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Top Data Science Learning Resources On Github For Beginners & Experts


Top Data Science Learning Resources On Github For Beginners & Experts


 



Github has not only been the go-to platform for releasing and developing state-of-the art Data Science tools but also a very familiar platform amongst the community as a guide for free learning sources.

Here is a list of Github repositories which listed in accordance with the star rating of the users on Github. And are also an amalgamation of Data science resources which would appeal to both the set of audience — beginners as well as experts:

Data-science-ipython-notebooks – A Compilation of Python Tutorials

Topics covered:

Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Stars: 15,056

Check here

The Open-Source Data Science Masters – Libraries and Tutorials in Python

The open-source curriculum for learning Data Science covers rudimentary concepts in both theory and technologies.

Topics covered:

Data Analysis

Python

R

Stars: 11,723

Check here

Awesome Data Science – A One Stop Source To Blogs And Podcasts

This is an open source Data Science repository to learn and apply towards solving real world problems using Data science.

Topics covered:

Why Data science, Data science colleges, MOOCs, Datasets, Podcasts and Blogs.

Stars: 9,484

Check here

Stanford-Tensorflow-tutorials – Assignments and Sample Code of the Main Course

This repository contains code examples for the course CS 20: TensorFlow for Deep Learning Research.

Stars: 8,204

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Data science Blogs –  A Collection Of Top Blogs In Alphabetical Order

This repository contains a curated list of top data science blogs which are available for free

Stars: 4,581

Check here

Deep Learning Specialization on Coursera – A Compilation of Code-based Quiz questions From the main Course

This repo contains all the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera.

Topics covered:

Keras, TensorFlow, Optimisation methods, Gradient checking, Step-by-step guide to building a neural network

See Also

Stars:3,493

Check here

Spark Notebook –  A Quick Start Guide to Spark

The Spark Notebook is the open source notebook aimed at enterprise environments, providing Data Scientists and Data Engineers with an interactive web-based editor that can combine Scala code, SQL queries, Markup and JavaScript in a collaborative manner to explore, analyse and learn from massive data sets.

Topics covered:

Spark, Scala

Stars: 2,721

Check here

Creative Applications of Deep Learning with Tensorflow – A Collection of Lecture Transcripts, Assignments and a Guide to Installation of Packages

This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow.

Topics covered:

TensorFlow, CUDA/GPU instructions

Stars: 1,269

Check here

 


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