Nathan George - Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data

"Better than any statistician at computer science and better at statistics than any computer scientist" this is a phrase I've heard said about data scientists since I started my official data science training. It might be true, but data science has grown to incorporate so many different fields and technologies that it might not be able to be captured with such a simple statement anymore. Not to mention that statistics, and especially computer science, cover a lot of ground, too. But as a quick-and-dirty way to describe data science in three words, "statistics + computer science" works.

Many people learn data science to improve their lives. For me, I wanted to transition out of the physical sciences, which are bound by physical locations, and have more freedom to travel around the world. Working in a digital space like data science allows for that, while high-tech manufacturing doesn't. For others, the increase in pay is alluring. For many of us, we see the stories about data scientists being happy and highly paid and are immediately interested in learning more. Some people learn data science due to their intellectual curiosity and the fun of it. In any case, if you want to be a data scientist, you'd better enjoy working with computers and data!

I wrote this book for a few reasons, and one good reason to create teaching materials or even teach courses is you will learn the materials better by teaching it. So, one thing I'd recommend doing if you want to really learn is to create some teaching materials. An easy way to do this is to write a blog post about using data science to solve a problem. It could be any dataset from Kaggle, for example, or some data you've got access to and are allowed to share.

In the book, we use Python to carry out data science. However, there are a plethora of tools for doing data science, so don't feel like Python is the only way. There is a debate among data scientists whether or not a data scientist must be able to program. On the one hand, being able to code enables us to use cutting-edge tools and integrate into other software products more easily.

On the other hand, not all data science work is the same, and some doesn't have to be done with code. Many people doing data science use R and other tools (such as GUIs) to carry out their work. However, Python seems to be the top choice and integrates nicely into software stacks at companies. Python, like any other skill, requires practice and dedication to master. This book is here to get you started, and I hope you have fun learning Python and data science and are excited to continue your data science journey well beyond this book.

This book is for people who want to get into data science, perhaps from a different career or background (even non-technical backgrounds). The book is intended for beginner to intermediate levels of Python and data science. Some examples of people who might find this book useful are:

• Students starting or about to start a data science, analytics, or related program (for example, a Bachelor's, Master's, bootcamp, or online courses)
• Recent college graduates or college students (all levels) who want to learn something to set them apart in the job market
• Employees of companies who need or want to learn data science and machine learning techniques with Python
• People who want to shift their career toward data science and are just beginning their transition into data science

Part I, An Introduction and the Basics

Chapter 1, Introduction to Data Science, gives an overview of data science, including the history, top skills and tools used in the field, specializations and related fields, and best practices for data science projects.

Chapter 2, Getting Started with Python, explains installing Python and Python distributions (specifically, Anaconda), editing and running code with code editors, IPython, Jupyter Notebooks, basic use of the command line, installing Python packages and using virtual environments, Python programming basics, how to deal with errors and use documentation, and software engineering best practices (including Git and GitHub).