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

Why look at these distinctions?

It might seem worthless to stop and think about what type of data we have before getting into the fun stuff, such as statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.

The same principle applies to data science. When given a dataset, it is tempting to jump right into exploring, applying statistical models, and researching the applications of machine learning in order to get results faster. However, if you don't understand the type of data that you are working with, then you might waste a lot of time applying models that are known to be ineffective with that specific type of data.

When given a new dataset, I always recommend taking about an hour (usually less) to make the distinctions mentioned in the following sections.