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Hands-On Data Science with R
Hands-On Data Science with R

Hands-On Data Science with R: Techniques to perform data manipulation and mining to build smart analytical models using R

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Profile Icon Doug Ortiz Profile Icon Bianchi Lanzetta Profile Icon Dasgupta Profile Icon Farias
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AU$24.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Paperback Nov 2018 420 pages 1st Edition
eBook
AU$33.99 AU$48.99
Paperback
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Renews at AU$24.99p/m
Arrow left icon
Profile Icon Doug Ortiz Profile Icon Bianchi Lanzetta Profile Icon Dasgupta Profile Icon Farias
Arrow right icon
AU$24.99 per month
Full star icon Full star icon Full star icon Full star icon Empty star icon 4 (1 Ratings)
Paperback Nov 2018 420 pages 1st Edition
eBook
AU$33.99 AU$48.99
Paperback
AU$60.99
Subscription
Free Trial
Renews at AU$24.99p/m
eBook
AU$33.99 AU$48.99
Paperback
AU$60.99
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Free Trial
Renews at AU$24.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

Hands-On Data Science with R

Descriptive and Inferential Statistics

"To understand God's thoughts we must study statistics, for these are the measure of his purpose."
– Florence Nightingale

Instead of trusting gut feeling and guesses, data scientists trust data. Descriptive statistics are wonderful for introducing data and scavenging for insights. Statistical hypothesis testing is a great way to check how likely some behavior displayed by data is due to an actual trend or randomness. Although some key statistical concepts are recovered during the chapter, readers will greatly benefit from prior knowledge on probabilities and distributions. This chapter will discuss how to use R to draw descriptive...

Measures of central tendency and dispersion

If you care to tackle a problem using the statistic's arsenal there are two tools to begin with: measures of central tendency and measures of variance. This is the starting point for most of the statistical problems. These measures are used in a thing that some would call descriptive analysis. A well done descriptive analysis may be all that you need, depending on the problem you have at hand. Think about the force continuum (and don't go straight to the Megazordstart small).

Central tendency (or average) means typical/middle value from a distribution. This is an abstract concept and we can't really measure it. Yet there are estimates that try to translate this abstract concept into an actual measure. Arithmetic mean, median, and mode are all widespread and consolidated attempts.

Even if you got yourself stuck...

Statistical hypothesis testing

Imagine that you have estimated something about your data that you don't know for sure. Assuming that what you have imagined is true, what are the chances of getting the estimations that you found or even more extreme values? This is hypothesis testing. Statistical hypothesis testing (or simply, hypothesis testing, HT) is the name given to a set of well-known, practical methods used to make inferences with statistics. As long you have data and you're willing to make some inferences about it, the odds are that HT is the way to go. It can work out a great variety of real-world problems.

Although it's usually better to work with experimental data, it's also possible to statistically test hypotheses using observational data as well. Exhibit A: economists all over the world are doing it. A medical treatment's effectiveness, production...

Summary

The intentions of this chapter were to introduce readers to measures of central tendency, dispersion, and statistical hypothesis testing. While the arithmetic mean, median, and mode are the most popular measures of central tendency, t-tests and z-tests may be the most popular statistical tests used. This chapter also taught you how to design your own function to run a z-test, and how to recover it from local files or the cloud. A/B tests were also covered.

In the next chapter, we will cover what data wrangling is and how to use it in R.

Quiz

  1. Which of these tests assume that the standard deviation is unknown?
    • The great macaroni test
    • The z-test
    • The t-test
    • Every A/B test
  2. Which of the following functions will give the probability of getting values equal or greater than one from a standardized normal distribution?
    • qnorm(1, lower.tail = F)
    • pnorm(1, lower.tail = F)
    • pnorm(1)
    • t.test(1, alternative = 'less')
  3. Select the false statement:
    • A/B tests can be used to compare website versions
    • A/B tests can be only used by the web industry
    • Mean is a measure of central tendency
    • Z-tests assume that the standard deviation is known

Answersexecuting the following code will give you the answers to the quiz questions:

set.seed(10)
round(runif(3,1,4))
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Key benefits

  • Explore the popular R packages for data science
  • Use R for efficient data mining, text analytics and feature engineering
  • Become a thorough data science professional with the help of hands-on examples and use-cases in R

Description

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.

Who is this book for?

If you are a data analyst, data engineer, statistician or an R programmer and aspiring to enter the field of machine learning and predictive analytics, then this is the right book to start your journey. Basic understanding of linear algebra/statistics would be beneficial and R programming would be required.

What you will learn

  • Understand the R programming language and its ecosystem of packages for data science
  • Obtain and clean your data before processing
  • Master essential exploratory techniques for summarizing data
  • Examine various machine learning prediction, models
  • Explore the H2O analytics platform in R for deep learning
  • Apply data mining techniques to available datasets
  • Work with interactive visualization packages in R
  • Integrate R with Spark and Hadoop for large-scale data analytics

Product Details

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Publication date : Nov 30, 2018
Length: 420 pages
Edition : 1st
Language : English
ISBN-13 : 9781789139402
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Product Details

Publication date : Nov 30, 2018
Length: 420 pages
Edition : 1st
Language : English
ISBN-13 : 9781789139402
Category :
Languages :
Concepts :
Tools :

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Table of Contents

15 Chapters
Getting Started with Data Science and R Chevron down icon Chevron up icon
Descriptive and Inferential Statistics Chevron down icon Chevron up icon
Data Wrangling with R Chevron down icon Chevron up icon
KDD, Data Mining, and Text Mining Chevron down icon Chevron up icon
Data Analysis with R Chevron down icon Chevron up icon
Machine Learning with R Chevron down icon Chevron up icon
Forecasting and ML App with R Chevron down icon Chevron up icon
Neural Networks and Deep Learning Chevron down icon Chevron up icon
Markovian in R Chevron down icon Chevron up icon
Visualizing Data Chevron down icon Chevron up icon
Going to Production with R Chevron down icon Chevron up icon
Large Scale Data Analytics with Hadoop Chevron down icon Chevron up icon
R on Cloud Chevron down icon Chevron up icon
The Road Ahead Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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