Note: The following repositories are not ordered in form of any ranking!!!
- data-science-ipython-notebooks
The repository is a compilation of data science Python notebooks which including resources where you can learn about libraries for data analysis such as Matplotlib, pandas, NumPy, and SciPy. A corpus of machine learning algorithms using Scikit-learn. Not only that it also has exercises for deep learning using TensorFlow, Theano, Caffe, and Keras modules.
A highly recommended repository if you are looking to get started with Data Science.
2. OpenDS4All
OpenDS4All is an initiative that aims to speed up the development of data science curriculum at academic institutions. It has assembled slides presentation, sample Jupyter notebooks and loads of other additional resources for designing, modifying, and delivering data science and data engineering education. The README file provides a set of instructions to be followed while accessing these resources.
3. awesome-datascience
This repository has one of the top collection of resources on data science. It has links for a lot of tutorials, courses, necessary modules for data science, journals, publications, magazines, presentations, books, and even podcasts. It is like having a portal which would lead you to where you want to go.
4. data-science-blogs
If you are into reading blogs, this repository can come handy to you. The repository has curated list of popular data science blogs arranged in alphabetical order which provide an in-depth knowledge about the topic. The repo is also open for contributions, so if you want to share your own blogs, you can do that as well.
5. LearnDataScience
This particular repository concentrates of ML algorithms such as Linear Regression, Logistic Regression, Random Forests, K-Means Clustering. Here you will find Jupyter notebooks on the ML algorithms as well as the datasets used in them. This could come in handy if you want to practice or hone your own skill set.