Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Quantitative versus qualitative data


When you ask a data scientist, "what type of data is this?", they will usually assume that you are asking them whether or not it is mostly quantitative or qualitative. It is likely the most common way of describing the specific characteristics of a dataset.

For the most part, when talking about quantitative data, you are usually (not always) talking about a structured dataset with a strict row/column structure (because we don't assume unstructured data even has any characteristics). All the more reason why the preprocessing step is so important.

These two data types can be defined as follows:

  • Quantitative data: This data can be described using numbers, and basic mathematical procedures, including addition, are possible on the set.

  • Qualitative data: This data cannot be described using numbers and basic mathematics. This data is generally thought of as being described using "natural" categories and language.

Example – coffee shop data

Say that we were processing...