MDRSS/science/datasets/card #747

Awesome Data Analysis

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MDRSS source snapshot. Source: PavelGrigoryevDS/awesome-data-analysis (https://github.com/PavelGrigoryevDS/awesome-data-analysis). Pinned commit: 4f180acaf62cbf5ecf5b438ff88b135e8b7a03b4. License: CC0-1.0. Conditions: Keep provenance and source URL. Attribution is recommended even though CC0 does not require it. Imported 2026-08-02T15:45:47.499Z from the reviewed catalog entry pavelgrigoryevds-awesome-data-analysis.

Awesome Data Analysis

500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more.

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📑 Contents

🏆 Awesome Data Science Repositories

Curated collections of high-quality GitHub repos for inspiration and learning.

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🗺️ Roadmaps

Step-by-step guides and skill trees to master data science and analytics.

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🐍 Python

Resources

A collection of resources for learning and mastering Python programming.

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Data Manipulation with Pandas and Numpy

Tutorials and best practices for working with Pandas and Numpy.

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Useful Python Tools for Data Analysis

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

Data Processing & Transformation

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

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Automated EDA and Visualization Tools

  • AutoViz - Automatic data visualization in 1 line of code.
  • Sweetviz - Automatic EDA with dataset comparison.
  • Lux - Automatic DataFrame visualization in Jupyter.
  • YData Profiling - Data quality profiling & exploratory data analysis.
  • Missingno - Visualize missing data patterns.
  • Vizro - Low-code toolkit for building data visualization apps.
  • Yellowbrick - Visual diagnostic tools for machine learning.
  • Great Tables - Create awesome display tables using Python.
  • DataMapPlot - Create beautiful plots of data maps.
  • Datashader - Quickly and accurately render even the largest data.
  • PandasAI - Conversational data analysis using LLMs and RAG.
  • Mito - Jupyter extensions for faster code writing.
  • D-Tale - Interactive GUI for data analysis in a browser.
  • Pandasgui - GUI for viewing and filtering DataFrames.
  • PyGWalker - Interactive UIs for visual analysis of DataFrames.
  • QGrid - Interactive grid for DataFrames in Jupyter.
  • Pivottablejs - Interactive PivotTable.js tables in Jupyter.

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Data Quality & Validation

  • PyOD - Outlier and anomaly detection.
  • Alibi Detect - Outlier, adversarial and drift detection.
  • Pandera - Data validation through declarative schemas.
  • Cerberus - Data validation through schemas.
  • Pydantic - Data validation using Python type annotations.
  • Dora - Automate EDA: preprocessing, feature engineering, visualization.
  • Great Expectations - Data validation and testing.

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Feature Engineering & Selection

  • FeatureTools - Automated feature engineering.
  • Feature Engine - Feature engineering with Scikit-Learn compatibility.
  • Prince - Multivariate exploratory data analysis (PCA, CA, MCA).
  • Fitter - Figures out the distribution your data comes from.
  • Feature Selector - Tool for dimensionality reduction of machine learning datasets.
  • Category Encoders - Extensive collection of categorical variable encoders.
  • Imbalanced Learn - Handling imbalanced datasets.

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Specialized Data Tools

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

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🗃️ SQL & Databases

Resources

SQL tutorials and database design principles.

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Databases

Popular open-source database systems for a variety of use cases and data models.

  • SQLite – Embedded, file‑based SQL engine, the most used database in the world.
  • PostgreSQL – Advanced open‑source relational database with extensibility and SQL compliance.
  • MySQL – Widely used relational database management system (Oracle’s community version).
  • MariaDB – Community developed fork of MySQL with enhanced features.
  • DuckDB – In‑process analytical database designed for fast OLAP queries.
  • ClickHouse – Columnar database for real‑time analytics on large datasets.
  • Apache Cloudberry - A mature open-source MPP database evolved from Greenplum with a newer PostgreSQL kernel.
  • TimescaleDB – Time‑series SQL database built on PostgreSQL.
  • TDengine – Time‑series database for IoT, vehicles, and industrial monitoring.
  • MongoDB – Document database with a flexible, JSON‑like data model.
  • Cassandra – Highly scalable, distributed NoSQL database for large‑scale deployments.
  • Redis – In‑memory data structure store used as database, cache, and message broker.
  • CockroachDB – Distributed SQL database with strong consistency and horizontal scaling.
  • Neo4j – Native graph database for connected data and relationships.
  • InfluxDB – Purpose‑built time‑series database for metrics and events.
  • ScyllaDB – Drop‑in Cassandra alternative written in C++ for higher performance.
  • Apache HBase – Distributed, scalable, big data store modeled after Google's Bigtable, running on top of HDFS.

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Tools

A collection of libraries and drivers for seamless database access and interaction.

  • PyODBC - Python library for ODBC database access.
  • SQLAlchemy - SQL toolkit and ORM for Python.
  • Psycopg2 - PostgreSQL database adapter.
  • MySQL Connector/Python - MySQL driver for Python.
  • PonyORM - ORM for Python with dynamic query generation.
  • PyMongo - Official MongoDB driver for Python.
  • SQLiteviz - A tool for exploring SQLite databases and visualizing the results of your queries.
  • DB Browser for SQLite - A high quality, visual, open source tool to create, design, and edit database files compatible with SQLite.
  • DBeaver - A free universal database tool and SQL client for developers, SQL programmers, and administrators.
  • Beekeeper Studio - 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.
  • SQLFluff - A modular SQL linter and auto-formatter designed to enforce consistent style and catch errors in SQL code.
  • PyMySQL - A pure-Python MySQL client library for interacting with MySQL databases from Python applications.
  • Vanna.AI - An AI-powered tool for generating SQL queries from natural language questions.
  • SQLChat - A chat-based SQL client that allows you to query databases using natural language conversations.
  • Records - SQL queries to databases via Python syntax.
  • Dataset - JSON-like interface for working with SQL databases.
  • SQLGlot - A no-dependency SQL parser, transpiler, and optimizer for Python.

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📊 Data Visualization

Resources

Color theory, chart selection guides, and storytelling tips.

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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.
  • Altair - 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.
  • Bqplot - A plotting library for IPython/Jupyter notebooks.
  • PyPalettes - A large (+2500) collection of color maps for Python.
  • Deck.gl - A WebGL-powered framework for visual exploratory data analysis of large datasets.
  • Python for Geo - Contextily: add background basemaps to your plots in GeoPandas.
  • OSMnx - A package to easily download, model, analyze, and visualize street networks from OpenStreetMap.
  • Apache ECharts - A powerful, interactive charting and visualization library for browser-based applications.
  • VisPy - A high-performance interactive 2D/3D data visualization library leveraging the power of OpenGL.
  • Glumpy - A Python library for scientific visualization that is fast, scalable and beautiful, based on OpenGL.
  • Pandas-bokeh - Bokeh plotting backend for Pandas.
  • QGIS - Free, open source, cross-platform geographic information system (GIS).
  • Flourish - Platform for creating interactive data visualizations and stories without coding.

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📈 Dashboards & BI

Resources

Ttutorials for building and enhancing dashboards and visualizations using various tools and frameworks.

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Tools

Frameworks for building custom dashboard solutions.

  • Dash - Framework for creating interactive web applications.
  • Streamlit - Simplified framework for building data applications.
  • Panel - Python library for creating custom interactive web apps and dashboards.
  • Gradio - Tool for creating and sharing machine learning applications.
  • OpenSearch Dashboards - A powerful data visualization and dashboarding tool for OpenSearch data, forked from Kibana.
  • GridStack.js - A library for building draggable, resizable responsive dashboard layouts.
  • Tremor - A React library to build dashboards fast with pre-built components for charts, KPIs, and more.
  • Appsmith - An open-source platform to build and deploy internal tools, admin panels, and CRUD apps quickly.
  • Grafanalib - A Python library for generating Grafana dashboards configuration as code.
  • H2O Wave - A Python framework for rapidly building and deploying realtime web apps and dashboards for AI and analytics.
  • Shiny for Python - Python version of the popular R Shiny framework.
  • Voilà - Turn Jupyter notebooks into standalone web applications.
  • Reflex - Full-stack Python framework for building web apps.
  • Taipy - Python library for building web applications and interactive dashboards.
  • Evidence - Business intelligence platform that uses SQL and Markdown for reports.

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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 open-source BI tool.
  • Apache Superset - Open-source data exploration and visualization platform.
  • Preset - A platform for modern business intelligence, providing a hosted version of Apache Superset.
  • Metabase - The simplest way to get analytics and business intelligence for everyone in your company.
  • Redash - Tool for visualizing and sharing data insights.
  • Grafana - Dashboarding and monitoring tool.
  • Datawrapper - 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.
  • Rath - Next-generation automated data exploratory analysis and visualization platform.
  • Kibana - The official visualization and dashboarding tool for the Elastic Stack (Elasticsearch, Logstash, Beats).

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🕸️ Web Scraping & Crawling

Resources

A collection of valuable resources, tutorials, and libraries for web scraping with Python.

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Tools

A list of libraries and tools for web scraping.

  • Requests - A simple, yet elegant, HTTP library for Python.
  • BeautifulSoup - A library for parsing HTML and XML documents.
  • Selenium - A tool for automating web applications for testing purposes.
  • Scrapy - An open-source and collaborative web crawling framework for Python.
  • Browser Use - A library for browser automation and web scraping.
  • Gerapy - Distributed Crawler Management Framework based on Scrapy, Scrapyd, Django, and Vue.js.
  • AutoScraper - A smart, automatic, fast, and lightweight web scraper for Python.
  • Feedparser - A library to parse feeds in Python.
  • Trafilatura - A Python & command-line tool to gather text and metadata on the web.
  • You-Get - A tiny command-line utility to download media contents (videos, audios, images) from the web.
  • MechanicalSoup - A Python library for automating interaction with websites.
  • ScrapeGraph AI - A Python scraper based on AI.
  • Snscrape - A social networking service scraper in Python.
  • Ferret - A web scraping system that lets you declaratively describe what data to extract using a simple query language.
  • Grab - A Python framework for building web scraping apps, providing a high-level API for asynchronous requests.
  • Playwright - Python version of the Playwright browser automation library.
  • PyQuery - A jQuery-like library for parsing HTML documents in Python.
  • Helium - High-level Selenium wrapper for easier web automation.
  • Scrapling - A framework for building web scrapers and crawlers.
  • Crawl4AI - Advanced web crawling framework designed for AI and data extraction tasks.

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🔢 Mathematics

A collection of resources for learning mathematics, particularly in the context of data science and machine learning.

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🎲 Statistics & Probability

Resources

A selection of resources focused on statistics and probability, including tutorials and comprehensive guides.

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Tools

A collection of tools focused on statistics and probability.

  • SciPy - Fundamental library for scientific computing and statistics.
  • Statsmodels - Statistical modeling, testing, and data exploration.
  • PyMC - A probabilistic programming library for Python that allows for flexible Bayesian modeling.
  • Pingouin - Statistical package with improved usability over SciPy.
  • scikit-posthocs - Post-hoc tests for statistical analysis of data.
  • Lifelines - Survival analysis and event history analysis in Python.
  • scikit-survival - Survival analysis built on scikit-learn for time-to-event prediction.
  • Bootstrap - Bootstrap confidence interval estimation methods.
  • PyStan - Python interface to Stan for Bayesian statistical modeling.
  • ArviZ - Exploratory analysis of Bayesian models with visual diagnostics.
  • PyGAM - A Python library for generalized additive models with built-in smoothing and regularization.
  • NumPyro - A probabilistic programming library built on JAX for high-performance Bayesian modeling.
  • Causal Impact - A Python implementation of the R package for causal inference using Bayesian structural time-series models.
  • DoWhy - A Python library for causal inference that supports explicit modeling and testing of causal assumptions.
  • Patsy - A Python library for describing statistical models and building design matrices.
  • Pomegranate - Fast and flexible probabilistic modeling library for Python with GPU support.
  • Pgmpy - Python library for probabilistic and causal inference using graphical models.

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🧪 A/B Testing

A collection of resources focused on A/B testing.

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⏳ Time Series Analysis

Resources

A collection of resources for understanding time series fundamentals and analytical techniques.

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Tools

A collection of tools for working with temporal data.

  • Facebook Prophet - A procedure for forecasting time series data based on an additive model.
  • Uber Orbit - A Python package for Bayesian time series forecasting and inference.
  • sktime - A unified Python framework for machine learning with time series, compatible with scikit-learn.
  • GluonTS - A Python toolkit for probabilistic time series modeling, built on MXNet.
  • Time-Series-Library - A library for deep learning-based time series analysis and forecasting.
  • TimesFM - A pretrained time series foundation model from Google Research for zero-shot forecasting.
  • PyTorch Forecasting - A PyTorch-based library for time series forecasting with neural networks.
  • Time-series-prediction - A collection of time series prediction methods and implementations.
  • PlotJuggler - A tool to visualize and analyze time series data logs in real-time.
  • TSFresh - Automatically extracting features from time series data.
  • pmdarima - Python library for ARIMA modeling and time series analysis.
  • Kats - Toolkit for analyzing time series data from Facebook Research.

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⚙️ Data Engineering

Resources

A collection of resources to help you build and manage robust data pipelines and infrastructure.

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Tools

A collection of tools for building, deploying, and managing data pipelines and infrastructure.

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

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📖 Natural Language Processing (NLP)

Resources

A selection of resources for learning and applying natural language processing in Python.

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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 open-source software library for advanced NLP in Python.
  • BERT - A transformer-based model for NLP tasks.
  • Flair - A simple framework for state-of-the-art NLP.
  • OpenHands - A library and framework for building applications with large language models.
  • Stanford CoreNLP - A Java suite of core NLP tools providing fundamental linguistic analysis capabilities.
  • John Snow Labs Spark-NLP - A state-of-the-art Natural Language Processing library built on Apache Spark.
  • TextAttack - A Python framework for adversarial attacks, data augmentation, and model training in NLP.
  • Gensim - Topic modeling and natural language processing library for Python.
  • Stanza - Python NLP library for many human languages, from the Stanford NLP Group.
  • SentenceTransformers - Framework for state-of-the-art sentence and text embeddings.
  • LangExtract - Google's library for structured information extraction from text using language models.
  • Rasa - Open-source framework for building contextual AI assistants and chatbots.

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🤖 Machine Learning & AI

Resources

A collection of resources to help you learn and apply machine learning concepts and techniques.

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Evidence88/100medium confidence
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