All that answers the question “*What is data science and why should I study it?”* is contained in this section. There are links to tutorials, exercises, software, infographics and practical guides on the world of data science.

### – Infographic

- Becoming a data scientist in 8 steps – An infographic by Datacamp on how to become data scientists in 8 easy steps.
- Curriculum via Metromap – The perfect curriculum of a data scientist in a nice metroline map style.
- Data scientist skillset – The basic knowledge of a data scientist divided into various categories.
- Modern data scientist – A little cheat sheet on who the modern data scientist really is.
- R vs Python – Pros and cons of the two major programming languages used in data science (R and Python).
- Statistics vs Machine Learning – What to choose between statistics and machine learning techniques depending on what problem to solve.
- The Data Science Industry – An infographic by Datacamp on key roles and positions in modern data science industry.

### – Where to learn Data Science?

- Awesome Data Science – An open source github repository to learn and apply towards solving real world problems.
- Data Science Central – The online resource for data science practitioners.
- GoDataDriven Blog – The blog of DataDriven Company.
- The Open Source Master to Data Science – The open source course to data science.

### – Python Resources

- A Byte of Python – A free book on programming using the Python language.
- Automate the Boring Stuff with Python – This teaches how to code small, practical programs to automate tasks on their computer.
- Getting started with Python – A guide for beginners from the Python Software Foundation.
- Google’s Python Class – A free class for people with a little bit of programming experience who want to learn Python.
- Hands-on Python Tutorial – A Python tutorial created by Dr. Andrew N. Harrington of Computer Science Department of Loyola University (Chicago).
- How to think like a Computer Scientist – A basic book to start programming in Python, reached its 3rd edition.
- Instant Python – This is a minimal crash-course in the programming language Python.
- Lear Python the Hard Way – One of the most famous online course of Python, reached its 3rd edition.
- Problem Solving with Algorithms and Data Structures – A book written for people who wants to learn Python in a practical way.
- Programming with Python – A series of Python lessons written by Software Carpentry.
- SoPython – The website of Stack Overflow Python community.
- The Hitchhiker’s Guide to Python! – This handcrafted guide exists to provide both novice and expert Python developers a best practice handbook.

### – R Resources

- A Complete tutorial to learn Data Science in R from Scratch – Tutorial created by Analytics Vidhya on basic data science with R.
- An introduction to R – Written by the R core team, it’s a must read for everyone who wants to learn this language.
- Cookbook for R – The goal of this book is to provide solutions to common tasks and problems in analyzing data.
- Programming with R – A series of R lessons written by Software Carpentry.
- R for Beginners – Written by Emmanuel Paradis, gives a starting point for people newly interested in R.
- R for reproducible Scientific Analysis – A series of R lessons to teach novice programmers to write modular code using R for data analysis written by Software Carpentry.
- TryR – A series of R lessons created by O’Reilly and Code School.

### – Big Data and Hadoop

- Understanding Big Data – PDF written by IBM about analytics for enterprise class Hadoop and streaming data.