The $5 campaign runs from December 15th 2020 to January 13th 2021.
A beginner's guide to statistical techniques and theory to build effective data-driven applications
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.
This book covers the following exciting features:
- Understand 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
- Drive actionable results and clean your data using statistical models, calculus, and probability
- Build and evaluate baseline machine learning models
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
dict = {"dog": "human's best friend", "cat": "destroyer of world"}
dict["dog"]# == "human's best friend"
len(dict["cat"]) # == 18
# but if we try to create a pair with the same key as an existing key
dict["dog"] = "Arf"
Following is what you need for this book: If you are an aspiring data scientist who wants to take your first steps in data science, this book is for you. If you have the basic math skills but want to apply them in data science, or you have good programming skills but lack the necessary math, this book will also help you. Some knowledge of Python programming will also help.
With the following software and hardware list you can run all code files present in the book (Chapter 1-15).
| Chapter | Software required | OS required |
|---|---|---|
| 1-14 | Python version(latest) | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Data Science Algorithms in a Week, Second Edition [Packt] [Amazon]
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Practical Data Science Cookbook, Second Edition [Packt] [Amazon]
Sinan Ozdemir is a data scientist, start-up founder, and educator living in the San Francisco Bay Area. He studied pure mathematics at Johns Hopkins University. He then spent several years conducting lectures on data science at Johns Hopkins University before founding his own start-up, Kylie.ai, which uses artificial intelligence to clone brand personalities and automate customer service communications. Sinan is also the author of Principles of Data Science, First Edition available through Packt.
Sunil Kakade is a technologist, educator, and senior leader with expertise in creating dataand AI-driven organizations. He is in the adjunct faculty at Northwestern University, Evanston, IL, where he teaches graduate courses of data science and big data. He has several research papers to his credit and has presented his work in big data applications at reputable conferences. He has US patents in areas of big data and retail processes. He is passionate about applying data science to improve business outcomes and save patients' lives. At present, Sunil leads the information architecture and analytics team for a large healthcare organization focused on improving healthcare outcomes and lives with his wife, Pratibha, and daughter, Preeti, in Scottsdale, Arizona.
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