Principles of Data Science

Sinan Ozdemir



Learn the techniques and math you need to start making sense of your data

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

1: HOW TO SOUND LIKE A DATA SCIENTIST
2: TYPES OF DATA
3: THE FIVE STEPS OF DATA SCIENCE
4: BASIC MATHEMATICS
5: IMPOSSIBLE OR IMPROBABLE – A GENTLE INTRODUCTION TO PROBABILITY
6: ADVANCED PROBABILITY
7: BASIC STATISTICS
8: ADVANCED STATISTICS
9: COMMUNICATING DATA
10: HOW TO TELL IF YOUR TOASTER IS LEARNING – MACHINE LEARNING ESSENTIALS
11: PREDICTIONS DON'T GROW ON TREES – OR DO THEY?
12: BEYOND THE ESSENTIALS
13: CASE STUDIES

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