Top Data Science Geek to Follow on GitHub

Top Data Science Geek to follow on GitHub
How to use and learn Data Science tools and techniques from this GitHub account?

  1. Create your account on GitHub
  2. Decide on what you want to start learning (Ex: Visualization or Machine Learning)
  3. Follow people who contribute on Visualization
  4. Clone the codes in your local machine, try and understand it
  5. Once you start executing it, you start learning it

Importance of GitHub Account
GitHub engagement helps a lot in terms of learning new things and keeping yourself updated. The most important prospects of this are you can contribute to open source community and this gets validated and rips fruit in long run.
If you’re strictly doing analytics and Data Science, a GitHub account won’t necessarily prove you have the skills that a company is looking for. However, it does show that you can build things, so if you’re applying for something more along the lines of data engineering it could definitely help.

University Professors & Authors

  1. Brian Caffo               (John Hopkins University)
  2. Roger D Peng          (John Hopkins University)
  3. Hilary Mason           (Chief Data Scientists at Bitly)
  4. Wes McKinney        (Author of Python for Data Analysis)


  1. Cameron Davidson Pilon     (Python, Algorithms)
  2. Mike Dewar              (Python, D3, Javascript)
  3. Thomas Wiecki        (Python, Bayesian Analysis)
  4. Julia Evans               (Machine Learning, Python)
  5. Randy Olson             (Python – Data Analysis, Matplotlib, Bokeh)
  6. Prakhar Srivastav    (Python, Algorithms)

R Programming

  1. Romain Francois     (R Programming)
  2. Jeff L                          (R Programming, Data Analysis)

Data Visualization

  1. Jason Davies           (D3, Data Visualization)
  2. Jake Vanderplas     (Machine Learning, Data Visualization)
  3. Justin Palmer          (D3, Data Visualisation)
  4. Mike Bostock           (D3, Data Visualisation)

Machine Learning

  1. Sebastian Raschka  (Machine Learning, Data Visualization)
  2. Pete Skomoroch      (Machine Learning, Big Data, Python)
  3. Andreas Mueller     (Machine Learning, Python)
  4. Gael Varoquaux      (Machine Learning, Statistics, Python)

Analytics, Statistics & Algorithms

  1. Hadley Wickham    (Statistics, Data Analysis, Data Visualisation)

Big Data & Spark

  1. Davies Liu                 (Apache Spark, Python)
  2. Mohammad Sajid    (Big Data, Python)

Artificial Intelligence and Deep Learning

  1. Heather Arthur         (Neural Network, Javascript)
  2. Andrej                         (Deep Learning, Neural Network, SVM)
  3. Micheal Nielsen        (Neural Networks, Deep Learning)
  4. Mathieu Blondel       (Machine Learning, Neural Networks)
  5. Oliver Grisel               (Machine Learning, Deep Learning)


  1. Stefan Karpinski      (Julia)
  2. John Myles White   (Julia, Machine Learning)

If you want to progress in your data science journey all you have to do is, choose your category and follow the learning diligently.

If you have any questions, doubts or suggestions drop in your comment below and we will be happy to answer them. If you want to make your own learning path share it with us how are you planning to follow your journey of becoming a data scientist and we will love to amend that here

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