Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Principles of Data Science

You're reading from   Principles of Data Science Understand, analyze, and predict data using Machine Learning concepts and tools

Arrow left icon
Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789804546
Length 424 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
Sunil Kakade Sunil Kakade
Author Profile Icon Sunil Kakade
Sunil Kakade
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
Marco Tibaldeschi Marco Tibaldeschi
Author Profile Icon Marco Tibaldeschi
Marco Tibaldeschi
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. How to Sound Like a Data Scientist FREE CHAPTER 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 14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service Other Books You May Enjoy Index

Summary

Although this is only our first look at data exploration, don't worry, this is definitely not the last time we will follow these steps for data science and exploration. From now on, every time we look at a new piece of data, we will use our steps of exploration to transform, break down, and standardize our data. The steps outlined in this chapter, while they are only guidelines, form a standard practice that any data scientist can follow in their work. The se steps can also be applied to any dataset that requires analysis.

We are rapidly approaching the section of this book that deals with statistical, probabilistic, and machine learning models. Before we can truly jump into these models, we have to look at some of the basics of mathematics. In the next chapter, we will take a look at some of the math necessary to perform some of the more complicated operations in modeling, but don't worry; the math that's required for this process is minimal, and we will go through...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image