Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
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

Arrow left icon
Profile Icon James D. Miller
Arrow right icon
$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (5 Ratings)
Paperback Nov 2017 286 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon James D. Miller
Arrow right icon
$19.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.6 (5 Ratings)
Paperback Nov 2017 286 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing
Table of content icon View table of contents Preview book icon Preview Book

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.

Left arrow icon Right arrow icon
Download code icon Download Code

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

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 17, 2017
Length: 286 pages
Edition : 1st
Language : English
ISBN-13 : 9781788290678
Category :
Languages :
Concepts :
Tools :

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : Nov 17, 2017
Length: 286 pages
Edition : 1st
Language : English
ISBN-13 : 9781788290678
Category :
Languages :
Concepts :
Tools :

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total $ 236.97
Basic Statistics and Data Mining for Data Science
$137.99
Statistics for Data Science
$43.99
Statistics for Machine Learning
$54.99
Total $ 236.97 Stars icon

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
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.