Book Image

Principles of Data Science - Second Edition

By : Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi
Book Image

Principles of Data Science - Second Edition

By: Sinan Ozdemir, Sunil Kakade, Marco Tibaldeschi

Overview of this book

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas. Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.
Table of Contents (17 chapters)
16
Index

Summary

In this chapter, we took a look at some basic mathematical principles that will become very important as we progress through this book. Between logarithms/exponents, matrix algebra, and proportionality, mathematics clearly has a big role not just in the analysis of data, but in many aspects of our lives.

The upcoming chapters will take a much deeper dive into two big areas of mathematics: probability and statistics. Our goal will be to define and interpret the smallest and biggest theorems in these two giant fields of mathematics.

It is in the next few chapters that everything will start to come together. So far in this book, we have looked at math examples, data exploration guidelines, and basic insights into types of data. It is time to begin to tie all of these concepts together.