How to Become a Data Scientist

How to use this instruction:

This instruction collected a series of online courses and books that can help you to advance your knowledge in this area without wondering around about the sources you need to study.

The first part of instruction is designed for thr people with a itroductary background in statistics and programming (e.g. Math and Engineering Graduates).

The second part in for the people who wants to have seeper understanding of mathematics of Machine Learning.

The third part is devoted to the more advanced tools like Deep Learning, RNN, CNN, XGBoost, and etc.

I rated the sources difficulties from 1- easy to 5 -hard.

Note 1:

If you do not have any cs background I highly recommend to take look at a Data Structure and Algorithm course and if you dont have a statuscal backgrond an introductry course in statistics can be very helpful.

Note 2:

The hyperlinks are subjected to changes, but you can find any of them by just googling the title easily.

1. Introduction to Data Science:

Online Courses:

• Coursera: Data Science 10-course specilization, offered by john hopkins university - link (1-3)
  
• YouTube: This course related to An Introduction to Statistical Learning book - link (2-4)
  
• Coursera: Machine Learning offered by Stanford University - link (2.5)
  
• edx: The Analytics Edge - link (3)

Books:

• An Introduction to Statistical Learning - pdf (3)
  
• Data Science for Business - link (2-3)
  

2. Understanding Theory (You can skip this part)

Online Courses:

• YouTube: Understanding Machine Learning - by Shai Ben-david - link (4)
  
• YouTube: Statistical Learning- Classification - by Ali Ghodsi - link (3-4)
  

Books:

• The Elements of Statistical Learning - pdf (4)
  
• Understanding Machine Learning: From Theory to Algorithms - pdf (4)
  
• Pattern Recognition and Machine Learning - link (4)
  

3. Advanced Tools

• YouTubeDeep Learning - by Ali Ghodsi - link (4)
  
• YouTube: Convolutional Neural Networks for Visual Recognition - link (3-4)
  
• Blog: colah's blog - link (3-4)
  
• Course Material: Deep Learning by Bhiksha Raj - link (4)
  

Good to know:

• Learn either python or R or both
• Learn C++ if you can
• Keep updated in the cummunity, thses are some links:
  • http://www.kdnuggets.com/
  • https://www.reddit.com/r/machinelearning
• Look for competitions, again some links:
  • https://competitions.codalab.org/
  • https://www.kaggle.com/