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Statistics for Data Science
Statistics for Data Science

Statistics for Data Science: Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Profile Icon James D. Miller
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₱1256.99 ₱1796.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (5 Ratings)
eBook Nov 2017 286 pages 1st Edition
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₱1256.99 ₱1796.99
Paperback
₱2245.99
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Free Trial
Arrow left icon
Profile Icon James D. Miller
Arrow right icon
₱1256.99 ₱1796.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (5 Ratings)
eBook Nov 2017 286 pages 1st Edition
eBook
₱1256.99 ₱1796.99
Paperback
₱2245.99
Subscription
Free Trial
eBook
₱1256.99 ₱1796.99
Paperback
₱2245.99
Subscription
Free Trial

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Statistics for Data Science

Declaring the Objectives

This chapter introduces and explains (yet again, from a developer's perspective) the basic objectives behind statistics for data science and introduces the reader to the important terms and key concepts (with explanations and examples) that are used throughout the book.

In this chapter, we've broken things down into the following topics:

  • A primer on the key objectives of data science
  • Bringing statistics into data science
  • Common terminologies used with statistics and data science

Key objectives of data science

As mentioned in Chapter 1, Transitioning from Data Developer to Data Scientist, the idea of how data science is defined is a matter of opinion.

I personally like the explanation that data science is a progression or, even better, an evolution of thought or steps, as shown in the following figure:

This data science evolution (depicted in the preceding figure) consists of a series of steps or phases that a data scientist tracks, comprising the following:

  • Collecting data
  • Processing data
  • Exploring and visualizing data
  • Analyzing (data) and/or applying machine learning (to data)
  • Deciding (or planning) based on acquired insight

Although a progression or evolution implies a sequential journey, in practice, this is an extremely fluid process; each of the phases may inspire the data scientist to reverse and repeat one or more of the phases until they are...

Summary

In this chapter, we said that, currently, how data science is defined is a matter of opinion. A practical explanation is that data science is a progression or, even better, an evolution of thought, consisting of collecting, processing, exploring, and visualizing data, analyzing (data) and/or applying machine learning (to the data), and then deciding (or planning) based upon acquired insight(s).

Then, with the goal of thinking like a data scientist, we introduced and defined a number of common terms and concepts a data scientist should be comfortable with.

In the next chapter, we will present and explain how a data developer might understand and approach the topic of data cleaning using several common statistical methods.

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Key benefits

  • No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;
  • Implement statistics in data science tasks such as data cleaning, mining, and analysis
  • Learn all about probability, statistics, numerical computations, and more with the help of R programs

Description

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.

Who is this book for?

This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful.

What you will learn

  • • Analyze the transition from a data developer to a data scientist mindset
  • • Get acquainted with the R programs and the logic used for statistical computations
  • • Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more
  • • Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis
  • • Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks
  • • Get comfortable with performing various statistical computations for data science programmatically

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 17, 2017
Length: 286 pages
Edition : 1st
Language : English
ISBN-13 : 9781788295345
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Product Details

Publication date : Nov 17, 2017
Length: 286 pages
Edition : 1st
Language : English
ISBN-13 : 9781788295345
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Table of Contents

12 Chapters
Transitioning from Data Developer to Data Scientist Chevron down icon Chevron up icon
Declaring the Objectives Chevron down icon Chevron up icon
A Developer's Approach to Data Cleaning Chevron down icon Chevron up icon
Data Mining and the Database Developer Chevron down icon Chevron up icon
Statistical Analysis for the Database Developer Chevron down icon Chevron up icon
Database Progression to Database Regression Chevron down icon Chevron up icon
Regularization for Database Improvement Chevron down icon Chevron up icon
Database Development and Assessment Chevron down icon Chevron up icon
Databases and Neural Networks Chevron down icon Chevron up icon
Boosting your Database Chevron down icon Chevron up icon
Database Classification using Support Vector Machines Chevron down icon Chevron up icon
Database Structures and Machine Learning Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6
(5 Ratings)
5 star 60%
4 star 0%
3 star 0%
2 star 20%
1 star 20%
Adi Mar 02, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Statistics is the main concept in data science , this book helps in analyzing from data developer to a data scientist , R programming logic's for stats and many more concepts. Useful for anyone who is interested in data science
Amazon Verified review Amazon
Vivek V. Oct 06, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Nice book
Amazon Verified review Amazon
Deepak Singh Nov 10, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Imprssive work
Amazon Verified review Amazon
Alexander Jul 31, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
A lot of blank space on approx. 200pages thus covering topics superficially. I would not recommend this book except to those who are looking for a quick intro.
Amazon Verified review Amazon
Chrisfs Jan 29, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I am very disappointed by the book. The contents don't match the title at all. There is very little statistics in the book. It covers the basics of preparing data for analysis and covers the dictionary meaning of some machine learning and statistical terms but it doesn't explain anything in any sort of detail. If you buy this book to learn about statistics, then it's very disappointing and a complete waste of money
Amazon Verified review Amazon
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