials and comprehensive guides. - - A curated list of statistics resources, software, and learning materials. - - Notebooks for understanding statistical learning concepts. - - Interactive visual resource for learning probability and statistics. - - Companion code for a practical statistics book. - Statistical Learning Theory - Stanford University - Lecture notes on statistical learning theory. - StatLect - Comprehensive online textbook covering probability and statistics concepts. - stanford.edu - Probabilities and Statistics - Refresher course on probabilities and statistics from Stanford University. - - Resource for learning Bayesian methods in Python. - - Code for the book "Bayesian Modeling and Computation in Python". - Stat Trek - A resource for learning statistics and probability, with tutorials and tools. - Online Statistics Book - An interactive online statistics book with simulations and demonstrations. - - Resource for studying statistics based on Wasserman's book. - - Book and code for an introduction to Probability and Statistics. - - Book and code for Bayesian statistical methods. - Causal Inference: The Mixtape - Practical guide to causal inference methods. - The Effect - Modern introduction to causality and research design. - - statistics hands-on handbook. ⬆ back to contents ---

Tools

A collection of tools focused on statistics and probability. - - Fundamental library for scientific computing and statistics. - - Statistical modeling, testing, and data exploration. - - A probabilistic programming library for Python that allows for flexible Bayesian modeling. - - Statistical package with improved usability over SciPy. - - Post-hoc tests for statistical analysis of data. - - Survival analysis and event history analysis in Python. - - Survival analysis built on scikit-learn for time-to-event prediction. - - Bootstrap confidence interval estimation methods. - - Python interface to Stan for Bayesian statistical modeling. - - Exploratory analysis of Bayesian models with visual diagnostics. - - A Python library for generalized additive models with built-in smoothing and regularization. - - A probabilistic programming library built on JAX for high-performance Bayesian modeling. - - A Python implementation of the R package for causal inference using Bayesian structural time-series models. - - A Python library for causal inference that supports explicit modeling and testing of causal assumptions. - - A Python library for describing statistical models and building design matrices. - - Fast and flexible probabilistic modeling library for Python with GPU support. - - Python library for probabilistic and causal inference using graphical models. ⬆ back to contents ---

🧪 A/B Testing

A collection of resources focused on A/B testing. - DynamicYield A/B Testing - An online course covering advanced testing and optimization techniques. - Evan's Awesome A/B Tools - A/B test calculators. - Experimentguide - A practical guide to A/B testing and experimentation from industry leaders. - Google's A/B Testing Course - A free Udacity course covering the fundamentals of A/B testing. - So You Think You Can Test? - Experience the challenges of A/B testing through this educational simulation. ⬆ back to contents ---

⏳ Time Series Analysis

Resources

A collection of resources for understanding time series fundamentals and analytical techniques. - - A curated list of resources dedicated to time series analysis and forecasting. - Forecasting: Principles and Practice - Comprehensive textbook on forecasting methods with practical examples. - NIST/SEMATECH e-Handbook - Official time series analysis guide from NIST. - - A curated list of tools, datasets, and papers dedicated to time series anomaly detection. - - A comprehensive list of Python tools and libraries for time series analysis. ⬆ back to contents ---

Tools

A collection of tools for working with temporal data. - - A procedure for forecasting time series data based on an additive model. - - A Python package for Bayesian time series forecasting and inference. - - A unified Python framework for machine learning with time series, compatible with scikit-learn. - - A Python toolkit for probabilistic time series modeling, built on MXNet. - - A library for deep learning-based time series analysis and forecasting. - - A pretrained time series foundation model from Google Research for zero-shot forecasting. - - A PyTorch-based library for time series forecasting with neural networks. - - A collection of time series prediction methods and implementations. - - A tool to visualize and analyze time series data logs in real-time. - - Automatically extracting features from time series data. - - Python library for ARIMA modeling and time series analysis. - - Toolkit for analyzing time series data from Facebook Research. ⬆ back to contents ---

⚙️ Data Engineering

Resources

A collection of resources to help you build and manage robust data pipelines and infrastructure. - - A comprehensive guide covering fundamental and advanced data engineering concepts. - - Free course on data engineering fundamentals. - - A curated list of data engineering tools, software, and resources. - - Techniques and strategies for building reliable data platforms. - - A curated list of pipeline toolkits for data processing and workflow management. - - A curated list of awesome database tools. - - Curated resources for learning and working with Apache Kafka: books, trainings, tools. ⬆ back to contents ---

Tools

A collection of tools for building, deploying, and managing data pipelines and infrastructure. - - A framework for transforming data in your warehouse using SQL and Jinja. - - A unified engine for large-scale data processing and analytics. - - A distributed event streaming platform for building real-time data pipelines. - - A data orchestrator for machine learning, analytics, and ETL. - - A platform to programmatically author, schedule, and monitor workflows. - - A data warehouse software for reading, writing, and managing large datasets in distributed storage using SQL. - - A framework that allows for the distributed processing of large data sets across clusters of computers. - - A Python module for building complex and batch-oriented data pipelines. - - A high-performance table format for huge analytic datasets. - - A highly scalable distributed NoSQL database designed for handling large amounts of data across many commodity servers. - - A framework for stateful computations over unbounded and bounded data streams (real-time stream processing). - - A unified model for defining both batch and streaming data-parallel processing pipelines. - - A cloud-native, distributed messaging and streaming platform. - - A storage layer that brings ACID transactions to Apache Spark and big data workloads. - - An open data lakehouse platform, built on a high-performance open table format. - - A distributed SQL query engine designed for fast analytic queries against large datasets. - - A metadata platform for the modern data stack. - - An open framework for collection and analysis of data lineage. - - A framework for creating reproducible, maintainable and modular data science code. - - A dynamic data management framework that allows for SQL parsing, optimization, and federation. - - Workflow orchestration for building resilient data pipelines. - - Universal columnar format and multi-language toolbox for fast data interchange. - - An , event-driven orchestrator that simplifies data workflow management. - - Orchestration engine for running complex, multi-step workflows and business processes. ⬆ back to contents ---

📖 Natural Language Processing (NLP)

Resources

A selection of resources for learning and applying natural language processing in Python. - - A ranked list of awesome Python libraries for natural language processing (NLP). - Hugging Face NLP Course - Official course on transformers and NLP from Hugging Face. - - Code examples and notebooks for practical natural language processing. - - Lecture materials from Oxford's Deep Natural Language Processing course. - NLTK Book - Natural Language Processing with Python. - - Jupyter notebooks demonstrating various NLP techniques and applications. - - The hands-on NLTK tutorial for NLP in Python. - - Yandex School of Data Analysis course on Natural Language Processing. - - Comprehensive NLP guide covering theory, models, and practical implementations. ⬆ back to contents ---

Tools

A collection of powerful libraries and frameworks for natural language processing. - Natural Language Toolkit (NLTK) - A leading platform for building Python programs to work with human language data. - TextBlob - A simple library for processing textual data. - SpaCy - An software library for advanced NLP in Python. - - A transformer-based model for NLP tasks. - - A simple framework for state-of-the-art NLP. - - A library and framework for building applications with large language models. - - A Java suite of core NLP tools providing fundamental linguistic analysis capabilities. - - A state-of-the-art Natural Language Processing library built on Apache Spark. - - A Python framework for adversarial attacks, data augmentation, and model training in NLP. - - Topic modeling and natural language processing library for Python. - - Python NLP library for many human languages, from the Stanford NLP Group. - - Framework for state-of-the-art sentence and text embeddings. - - Google's library for structured information extraction from text using language models. - - framework for building contextual AI assistants and chatbots. ⬆ back to contents ---

🤖 Machine Learning & AI

Resources

A collection of resources to help you learn and apply machine learning concepts and techniques. - - A curated list of awesome Machine Learning frameworks, libraries and software. - - Machine learning and deep learning tutorials, articles and other resources. - - A curated list of awesome Deep Learning tutorials, projects and communities. - - A ranked list of awesome machine learning Python libraries and tools. - - A beginner-friendly introduction to machine learning concepts and practices. - - Open Machine Learning Course with practical assignments and real-world applications. - - A free practical machine learning course focused on building and deploying models. - - A curated list of artificial intelligence resources. - - Official repository for Google Research projects and publications. - - A comprehensive coding challenge to learn machine learning over 100 days. - - Resource for building and deploying machine learning applications. - - Hands-on guide to machine learning and deep learning using Python. - - Microsoft's curriculum on artificial intelligence. - - Educational repository for building LLMs from scratch. - - A comprehensive guide to generative AI models, tools, and applications. - - A curated list of papers, projects, and resources related to Large Language Models. - - Jupyter notebooks covering various machine learning algorithms and applications. - Understanding Deep Learning - Comprehensive and accessible textbook on deep learning fundamentals. - - Curated roadmap of seminal deep learning papers for newcomers. - - Curated resources and tools for applied machine learning in industry. - - Implementations of deep learning papers with annotated code. - - Core machine learning algorithms implemented in Python from scratch. - - Carefully curated list of AI/ML books, courses, and practical tools. ⬆ back to contents ---

Tools

A collection of tools for developing and deploying machine learning models.

Machine Learning

- - Machine learning library for classical algorithms and model building. - - Optimized distributed gradient boosting library for tree-based models. - - Fast, distributed, high-performance gradient boosting framework. - - High-performance gradient boosting on decision trees with categorical features support. - - distributed machine learning platform. - - GPU-accelerated machine learning algorithms from RAPIDS. - - Modern C++ toolkit containing machine learning algorithms and tools. - - Game theoretic approach to explain the output of any machine learning model. - - Fit interpretable models and explain blackbox machine learning. - - Hyperparameter optimization framework.

Deep Learning

- - End-to-end platform for machine learning and deep learning. - - Deep learning framework with strong support for research and production. - - PyTorch wrapper for high-performance AI research. - - High-level library to help with training and evaluating neural networks. - - High-level neural networks API, running on top of TensorFlow. - - Deep learning library simplifying training fast and accurate neural nets. - - Model-definition framework for state-of-the-art machine learning models. - - Library for state-of-the-art pretrained diffusion models. - - Library for efficiently adapting large pretrained models. - - Real-time object detection system. - - YOLOv8 and other computer vision models. - - Open standard for machine learning interoperability. - - Geometric deep learning extension library for PyTorch. - - Deep universal probabilistic programming with Python and PyTorch. - - Scikit-learn compatible neural network library. - - DeepMind's library for building complex neural networks. - - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more. - - Official TensorFlow repository with models and examples. - - A simple framework that automates ML/DL workflows by providing prebuilt trainers, templates, logging, configuration management, and much more. ⬆ back to contents ---

🚀 MLOps

Resources

Materials and curated lists for machine learning operations. - - A free course focused on the practical aspects of deploying and maintaining ML systems. - - A curated list of references for MLOps. - - A curated list of awesome MLOps tools. - - An awesome & curated list of best LLMOps tools for developers. - - A course dedicated to Large Language Models, their architecture and applications. - - A practical guide to machine learning engineering and MLOps best practices. - - A curated list of tools for deploying, monitoring, and maintaining ML systems in production. - - Official recipes and examples for working with Llama models. - - Curated resources, tools, and projects for the Kubeflow machine learning platform. ⬆ back to contents ---

Tools

Platforms and utilities for deploying, monitoring, and maintaining ML systems. - - High-performance distributed training framework. - - Version control system for machine learning projects. - - Tool for analyzing and monitoring data and model drift. - - Validation for ML models and data. - - Tool to build, debug, and execute ML pipelines with native Python. - - Real-time performance monitoring. - - Fast, search engine. - - High-throughput and memory-efficient inference library for LLMs. - - LLM framework for building search and question answering systems. - - Machine learning toolkit for Kubernetes. - - platform for deploying and monitoring machine learning models in production. - - A feature store for machine learning that manages and serves ML features to models. - - Framework for building, shipping, and scaling ML applications. - - platform for the complete machine learning lifecycle. - - Tool for experiment tracking, dataset versioning, and model management. - - ML platform for tracking, comparing and optimizing experiments. - - A human-friendly Python library for helping scientists and engineers build and manage real-life data science projects. - - Platform for integrating AI into databases and applications. - - Standardized serverless inference platform for deploying and serving machine learning models on Kubernetes. - - Brings machine learning capabilities to SQL, enabling model training and prediction using SQL syntax. - - Framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. - - Unified interface to call all LLM APIs (OpenAI, Anthropic, Cohere, etc.) with consistent output formatting. ⬆ back to contents ---

🧠 AI Applications & Platforms

Resources

A collection of resources focused on AI applications and platforms. - - Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and models. - - A curated list of modern Generative Artificial Intelligence projects and services. - - Microsoft's course on designing and building AI agents. - - Course on generative AI for beginners from Microsoft. - - Free hands-on course on modern tools for building and deploying AI applications. - - Practical course to master large language models from start to finish. - - A curated list of AI autonomous agents, environments, and frameworks. - - The Generative AI Landscape - A Collection of Awesome Generative AI Applications. - - A collection of projects showcasing RAG, agents, workflows, and other AI use cases. - - System Prompts, Internal Tools & AI Models from various AI applications and coding tools. - - Collection of advanced techniques for Retrieval-Augmented Generation. - - Awesome list of tools and projects with the awesome LangChain framework. - - A curated list of Artificial Intelligence Top Tools. - - A curation of awesome tools, documents and projects about LLM Security. - - Official Anthropic examples and recipes for working with Claude AI. - - Covers LLM fundamentals, prompt engineering, and fine-tuning. - - Resources for building, deploying, and maintaining AI systems. - - Code-first tutorials for building production-grade GenAI agents. - - Curated list of 120+ LLM libraries across various categories. - - Repository of AI agent implementations and tutorials. - - Personal notes and essays on AI and software development. - - Comprehensive list of large language models and their capabilities. - - Guides, papers, and resources for prompt engineering with LLMs. - - Collection of prompt engineering techniques and strategies. - - 500+ AI agent projects with code for learning and inspiration. - - Roadmap and resources for mastering generative AI technologies. - - Collection of templates, integrations, and resources for the n8n automation platform. - - Up-to-date list of free APIs for accessing large language models (LLMs). ⬆ back to contents ---

Tools

A collection of frameworks, platforms, and end-user applications for building and deploying AI-powered solutions.

AI Agents & Automation

- - Workflow automation platform for connecting APIs and services. - - Framework for orchestrating role-playing AI agents. - - Framework for building multi-agent conversational systems. - - Autonomous AI agent that can complete complex tasks. - - Framework for building stateful, multi-actor applications with LLMs, with cycles and control flow. - - framework for building agentic AI systems. ogrammatically. - - platform for building and deploying AI agents. - - Multi-modal intelligent agent framework by Tencent Cloud. - - Tool-using reasoning agent with execution-augmented reasoning. - - LangChain framework for building sophisticated multi-agent systems. - - AI memory system for long-term context and personalized interactions. - - AI-powered browser automation framework for web interaction. - - Open agentic framework that autonomously interacts with computer GUIs like a human. - - AI agent platform for building and scaling production-grade autonomous agents. - - Powerful visual platform for building and deploying AI-powered agents and workflows. - - Framework for building and deploying AI agents with advanced reasoning and tool use. - - UI visual tool for building custom LLM orchestration flows and AI agents. - - Multi-agent framework that simulates roles in a software company to build projects. - - Local AI research assistant that searches web, papers, and documents. - - AI agent CLI that writes code, uses terminal, browses web, and runs locally. - - AI coworker that learns from your emails/meetings to automate drafting, prep, and tasks. - - Python SDK that dispatches parallel web-research agents across table rows, synthesizing multi-agent findings into structured columns. - - Framework for building a personal AI assistant with memory, skills, and learning ability. - - Collection of ready-to-use workflow templates for the n8n automation platform. - - AI browser automation using LLMs & computer vision. Playwright-compatible SDK + no-code workflows. - - desktop alternative to Claude Cowork for running agents, skills, and MCP locally with team collaboration features. - - Agentic LLM for autonomous data science, which can autonomously complete a wide range of data-centric tasks without human intervention.

Development Frameworks & Tools

- - Framework for developing applications powered by language models. - - Data framework for LLM-based applications with RAG capabilities. - - Official Python library for OpenAI API. - - Official OpenAI framework for building AI agents. - - RAG (Retrieval-Augmented Generation) workflow platform. - - Web crawling and data extraction service for AI applications. - - Framework for augmenting humans using AI. - - platform for building AI applications with custom API keys. - - Powerful visual platform for building and deploying AI-powered agents and workflows. - - Scalable generative AI framework from NVIDIA for LLMs, Multimodal, and Speech AI. - - AI-powered agent framework for automatic code generation from research papers and text.

Code Generation & Assistance

- - AI-powered code generation tool. - - AI pair programmer that writes entire applications. - - Self-hosted AI coding assistant.

Model Deployment & Platforms

- - Tool for running large language models locally. - - Open platform for operating large language models in production. - - Self-hosted, local-first AI model deployment platform. - - Visual LLM application development platform. - - Easy-to-use LLM fine-tuning framework. - - Library for faster and more memory-efficient LLM fine-tuning. - - Fully private, on-premise document intelligence platform for chatting with your documents using local LLMs.

AI Reliability & Debugging

- - Pytest-style unit testing framework for LLMs. Metrics for RAG, agents, hallucination, summarization, and custom criteria. - - Evaluation toolkit for LLM apps. Metrics, test generation, and insights for optimizing RAG pipelines and agents. - - AI observability platform. Tracing, datasets, experiments, and playground for troubleshooting and evaluating LLM apps. - - debugging infrastructure for RAG and AI agents. Includes 16-problem RAG failure map and TXT stress-test engine.

End-User Applications

- - Web interface for interacting with various LLMs. - - Visual node-based interface for Stable Diffusion. - - Modern AI conversation interface. - - ChatGPT alternative. - - Personal second brain and AI assistant. - - AI-powered image upscaling tool. - - AI face swapping and enhancement tool. - - Documentation-based question answering system. - - AI-powered research assistant for iterative, deep research on any topic. - - Local AI that records, searches, and automates tasks based on your screen and audio. - - multimodal creative assistant and privacy-focused alternative to Canva/Manus for local image/video generation. - - AI-powered personalized learning assistant with document Q&A, exercise generation, and deep research capabilities.

Additional Tools

- - unified multimodal model for understanding and generating images. - - Robust speech recognition model for transcription and translation. - - Generative TTS model optimized for natural, expressive daily dialogue with fine-grained prosody control. - - On-device TTS model with instant voice cloning from audio samples.. - - Collection of resources, guides, and tools for effective Claude Code AI assistant use. ⬆ back to contents ---

☁️ Cloud Platforms & Infrastructure

Resources

A collection of resources for mastering cloud-native technologies, containerization, and infrastructure management. - - A curated list of resources for cloud native technologies. - - A curated list for awesome Kubernetes resources. - - A curated list of Docker resources and projects. - - Hands-on labs to help you learn about the AWS Well-Architected Framework. - - Tutorial for bootstrapping a Kubernetes cluster the hard way on Google Cloud Platform. - - A curated list of Docker Compose samples. - - A best practices guide for Amazon EKS. - - A list of Free Software network services and web applications which can be hosted locally. - - A curated list of awesome selfhosted applications and solutions using Docker. - - A curated list of awesome Kubernetes tutorials, tools, and resources. - - A curated list of awesome cloud security resources, tools, and best practices. - - Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, and more. - - Curated hands-on labs and exercises for learning cloud security platforms. ⬆ back to contents ---

Tools

Tools for containerization, orchestration, infrastructure as code, and cloud-native development.

Containerization & Orchestration

- - Open platform for developing, shipping, and running applications in containers. - - A tool for defining and running multi-container Docker applications. - - Production-grade container orchestration system. - - Conversion tool from Docker Compose to Kubernetes.

Infrastructure as Code

- - Infrastructure as Code tool. - - fork of Terraform. - - Modern IaC platform using familiar programming languages. - - Define Kubernetes apps using familiar languages.

CI/CD & GitOps

- - automation server. - - Declarative GitOps continuous delivery. - - Container-native workflow engine. - - Kubernetes-native CI/CD framework. - - Multi-cloud continuous delivery. - - Portable devkit for CI/CD pipelines.

Service Mesh & API Gateways

- - Modern HTTP reverse proxy and load balancer. - - Cloud-native API Gateway. - - Dynamic API gateway. - - Manages Envoy Proxy as gateway. - - Cloud-native API gateway based on Istio. - - Service mesh management.

Kubernetes Ecosystem

- - Package manager for Kubernetes. - - Configuration customization for Kubernetes. - - Web-based UI for Kubernetes. - - Continuous development for Kubernetes. - - Local development for Kubernetes. - - Progressive delivery operator. - - Application delivery platform. - - Kubernetes multi-cloud management.

Developer Platforms & Control Planes

- - Cloud native control plane. - - Kubernetes packages and Helm charts. - - Kubernetes dashboard. - - End-to-end developer platform.

Additional Tools

- - Tool for building and managing portable virtual development environments as code. ⬆ back to contents ---

🏗️ System Design & Architecture

Resources for building and architecting robust, scalable data systems. - - Comprehensive guide to large-scale system design. Includes Anki flashcards for interview prep. - - Design patterns for building scalable systems with real-world case studies from Netflix, Facebook, and Uber. - - Visual introduction to system design concepts with detailed diagrams and illustrations. - - Curated collection of articles, videos, books, and interview questions. - - Concise course covering load balancing, caching, databases, and CAP theorem. - - Practical system design examples and structured interview preparation. - - Extensive catalog of software architecture articles, books, and tools. - - Collection of patterns from code-level to microservices and big data. - - Practical questions, diagrams, and solutions for architecture interviews. - - Deep dive into Domain-Driven Design (DDD) and hexagonal architecture. - - Collection of design system guides and component libraries for large projects. ⬆ back to contents ---

⚡ Productivity

Resources

A collection of resources to enhance productivity. - - A next-generation data science IDE. - - An AI web automation tool with multi-agent system that runs directly in your browser. - - Ranked list of notable Jupyter Notebook, Hub, and Lab projects. - - AI native data science notebook platform compatible with Jupyter, featuring real-time collaboration, environment management, and integrations. - - All-in-one workspace for notes, docs, and data visualization. - - Reactive Python notebook for reproducible and interactive data science. - - A collection of useful prompts for data scientists using ChatGPT. - Gamma.app - AI-powered platform for creating and sharing presentations and documents. - - A standardized project structure for data science projects. - - Comprehensive guide to learning regular expressions with examples and exercises. - - Curated collection of regex tools, libraries, and learning resources. - The Markdown Guide - Comprehensive guide to learning Markdown. - - A tool to automatically generate README.md files for your projects. - - Extension for writing emails in Markdown and rendering them before sending. - - Simple and elegant markdown editor for documentation. - - Lightweight markdown processor for fast document rendering. - - AI tool that converts screenshots into code for various frontend stacks. - Codebeautify - All-in-one online code formatter and beautifier for Python, SQL, JSON, and more. - Notion - An all-in-one workspace for note-taking and task management. - Trello - A visual project management tool. - - A habit-building and productivity app that treats your life like a role-playing game. - Bujo - Tools to help transform the way you work and live. - Parabola - An AI-powered workflow builder for organizing data. - Asana - A project management platform for tracking work and projects. - - An , browser-based computing environment and cloud OS. - - Plugin-driven, WYSIWYG markdown editor framework inspired by Typora. - - AI tool for bilingual scientific PDF translation preserving formulas, charts, and layout. ⬆ back to contents ---

Useful Linux Tools

A selection of tools to enhance productivity and functionality in Linux environments. - - Simplified and community-driven man pages with practical examples. - - Cat clone with syntax highlighting. - - Modern replacement for ls. - - Faster grep alternative. - - Smarter cd command. - - Simple animated GIF screen recorder with an easy to use interface. - - Clipboard manager with advanced features. - - Command-line translator using Google Translate, Bing Translator, Yandex.Translate, etc. - - Cross-platform Text Expander written in Rust. - - Powerful yet simple to use screenshot software. - - An diagramming software for making flowcharts, process diagrams, and more. - - A powerful, free, and vector graphics editor for creating and editing visualizations. - Rclone - A command-line program to manage files on cloud storage. - Rsync - A fast and versatile file copying tool that can synchronize files and directories between two locations over a network or locally. - - System restore tool for Linux that creates filesystem snapshots using rsync+hardlinks or BTRFS snapshots. - - A comfortable and well-configurable graphical frontend for incremental backups. - - A command-line fuzzy finder. - - SQL powered operating system instrumentation, monitoring, and analytics. - GNU Parallel - A tool to run jobs in parallel. - HTop - An interactive process viewer. - Ncdu - A disk usage analyzer with an ncurses interface. - - A command line tool to correct your previous console command. - - A tool for querying, processing, and formatting data in various file formats (CSV, JSON, etc.), like awk/sed/cut for data. - - Command-line JSON processor for parsing and manipulating JSON data. - - Portable command-line YAML processor (like jq for YAML and XML). - - Run SQL directly on CSV or TSV files from the command line. - - Interactive multitool for tabular data exploration in the terminal. - - Suite of command-line tools for working with CSV data. - - Modern command-line HTTP client for API testing and debugging. - - Cross-platform system monitoring tool for resource usage analysis. - - Command-line benchmarking tool for performance testing. - - Draw basic graphs in the terminal for quick data visualization. - - Simple, fast and user-friendly alternative to 'find'. - - More intuitive version of du written in rust. - - Cross-platform graphical process/system monitor. - - Tool for managing and securely storing passwords and secrets. ⬆ back to contents ---

Useful VS Code Extensions

A collection of extensions to enhance functionality and productivity in Visual Studio Code. - JDBC Adapter - Connect to various databases using JDBC. - DBCode - Connect - Database client for managing and querying databases. - Markdown All in One - Essential tools for Markdown editing. - Markdown Preview Styles - Changes VS Code's markdown preview to match 's styling. - Snippington Python Pandas Basic - Basic tools for working with Pandas in Python. - PDF Viewer for Visual Studio Code - View PDF files directly in VS Code. - Quick Python Print - Quickly handle print operations in Python. - Rainbow CSV - Highlight CSV and TSV files and run SQL-like queries. - Remove Blank Lines - Extension to remove empty lines in documents. - PDF Preview in VSCode - Show PDF previews in VS Code. - CSV to Table - Convert CSV/TSV/PSV files to ASCII formatted tables. - Data Preview - Import, view, slice, and export data. - Data Wrangler - Tool for cleaning and preparing tabular datasets. - Error Lens - Enhances the display of errors and warnings in code. - Indent Rainbow - Makes indentation easier to read. - Markdown Table Editor - Add features to edit Markdown tables. - WYSIWYG Editor for Markdown - View Word and Excel files and edit Markdown. - Prettier - Code formatting extension for VS Code. - Project Manager - Easily switch between projects. - Python Indent - Automatically indent Python code. - SandDance - Visually explore and present your data. - SQL Notebooks - Open SQL files as VSCode Notebooks. - SQL Tools - Database management tools for VSCode. - Kanban Board - A Kanban board extension for organizing tasks within VS Code. - Path Autocomplete - Provides path completion for files and directories in VS Code. - Path Intellisense - Autocompletes filenames in your code. - Python Imports Utils - Utilities for managing Python imports. - Workspace Dashboard - Organize your workspaces in a speed-dial manner. - Remote Development - Open any folder in a container, on a remote machine, or in WSL. - Text Power Tools - An all-in-one solution with 240+ commands for text manipulation. - Toggle Quotes - Toggle between single, double, and backticks for strings. - Comment Translate - Helps translate comments, strings, and variable names in your code. - Text Marker - Select text in your code and mark all matches with configurable highlight color. - Bookmarks - Mark lines in your code and jump to them easily. - Dendron - A hierarchical note-taking tool that grows as you do. - Gitignore Generator - Simplifies the process of generating .gitignore files. - Test Explorer UI - Run your tests in the sidebar of Visual Studio Code. - Python Test Explorer - Run your Python tests in the sidebar of Visual Studio Code. - VSCode Markdownlint - A VS Code extension to lint and style check markdown files. ⬆ back to contents ---

📚 Skill Development & Career

Practice Resources

A collection of resources to enhance skills and advance your career in data analysis and related fields. - LeetCode - A platform for preparing technical coding interviews. - Kaggle Competitions - Platform for participating in data analysis and machine learning competitions. - Makeovermonday - A platform focused on enhancing data visualization practices. - Workout Wednesday - Engage in weekly challenges to improve your visualization skills. - - Repository for the TidyTuesday project, promoting data analysis. - DrivenData Competitions - Data analysis competitions with a social impact focus. - Codecademy Data Science Path - Interactive courses for learning data analysis. - - A course to master SQL for data analysis, complete with real-world projects. - Hugging Face Tasks - Hands-on practice with specific NLP and machine learning tasks using real models. - - Collection of curated resources and strategies for LeetCode practice. - - Company-wise Leetcode problems for interview preparation. ⬆ back to contents ---

Curated Jupyter Notebooks

A selection of curated Jupyter notebooks to support learning and exploration in data science and analysis. - - Data & AI notebook templates catalog organized by tools. - - Data science Python notebooks covering various topics. - - Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney. - - Apache Spark & Python tutorials for big data analysis and machine learning. - - Example notebooks for data mining accompanying the course at Southern Methodist University. - - Python data repository with Jupyter notebooks and scripts. - - An introduction to data science using Python and Pandas with Jupyter notebooks. - - A collection of IPython notebooks covering various topics. - - A collection of interactive Python notebooks for learning data science concepts. - - Optimized notebooks for faster AI model training and fine-tuning. - - Official Hugging Face notebooks for NLP, vision, audio, and diffusion models. - - Official Jupyter notebooks from François Chollet's Deep Learning with Python book. - - Python notebooks for geostatistics and numerical demonstrations. ⬆ back to contents ---

Data Sources & Datasets

A collection of resources for accessing datasets and data sources for analysis and projects. - Kaggle Datasets - Extensive collection of datasets for practice in data analysis. - - A Python library for downloading datasets from Kaggle, Google Drive, and other online sources. - - An multi-tool for exploring and publishing data. - - Curated list of high-quality open datasets. - - Collection of various open data sources. - Free Datasets for Projects - Dataquest's compilation of free datasets. - Data World - The enterprise data catalog that CIOs, governance professionals, data analysts, and engineers trust in the AI era. - - A list of publicly available datasets with real-time data. - Google Dataset Search - A search engine for datasets from across the web. - NASA Open Data Portal - A site for NASA's open data initiative, providing access to NASA's data resources. - The World Bank Data - Free and open access to global development data by The World Bank. - - A collection of audio and speech datasets for voice AI and machine learning. - - A lightweight library to easily share and access datasets for audio, computer vision, and NLP. - - A collection of ready-to-use datasets for use with TensorFlow and other Python ML frameworks. - - A curated list of datasets for natural language processing (NLP) tasks. - - The torchvision.datasets module provides many built-in computer vision datasets. - - A collection of datasets and resources for training and fine-tuning Large Language Models (LLMs). - - A collection of datasets from Unsplash, useful for computer vision and research. - - A curated list of awesome JSON datasets that are publicly available without authentication. ⬆ back to contents ---

Resume and Interview Tips

A variety of resources to help you prepare for interviews and enhance your resume. - - Curated list of data science interview questions and answers. - - Resource to help you prepare for your upcoming data science interviews. - - A comprehensive collection of data science interview questions and resources. - - AI interview preparation guide with questions and solutions. - The Data Science Interview Book - A comprehensive resource to prepare for data science and machine learning interviews. - - A comprehensive guide to preparing for machine learning engineering interviews. - - Collection of machine learning interview questions and answers. - - Everything you need to prepare for your technical interview. - - Personal tech interview study guide covering algorithms and data structures. - Devinterview - Ace your next tech interview with confidence. - Interviewqs - Ace your next data science interview. - - A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep. - Dataford - Interview guides for data engineering, data science, ML, analytics, and PM roles. - Interview Query - Another platform to prepare for data science interviews. - - Curated resources for mastering behavioral and system design interviews. - Enhancv Data Scientist Resumes - A collection of resume examples and tips tailored for data scientists. - Data Science Portfolio - A platform to create and showcase your data science portfolio. - InterviewBit - SQL Interview Questions - Collection of SQL interview questions. - StrataScratch - Platform with real data science interview questions from top companies. - - Curated collection of coding patterns and strategies for technical interviews. - - Modern, technical resume template and example. - - Professional CV and resume templates built with LaTeX. - - resume builder with multiple templates and customization options. - - Collection of modern resume templates and CV examples. ⬆ back to contents ---

📋 Cheatsheets

A collection of cheatsheets across various domains to aid in quick reference and learning.

GoalKicker Programming Notes

- Python Notes for Professionals - A massive collection of Python concepts, idioms, and best practices for all levels. - SQL Notes for Professionals - A definitive guide to SQL syntax, queries, and database interaction concepts. - PostgreSQL Notes for Professionals - A professional compendium of knowledge for PostgreSQL administration and development. - MySQL Notes for Professionals - Essential reference material for working with the MySQL database management system. - Oracle Database Notes for Professionals - A guide to Oracle Database concepts, PL/SQL, and administration tasks. - MongoDB Notes for Professionals - A practical guide to working with NoSQL and MongoDB for modern application development. - Bash Notes for Professionals - A comprehensive guide to shell scripting and command-line mastery. - Git Notes for Professionals - Everything you need to know about version control with Git, from basics to advanced workflows. - Linux Notes for Professionals - A deep dive into Linux system administration, commands, and environment management. - Microsoft SQL Server Notes for Professionals - A detailed reference for developing and administering MS SQL Server databases. - PowerShell Notes for Professionals - A guide to task automation and configuration management using PowerShell. ⬆ back to contents ---

Python

- Python Cheat Sheet - Comprehensive Python syntax and examples. - - Interactive Python learning. - Pythoncheatsheet - Quick reference for Python basics and advanced topics. - - Detailed Python functions and libraries. - - A comprehensive cheatsheet for the Python programming language. - - Concise Python cheat sheet for quick reference and interview prep. ⬆ back to contents ---

Data Science & Machine Learning

- - List of Data Science Cheatsheets. - - Cheatsheets for data science, ML, computer science and more. - - Cheat sheets for quick reference in data science mathematics. - Pandas Cheat Sheet - Data manipulation with Pandas. - - Common PySpark patterns. - - Concise machine learning cheat sheets covering key concepts and equations. ⬆ back to contents ---

Linux & Git

- - Linux commands and shortcuts. - - Comprehensive list of Linux/Bash commands for developers and sysadmins. - - Bash scripting essentials. - - Unix terminal basics. - - Git/ workflows and tips. - - Git commands and best practices. - - Branching strategies. ⬆ back to contents ---

Probability & Statistics

- - Probability and statistics for engineers. - - In-depth probability concepts. - - Key statistical methods. ⬆ back to contents ---

SQL & Databases

- - Handy SQL reference guide. - PostgreSQL Cheatsheet - A handy reference for the most common PostgreSQL psql commands and queries. ⬆ back to contents ---

Miscellaneous

- - Mega-repository of cheat sheets. - Dataquest - Power BI Cheat Sheet - A helpful resource for Power BI users. - Data Structures Cheat Sheet - A concise reference for common data structures and their properties. - - Official cheatsheets for the Matplotlib plotting library in Python. - - VS Code shortcuts. - - Formatting for READMEs. - - Emojis in Markdown. - - Docker commands and workflows. - - Containerization basics. ⬆ back to contents ---

📦 Additional Python Libraries

A collection of supplementary Python libraries that enhance development workflow, automate processes, and maintain project quality beyond core data analysis tools.

Code Quality & Development

- - Uncompromising Python code formatter. - - Framework for managing pre-commit hooks. - - Python code static analysis. - - Optional static typing for Python. - - Rich text and beautiful formatting in the terminal. - - Debugging without using print. - - Logs pandas operations for data transformation tracking. - - Code style validator for Pandas. - - Python module dependency graphs. - - Automated Python imports for data science. - - Blazingly fast cognitive complexity analysis for Python, written in Rust. ⬆ back to contents ---

Documentation & File Processing

- - Documentation generator. - - API documentation for Python projects. - - Project documentation with Markdown. - OpenPyXL - Read/write Excel files. - - Exports data to XLSX, JSON, CSV. - - Reads and writes PDF files. - - Reads and writes Word documents. - - Smart CSV reader for messy data. - - Convert HTML to Markdown. - - Integration of Python with Excel. - - Converts XML to Python dictionaries. - - Python tool for converting files and office documents to Markdown. - - Build publication-quality books from Jupyter notebooks. - - Convert HTML to PDF. - - Advanced PDF manipulation library. - - PDF table extraction library. - - Fast, high-accuracy PDF and document conversion tool with layout preservation. ⬆ back to contents ---

Web & APIs

- - Next-generation HTTP client for Python. - - Modern web framework for building APIs. - - Lightweight Python web framework for building applications and APIs. - - Library for building CLI applications. - - Persistent caching for requests library. - - Asynchronous HTTP client/server framework for asyncio and Python. ⬆ back to contents ---

Miscellaneous

- - An extremely fast Python package installer and resolver. - - Fancy functional tools for Python. - - Image processing library. - - Fixes broken Unicode strings. - - Queries JSON data (SQL-like for JSON). - - Transforms nested data structures. - - Diagrams as code for cloud architecture. - - Framework for writing small tests. - - Pattern matching for Python dictionaries. - - A Python module for learning all major algorithms. - - A Python library used to interact with Git repositories. - - Progress bars for loops and operations. - - Python logging made simple. - - Beautiful command line interfaces. - - Python dependency management and packaging. - - Elegant configuration management. - - Tool for parameterizing and executing Jupyter notebooks programmatically. - - Pure Python framework for the Telegram Bot API with async support. ⬆ back to contents ---

📝 More Awesome Lists

A curated list of other awesome lists on various topics and technologies. - - A curated list of awesome lists. - - platform with thousands of interactive lessons for learning web development. - - A curated list of awesome big data frameworks, resources, and tools. - - A curated list of awesome geospatial libraries, tools, and resources. - - A repository for ChatGPT prompt curation. - - Curated list of Jupyter projects, libraries, and resources. - - Actively curated list of awesome BI tools. - - A curated list of resources for prompt engineering with LLMs like ChatGPT. - - A collection of bookmarks, resources, articles about product design. - - A curated list of awesome command-line frameworks, toolkits, and guides. - - A curated list of awesome FastAPI frameworks, libraries, and resources. - - A list of awesome applications and tools for Linux. - - A curated list of resources for product managers and aspiring PMs. - - A list of free software and applications written in Python. - - A curated list of awesome AutoHotkey libraries, scripts, and resources. - - A curated list of delightful productivity resources. - - A curated list of resources for scientific writing, publishing, and research. - - A curated list of LaTeX resources, libraries, and tools. - - A curated list of awesome Actions for automation. - - A curated list of Quarto resources, including talks, tools, examples, and articles. are welcome! - - A comprehensive list of useful VS Code extensions and resources. - - Collection of well-crafted README files for inspiration. - - A collection of awesome profile READMEs and resources. - - A collection of resources for code review practices. - - A curated list of IT and developer certifications and learning resources. - - A list of ngrok alternatives and tunneling software. - - Books, papers, videos, and toolboxes related to anomaly detection. - - Collection of powerful prompts for Anthropic's Claude AI. - - Curated list of Linux applications, tools, and resources for users and developers. - - List of beginner-friendly projects for to software. - - Curated list of helpful websites for programmers and engineers. - - Curated list of creative coding resources and libraries. - - Curated list of AI applications, tools, and research in finance. - - Collection of resources for learning and practicing algorithms and data structures. - - Curated resources for serverless architectures and cloud computing. - - Curated list of R packages, frameworks, and learning resources. - - Collection of effective system prompts for various AI models. - - Curated list of Intelligence (OSINT) tools and resources. - - Collection of Telegram bots, channels, and tools for developers. - - List of SaaS, PaaS, and IaaS offerings with free developer tiers. - - Icon library and toolkit for scalable vector graphics on the web. - - Curated list of essential tools and resources for creating great documentation. - - Curated list of software testing resources: tools, frameworks, books, and best practices. - - Comprehensive collection of resources, libraries, and tools for working with GraphQL. - - Resources, tips, and tools for finding and thriving in remote work. - - Curated list of frameworks, libraries, and utilities for asyncio-based Python programming. - - Massive collection of plugins, themes, and resources for customizing Zsh. - - Collection of links to free technical books on programming, databases, DevOps, and analytics. - - Largest multilingual collection of free programming books and learning materials. ⬆ back to contents ---

🌐 Additional Resources and Tools

A wide range of resources and tools designed to facilitate learning, development, and exploration across different domains. - - Path to a free, self-taught education in computer science. - - Course materials for the Data Science Foundations course. - - Production-ready OCR toolkit with multilingual and document AI support. - - A comprehensive list of free APIs for various purposes. - arXiv.org - A free distribution service and open-access archive for scholarly articles. - Elicit - An AI research assistant that helps automate parts of literature review. - - A massive collection of AI and machine learning projects with code for learning and portfolios. - - Full-stack template with FastAPI, React, and PostgreSQL. - Kittl - Platform for creating and editing charts and data visualizations. - - High Performace IDE for Jupyter Notebooks. - Sketch - Toolkit designed for designers, focusing on their workflow. - Growth.Design - A collection of product case studies and behavioral psychology insights for data-driven decision-making. - - Collection of badges for profiles and Markdown files. - - Curated list of free university computer science video courses. - - Tutorials on how to build your own technology from scratch. - - Technical explanation of what happens when you type a URL and press Enter. - - Extensive collection of exercises and questions for DevOps and Linux interview prep. - - Regularly updated list of free certification courses from top cloud and tech companies. - - Comprehensive list of free resources for students learning programming and tech. - - Up-to-date list of summer internships in tech with deadline tracking. - - Open learning course and toolkit for football data analysis with Python and R. ⬆ back to contents ---

案例信息

所属行业: 政务数字化
技术栈: 未知
上线时间: 2025
最近更新: 2026-08-11
在线地址: —

技术标签

aianalyticsbig-databusiness-intelligencedashboarddata-analysisdata-science