Data Science Resources

A curated list of resources to help you get started with Data Science.

Inspired by awesome-python.

• Data Science Resources
  • Prerequisites
  • Websites
  • MOOC
  • Ebooks
  • Frameworks and Libraries
  • Blogs
  • Social-Network
  • Competitions
  • Datasets
  • Newsletter

Prerequisites

• Matrix Calculus you need for Deep Learning
  • Paper - Arvix

Websites

• Numfocus - Open Code, Better Science
• Arvix Sanity Preserver - Top arxiv's paper all time
• Interactive Machine Learning - Interactive Machine Learning, Deep Learning and Statistics

MOOC

• Machine Learning course - Andrew Ng (Stanford) - Coursena
• Deep Learning course - Andrew Ng (Stanford) - Coursena
  • Youtube
• CS231 - CNN for Visual Recognition (Stanford)
• CD224d/n - NLP with Deep Learning (Stanford)
• Statistics and probability - Khanacademy
• Fast.ai - Deep Learning for Coders
• Bloomberg - Foundations of Machine Learning
• Machine Learning crash course - Google

Ebooks

• Data Science Handbook - Numpy, Pandas, Matplotlib, Machine Learning with Scikit-learn.
• Speech and Language Processing - Stanford (Draft Version)
• Neural Networks and Deep Learning - Deep Learning tutorial
• Deep Learning book
  • Deep Learning book - Vietnamese Version
• An Introduction to Statistical Learning - with Applications in R
• An Elements of Statistical Learning
• Think Stats - Probability and Statistics for Programmers
• Think Bayes - Bayesian Statistics Made Simple
• Introduction to Probability - Massachusetts Institute of Technology (PDF Version)
• Cookiecutter Data Science - Cookiecutter Data Science
• Data Science at the Command Line - Data Science at the Command Line

Frameworks and Libraries

• Scikit-learn - Machine Learning
• Scikit-image - Image Processing
• Numpy - Scientific Computing
• Scipy - Numerical Integration and Optimization
• Pandas - Data Analysis Toolkit
• Matplotlib - Data Visualization
  • Seaborn
  • Bokeh
• Sympy - Symbolic Mathematics
• Jupyter - Interactive Computing, Research and Education
• Tensorflow - Deep Learning (Google)
• Pytorch - Deep Learning (Facebook)
• Keras - Deep Learning lib for Human (High-level API)
• xgboost / LightGBM / catboost - Gradient Boosting
  • xgboost
  • LightGBM
  • catboost
• NLTK - NLP
• Gensim - Topic Modeling
• Librora - Audio and Music Analysis
• Featuretools - Automated Feature Engineering
• Scrapy - Web Scraping
• NetworkX - Graphs and Networks
• Shogun - Machine Learning Toolbox (C++)
• Annoy - Spotify - Approximate Nearest Neighbors (for NOT-SPARSE data)
• Pysparnn - Facebook - Approximate Nearest Neighbors (for SPARSE data)
• Faiss - Facebook - Similarity search and clustering (Dense Vectors)

Blogs

• Machine Learning cơ bản - Tiep Vu Huu
• Medium - Toward Data Science
• Medium - InsightData
• OpenAI
• WildML
• Distill.pub
• Colab's blog
• Lilianweng's blog
• Ruder's blog
• Echen's blog

YouTube Videos & Channels

• Giant Neural Network - Introduction to Neural Network
• 3Blue1Brown - Introduction to Neural Network
• Two Minute Papers - Paper review channel
• Deep Learning dot AI - Deep Learning course by Andrew Y. Ng (Stanford)
• Hvass Laboratories - Tensorflow Tutorial
• Code Bullet - Reinforcement Learning
• DeepMind

Social Network

• Twitter
  • 50 AI Influencers to follow on Twitter - link pdf
• Reddit
  • /r/MachineLearning
• Medium
  • Towards Data Science - Sharing concepts, ideas, and codes
• Quora

Roadmap

• Data Science - HasBrain

Competitions

• Kaggle.com - Winning solutions of kaggle competitions

Datasets

• Fivethirtyeight
  • Github

Newsletter

• DataMachina
• Analytic Vidhya
• Data Elixir
• AI Weekly
• Sebastian Ruder
• Mybridge
• Python Weekly

to be continued ...