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Principles of Data Science

You're reading from   Principles of Data Science Mathematical techniques and theory to succeed in data-driven industries

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781785887918
Length 388 pages
Edition 1st Edition
Languages
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Author (1):
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Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (15) 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 Index

How do we obtain and sample data?


If statistics is about taking samples of populations, it must be very important to know how we obtain these samples, and you'd be correct. Let's focus on just a few of the many ways of obtaining and sampling data.

Obtaining data

There are two main ways of collecting data for our analysis: observational and experimentation. Both these ways have their pros and cons, of course. They each produce different types of behavior and, therefore, warrant different types of analysis.

Observational

We might obtain data through observational means, which consists of measuring specific characteristics but not attempting to modify the subjects being studied. For example, you have a tracking software on your website that observes users' behavior on the website, such as length of time spent on certain pages and the rate of clicking on ads, all the while not affecting the user's experience, then that would be an observational study.

This is one of the most common ways to get data...

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