As a programmer you're spoilt for choice when it comes to programming languages. You can choose from any of a dozen really powerful languages to get the job done.
When it comes to Data Science programming though, there are really only 2 choices - Python and R.
Sure, there are lots of other languages that are useful and can be used to supplement your choice, but primarily you're going to be programming in Python or R.
I'm not going to go too much into the pros and cons of each in this post. Rather, if you've made your choice and decided you're going to go with the snake, then you can enjoy a number of benefits:
If you're not sure how to get started with Python, the 3 books in this blog post will help you make your first steps.
Disclosure: the three books in this post link you to the listed book at your local Amazon store.
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In this post - the 4th in a series of 8 in which we bring you 21 Inspirational Books for All Aspiring Data Scientists, we highlight 3 books to introduce you to the Python programming language and how it is being used in Data Science:
They are all highly recommended reading and will get your Python skills from zero to hero in no time...
3 Essential Python Books for Aspiring Data Scientists @eelrekab @chi2innovations #datascience #python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization.
Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns.
You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific
All 8 posts in the series: