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Principles of Data Science

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

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789804546
Length 424 pages
Edition 2nd Edition
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Authors (3):
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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
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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

In this chapter, we looked at different statistical tests, including chi-square and t-tests, as well as point estimates and confidence intervals, in order to ascertain population parameters based on sample data. We were able to find that even with small samples of data, we can make powerful assumptions about the underlying population as a whole.

Using, concepts we reviewed in this chapter, data scientists will be able to make inferences about entire datasets based on certain samples of data. In addition, they will be able to use hypothesis tests to gain a better understanding of full datasets, given samples of data.

Statistics is a very wide and expansive subject that cannot truly be covered in a single chapter; however, our understanding of the subject will allow us to carry on and talk more about how we can use statistics and probability in order to communicate our ideas through data science in the next chapter.

In the next chapter, we will discuss different ways of communicating...

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