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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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Toc

Table of Contents (15) Chapters Close

Preface 1. How to Sound Like a Data Scientist 2. Types of Data FREE CHAPTER 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

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "For these operators, keep the boolean datatype in mind."

A block of code is set as follows:

tweet = "RT @j_o_n_dnger: $TWTR now top holding for 
             Andor, unseating $AAPL"

words_in_tweet = first_tweet.split(' ') # list of words in tweet

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

for word in words_in_tweet:             # for each word in list
  if "$" in word:                       # if word has a "cashtag" 
  print "THIS TWEET IS ABOUT", word  # alert the user

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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