Top Data Science Geek to follow on GitHub
How to use and learn Data Science tools and techniques from these GitHub account?
- Create your account on GitHub
- Decide on what you want to start learning (Ex: Visualization or Machine Learning)
- Follow people who contributes on Visualization
- Clone the codes in your local machine, try and understand it
- 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 is 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
- Brian Caffo (John Hopkins University)
- Roger D Peng (John Hopkins University)
- Hilary Mason (Chief Data Scientists at Bitly)
- Wes McKinney (Author of Python for Data Analysis)
- Cameron Davidson Pilon (Python, Algorithms)
- Thomas Wiecki (Python, Bayesian Analysis)
- Julia Evans (Machine Learning, Python)
- Randy Olson (Python – Data Analysis, Matplotlib, Bokeh)
- Prakhar Srivastav (Python, Algorithms)
- Jason Davies (D3, Data Visualization)
- Jake Vanderplas (Machine Learning, Data Visualization)
- Justin Palmer (D3, Data Visualisation)
- Mike Bostock (D3, Data Visualisation)
- Sebastian Raschka (Machine Learning, Data Visualization)
- Pete Skomoroch (Machine Learning, Big Data, Python)
- Andreas Mueller (Machine Learning, Python)
- Gael Varoquaux (Machine Learning, Statistics, Python)
Analytics, Statistics & Algorithms
- Hadley Wickham (Statistics, Data Analysis, Data Visualisation)
Big Data & Spark
Artificial Intelligence and Deep Learning
- Andrej (Deep Learning, Neural Network, SVM)
- Micheal Nielsen (Neural Networks, Deep Learning)
- Mathieu Blondel (Machine Learning, Neural Networks)
- Oliver Grisel (Machine Learning, Deep 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