data-science

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

Apache Superset is a Data Visualization and Data Exploration Platform

scikit-learn: machine learning in Python

Deep Learning for humans

The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

Learn how to develop, deploy and iterate on production-grade ML applications.

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Streamlit — A faster way to build and share data apps.

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Data Apps & Dashboards for Python. No JavaScript Required.

matplotlib: plotting with Python

Prefect is a workflow orchestration framework for building resilient data pipelines in Python.

A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.

An orchestration platform for the development, production, and observation of data assets.

The backtesting engine that gives you an unfair advantage. Test thousands of trading ideas in the time others test one.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

A curated list of awesome Jupyter projects, libraries and resources

150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data

Algorithmic Trading in Python with Machine Learning

Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet

Python Client for Supabase. Query Postgres from Flask, Django, FastAPI. Python user authentication, security policies, edge functions, file storage, and realtime data streaming. Good first issue.