Principles of Data Science: Mathematical techniques and theory to succeed in data-driven industries (Paperback or Softback)

Key Features

• Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
• More than just a math class, learn how to perform real-world data science tasks with R and Python
• Create actionable insights and transform raw data into tangible value

Book Description

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.

What you will learn

• Get to know the five most important steps of data science
• Use your data intelligently and learn how to handle it with care
• Bridge the gap between mathematics and programming
• Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
• Build and evaluate baseline machine learning models
• Explore the most effective metrics to determine the success of your machine learning models
• Create data visualizations that communicate actionable insights
• Read and apply machine learning concepts to your problems and make actual predictions

About the Author

Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.

After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.

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

Sinan Ozdemir

Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams. After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.