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the official repository for intro to Data Science course held by TAU spring2019

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Introduction to Data Science

python refresher materials

python resources python is an extremely popular open source high level general purpose programming language. there are many online resources for learning python. Most of which teach python from the perspective of the software engineer, teaching various use cases such as web development, ETL processes, etc. for our purposes it is better to focus on material that tech python from the Data Science perspective (gathering and loading data, manipulating data, visualizations, ML, DL etc.) following is a short list of good materials, some completely free, and others provide some kind of trial period. You are encouraged to search the web for additional resources.

python data-science handbook

free o’reilly textbook with code examples in github https://jakevdp.github.io/PythonDataScienceHandbook/

towards data-science

lots of free materials and blogposts for data science beginners https://towardsdatascience.com/python-for-data-science-from-scratch-part-i-390f01d91748

data camp

high quality moocs for learning data science and programming. some courses and materials are free but most aren’t http://www.datacamp.com

coursera

many moocs from various institutions some courses are free, some provide a free trial one relevant course: https://www.coursera.org/learn/python-for-applied-data-science?action=enroll&authMode=signup

intro notebooks

If you need a python refresher, see: misc/intro2python.

The recommended order of intro notebook is:

  1. basics
  2. input_output
  3. numpy
  4. pandas
  5. plotting

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