Book Image

Principles of Data Science - Third Edition

By : Sinan Ozdemir
Book Image

Principles of Data Science - Third Edition

By: Sinan Ozdemir

Overview of this book

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.
Table of Contents (18 chapters)

Data Science Terminology

We live in the Data Age. No matter the industry you work in, be it IT, fashion, food, or finance, there is no doubt that data affects your life and work. At some point today, this week, or this month, you will either have or hear about a conversation about data. News outlets are covering more and more stories about data leaks, cybercrimes, and how modern artificial intelligence and machine learning algorithms are changing the way we work and live.

In this book, we will attempt to cover, to put it simply, the principles of how we should interpret, interact with, manipulate, and utilize data. We will attempt to cover the principles of data science. Before we can begin covering such a huge topic, first, we have to build a solid foundation below our feet.

To begin our journey, this chapter will explore the terminology and vocabulary of the modern data scientist. We will learn keywords and phrases that will be essential in our discussion of data science throughout this book. We will also learn why we use data science and learn about the three key domains that data science is derived from before we begin to look at the code in Python, the primary language that will be used in this book.

This chapter will cover the following topics:

  • The basic terminology of data science
  • The three domains of data science
  • The basic Python syntax