Hands-On Prescriptive Analytics

Chapter 3. Python Essentials

I focus on applications in this book. These are restricted to the business domain reflecting my background, but the ideas and concepts can be applied equally well to the public domain. So you should broadly interpret the word “application.”

Regardless of the domain, software is needed for applications since computers are used everywhere in our modern technology-driven world and are run by software. To do any credible analytical work as a data scientist, you not only need software capable of doing it, but you also need an understanding of how to use it.

My software of choice is Python. I will discuss the reasons for this in “Python Structure: Overview”. Then, I will review the Python language in the remainder of this chapter. The leading questions for this chapter are:

1. Why use Python for data science?

2. What are Python’s advantages?

3. What are packages?

4. How do you use packages in Python?

5. What is Best Practices?

6. What are data containers in Python?

7. What are the main data types and containers in Python?

8. How do you process data in Python?

9. What are some key Python functions and their use in data science?

10. How can you create your own Python functions?

11. What is the most efficient way to access and use Python for data science?

Since I use Python in this book, this chapter will provide you with some basic material to get you started. Many of the Python tools discussed here will be illustrated throughout this book. I will provide references ...