Search icon Close icon
Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Checkout

Change country

Modal Close icon
Country selected Country selected
country flag United States
Country selected
country flag Great Britain
Country selected
country flag India
Country selected
country flag Germany
Country selected
country flag France
Country selected
country flag Canada
Country selected
country flag Russia
Country selected
country flag Spain
Country selected
country flag Brazil
Country selected
country flag Australia
Country selected
country flag Singapore
Country selected
country flag Hungary
Country selected
country flag Ukraine
Country selected
country flag Luxembourg
Country selected
country flag Estonia
Country selected
country flag Lithuania
Country selected
country flag South Korea
Country selected
country flag Turkey
Country selected
country flag Switzerland
Country selected
country flag Colombia
Country selected
country flag Taiwan
Country selected
country flag Chile
Country selected
country flag Norway
Country selected
country flag Ecuador
Country selected
country flag Indonesia
Country selected
country flag New Zealand
Country selected
country flag Cyprus
Country selected
country flag Denmark
Country selected
country flag Finland
Country selected
country flag Poland
Country selected
country flag Malta
Country selected
country flag Czechia
Country selected
country flag Austria
Country selected
country flag Sweden
Country selected
country flag Italy
Country selected
country flag Egypt
Country selected
country flag Belgium
Country selected
country flag Portugal
Country selected
country flag Slovenia
Country selected
country flag Ireland
Country selected
country flag Romania
Country selected
country flag Greece
Country selected
country flag Argentina
Country selected
country flag Netherlands
Country selected
country flag Bulgaria
Country selected
country flag Latvia
Country selected
country flag South Africa
Country selected
country flag Malaysia
Country selected
country flag Japan
Country selected
country flag Slovakia
Country selected
country flag Philippines
Country selected
country flag Mexico
Country selected
country flag Thailand
Country selected
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Home > Data > Data Science > Principles of Data Science
Principles of Data Science
Principles of Data Science

Principles of Data Science: Mathematical techniques and theory to succeed in data-driven industries

$9.99 $39.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9 (8 Ratings)
eBook Dec 2016 388 pages 1st Edition
eBook
$9.99 $39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m
$9.99 $39.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9 (8 Ratings)
eBook Dec 2016 388 pages 1st Edition
eBook
$9.99 $39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$9.99 $39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Billing Address

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

Principles of Data Science

Chapter 2. Types of Data

Now that we have a basic introduction to the world of data science and understand why the field is so important, let's take a look at the various ways in which data can be formed. Specifically, in this chapter we will look at the following topics:

  • Structured versus unstructured data
  • Quantitative versus qualitative data
  • The four levels of data

We will dive further into each of these topics by showing examples of how data scientists look at and work with data. This chapter is aimed to familiarize ourselves with the fundamental ideas underlying data science.

Flavors of data

In the field, it is important to understand the different flavors of data for several reasons. Not only will the type of data dictate the methods used to analyze and extract results, knowing whether the data is unstructured or perhaps quantitative can also tell you a lot about the real-world phenomenon being measured.

We will look at the three basic classifications of data:

  • Structured vs unstructured (sometimes called organized vs unorganized)
  • Quantitative vs qualitative
  • The four levels of data

The first thing to pay attention to is my use of the word data. In the last chapter, I defined data as merely being a collection of information. This vague definition exists because we may separate data into different categories and need our definition to be loose.

The next thing to remember while we go through this chapter is that for the most part, when I talk about what type of data this is, I will refer to either a specific characteristic of a dataset or to the entire dataset as a...

Why look at these distinctions?

It might seem worthless to stop and think about what type of data we have before getting into the fun stuff, like statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.

Consider an example where we are looking at election results for a county. In the dataset of people, there is a "race" column that is denoted via an identifying number to save space. For example perhaps caucasian is denoted by 7 while Asian American is 2. Without understanding that these numbers are not actually ordered numbers like we think about them (where 7 is greater than 2 and therefore caucasian is "greater than" Asian American) we will make terrible mistakes in our analysis.Discuss

The same principle applies to data science. When given a dataset, it is tempting to jump right into exploring, applying statistical models, and researching the applications of machine learning in order to get results...

Structured versus unstructured data

The distinction between structured and unstructured data is usually the first question you want to ask yourself about the entire dataset. The answer to this question can mean the difference between needing three days or three weeks of time to perform a proper analysis.

The basic breakdown is as follows (this is a rehashed definition of organized and unorganized data in the first chapter):

  • Structured (organized) data: This is data that can be thought of as observations and characteristics. It is usually organized using a table method (rows and columns).
  • Unstructured (unorganized) data: This data exists as a free entity and does not follow any standard organization hierarchy.

Here are a few examples that could help you differentiate between the two:

  • Most data that exists in text form, including server logs and Facebook posts, is unstructured
  • Scientific observations, as recorded by careful scientists, are kept in a very neat and organized (structured) format
  • A...

Quantitative versus qualitative data

When you ask a data scientist, "what type of data is this?", they will usually assume that you are asking them whether or not it is mostly quantitative or qualitative. It is likely the most common way of describing the specific characteristics of a dataset.

For the most part, when talking about quantitative data, you are usually (not always) talking about a structured dataset with a strict row/column structure (because we don't assume unstructured data even has any characteristics). All the more reason why the preprocessing step is so important.

These two data types can be defined as follows:

  • Quantitative data: This data can be described using numbers, and basic mathematical procedures, including addition, are possible on the set.
  • Qualitative data: This data cannot be described using numbers and basic mathematics. This data is generally thought of as being described using "natural" categories and language.

Example – coffee...

The road thus far…

So far in this chapter, we have looked at the differences between structured and unstructured data as well as between qualitative and quantitative characteristics. These two simple distinctions can have drastic effects on the analysis that is performed. Allow me to summarize before moving on the second half of the chapter.

Data as a whole can either be structured or unstructured, meaning that the data can either take on an organized row/column structure with distinct features that describe each row of the dataset, or exist in a free-form state that usually must be preprocessed into a form that is easily digestible.

If data is structured, we can look at each column (feature) of the dataset as being either quantitative or qualitative. Basically, can the column be described using mathematics and numbers or not? The next part of this chapter will break down data into four very specific and detailed levels. At each order, we will apply more complicated rules of mathematics...

Flavors of data

In the field, it is important to understand the different flavors of data for several reasons. Not only will the type of data dictate the methods used to analyze and extract results, knowing whether the data is unstructured or perhaps quantitative can also tell you a lot about the real-world phenomenon being measured.

We will look at the three basic classifications of data:

  • Structured vs unstructured (sometimes called organized vs unorganized)

  • Quantitative vs qualitative

  • The four levels of data

The first thing to pay attention to is my use of the word data. In the last chapter, I defined data as merely being a collection of information. This vague definition exists because we may separate data into different categories and need our definition to be loose.

The next thing to remember while we go through this chapter is that for the most part, when I talk about what type of data this is, I will refer to either a specific characteristic of a dataset or to the entire dataset as a whole...

Why look at these distinctions?

It might seem worthless to stop and think about what type of data we have before getting into the fun stuff, like statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.

Consider an example where we are looking at election results for a county. In the dataset of people, there is a "race" column that is denoted via an identifying number to save space. For example perhaps caucasian is denoted by 7 while Asian American is 2. Without understanding that these numbers are not actually ordered numbers like we think about them (where 7 is greater than 2 and therefore caucasian is "greater than" Asian American) we will make terrible mistakes in our analysis.Discuss

The same principle applies to data science. When given a dataset, it is tempting to jump right into exploring, applying statistical models, and researching the applications of machine learning in order to get results faster. However, if...

Structured versus unstructured data

The distinction between structured and unstructured data is usually the first question you want to ask yourself about the entire dataset. The answer to this question can mean the difference between needing three days or three weeks of time to perform a proper analysis.

The basic breakdown is as follows (this is a rehashed definition of organized and unorganized data in the first chapter):

  • Structured (organized) data: This is data that can be thought of as observations and characteristics. It is usually organized using a table method (rows and columns).

  • Unstructured (unorganized) data: This data exists as a free entity and does not follow any standard organization hierarchy.

Here are a few examples that could help you differentiate between the two:

  • Most data that exists in text form, including server logs and Facebook posts, is unstructured

  • Scientific observations, as recorded by careful scientists, are kept in a very neat and organized (structured) format

  • A genetic...

Left arrow icon

Page 1 of 9

Right arrow icon
Download code icon Download Code

Key benefits

  • Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
  • More than just a math class, learn how to perform real-world data science tasks with R and Python
  • Create actionable insights and transform raw data into tangible value

Description

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

Who is this book for?

You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you.

What you will learn

  • Get to know the five most important steps of data science
  • Use your data intelligently and learn how to handle it with care
  • Bridge the gap between mathematics and programming
  • Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
  • Build and evaluate baseline machine learning models
  • Explore the most effective metrics to determine the success of your machine learning models
  • Create data visualizations that communicate actionable insights
  • Read and apply machine learning concepts to your problems and make actual predictions

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 16, 2016
Length: 388 pages
Edition : 1st
Language : English
ISBN-13 : 9781785888922
Category :
Data
Languages :
Python
Concepts :
Data Science

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Billing Address

Product Details

Publication date : Dec 16, 2016
Length: 388 pages
Edition : 1st
Language : English
ISBN-13 : 9781785888922
Category :
Data
Languages :
Python
Concepts :
Data Science

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
START FREE TRIAL
BUY NOW
$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
START FREE TRIAL
BUY NOW
$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
START FREE TRIAL
BUY NOW

Frequently bought together

Practical Business Intelligence
Practical Business Intelligence
Read more
Dec 2016 352 pages
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $54.99
$9.99 $43.99
$54.99
Principles of Data Science
Principles of Data Science
Read more
Dec 2016 388 pages
Full star icon 3.9 (8)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $48.99
$9.99 $39.99
$48.99
Practical Machine Learning
Practical Machine Learning
Read more
Jan 2016 468 pages
Full star icon 3.9 (19)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $50.99
$9.99 $40.99
$50.99
Stars icon
Total $ 154.97
Practical Business Intelligence
$54.99
Principles of Data Science
$48.99
Practical Machine Learning
$50.99
Total $ 154.97 Stars icon
Banner background image

Table of Contents

14 Chapters
1. How to Sound Like a Data Scientist Chevron down icon Chevron up icon
1. How to Sound Like a Data Scientist
What is data science?
The data science Venn diagram
Some more terminology
Data science case studies
Summary
2. Types of Data Chevron down icon Chevron up icon
2. Types of Data
Flavors of data
Why look at these distinctions?
Structured versus unstructured data
Quantitative versus qualitative data
The road thus far…
The four levels of data
Data is in the eye of the beholder
Summary
3. The Five Steps of Data Science Chevron down icon Chevron up icon
3. The Five Steps of Data Science
Introduction to data science
Overview of the five steps
Explore the data
Summary
4. Basic Mathematics Chevron down icon Chevron up icon
4. Basic Mathematics
Mathematics as a discipline
Basic symbols and terminology
Linear algebra
Summary
5. Impossible or Improbable – A Gentle Introduction to Probability Chevron down icon Chevron up icon
5. Impossible or Improbable – A Gentle Introduction to Probability
Basic definitions
Probability
Bayesian versus Frequentist
Compound events
Conditional probability
The rules of probability
A bit deeper
Summary
6. Advanced Probability Chevron down icon Chevron up icon
6. Advanced Probability
Collectively exhaustive events
Bayesian ideas revisited
Random variables
Summary
7. Basic Statistics Chevron down icon Chevron up icon
7. Basic Statistics
What are statistics?
How do we obtain and sample data?
Sampling data
How do we measure statistics?
The Empirical rule
Summary
8. Advanced Statistics Chevron down icon Chevron up icon
8. Advanced Statistics
Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary
9. Communicating Data Chevron down icon Chevron up icon
9. Communicating Data
Why does communication matter?
Identifying effective and ineffective visualizations
When graphs and statistics lie
Verbal communication
The why/how/what strategy of presenting
Summary
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials Chevron down icon Chevron up icon
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
What is machine learning?
Machine learning isn't perfect
How does machine learning work?
Types of machine learning
How does statistical modeling fit into all of this?
Linear regression
Logistic regression
Probability, odds, and log odds
Dummy variables
Summary
11. Predictions Don't Grow on Trees – or Do They? Chevron down icon Chevron up icon
11. Predictions Don't Grow on Trees – or Do They?
Naïve Bayes classification
Decision trees
Unsupervised learning
K-means clustering
Choosing an optimal number for K and cluster validation
Feature extraction and principal component analysis
Summary
12. Beyond the Essentials Chevron down icon Chevron up icon
12. Beyond the Essentials
The bias variance tradeoff
K folds cross-validation
Grid searching
Ensembling techniques
Neural networks
Summary
13. Case Studies Chevron down icon Chevron up icon
13. Case Studies
Case study 1 – predicting stock prices based on social media
Case study 2 – why do some people cheat on their spouses?
Case study 3 – using tensorflow
Summary
Index Chevron down icon Chevron up icon
Index

Recommendations for you

Left arrow icon
Python Machine Learning By Example
Python Machine Learning By Example
Read more
Jul 2024 518 pages
Full star icon 4.9 (8)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $45.99
$9.99 $36.99
$45.99
Unlocking Data with Generative AI and RAG
Unlocking Data with Generative AI and RAG
Read more
Sep 2024 346 pages
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $39.99
$9.99 $31.99
$39.99
Building LLM Powered Applications
Building LLM Powered Applications
Read more
May 2024 342 pages
Full star icon 4.2 (22)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Python for Algorithmic Trading Cookbook
Python for Algorithmic Trading Cookbook
Read more
Aug 2024 404 pages
Full star icon 4.6 (15)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $59.99
$9.99 $47.99
$59.99
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
Read more
Feb 2022 774 pages
Full star icon 4.4 (95)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $54.99
$9.99 $43.99
$54.99
Artificial Intelligence Engines
Artificial Intelligence Engines
Read more
Nov 2024 217 pages
eBook
eBook
  • eBook eBook $6.98
$6.98 $9.99
AI Product Manager's Handbook
AI Product Manager's Handbook
Read more
Nov 2024 484 pages
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
RAG-Driven Generative AI
RAG-Driven Generative AI
Read more
Sep 2024 334 pages
Full star icon 4.5 (13)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $43.99
$9.99 $35.99
$43.99
Hands-On Reinforcement Learning with Python
Hands-On Reinforcement Learning with Python
Read more
Jun 2018 318 pages
Full star icon 2.6 (18)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $38.99
$9.99 $29.99
$38.99
LLM Engineer's Handbook
LLM Engineer's Handbook
Read more
Oct 2024 522 pages
Full star icon 4.8 (16)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $59.99
$9.99 $47.99
$59.99
Right arrow icon

Customer reviews

Top Reviews
  • Top Reviews
  • Most Recent
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.9
(8 Ratings)
5 star 62.5%
4 star 12.5%
3 star 0%
2 star 0%
1 star 25%
Filter icon Filter
Top Reviews
  • Top Reviews
  • Most Recent

Filter reviews by

Amazon Customer Jan 06, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was lucky to attend classroom Data Science course with Sinan Ozdemir. Sinan is very good at explain really complex mathematical concepts in a very approachable and everyday terms. This really helps people like myself who aren't in touch with theoretical mathematics. This book is written in the same tone that Sinan delivers lectures and that is very valuable in absorbing the complex concepts delivered in this book. The examples and datasets are carefully chosen to deliver specific concepts provide reference for future use. I would recommend this book to anybody with or without formal training in statistics but would like to pick up on the breadth of Data Science. I would also recommend this book to Data Science instructors as an ideal for organization and flow of training on Data Science. This book is the best Data Science book on the market that can introduce you to the world of Data Science and prepare you for interviews.
Amazon Verified review Amazon
Dr Rashmi Yogendra Dhote Jul 09, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The quality of the product I ordered is up to the mark
Amazon Verified review Amazon
willmidwest Jan 19, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Preparing to enter an advanced degree program. This book is Foundational to beginning that journey!
Amazon Verified review Amazon
Suresh Deshpande Sep 20, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Wonderful big with simple explanation
Amazon Verified review Amazon
Amazon Customer Aug 05, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Author really has grip on mathematical tools applied for data analytics. Good books for folks like me who did not consider mathematics seriously in engineering
Amazon Verified review Amazon

People who bought this also bought

Left arrow icon
Machine Learning Engineering with Python
Machine Learning Engineering with Python
Read more
Aug 2023 462 pages
Full star icon 4.6 (36)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Deep Learning with TensorFlow and Keras – 3rd edition
Deep Learning with TensorFlow and Keras – 3rd edition
Read more
Oct 2022 698 pages
Full star icon 4.6 (45)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
Read more
May 2023 286 pages
Full star icon 4.2 (34)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Generative AI with LangChain
Generative AI with LangChain
Read more
Dec 2023 368 pages
Full star icon 4 (34)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $49.99
$9.99 $39.99
$49.99
Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
Read more
May 2023 456 pages
Full star icon 4.5 (49)
eBook
eBook
  • eBook eBook $9.99
  • Paperback Paperback $39.99
$9.99 $31.99
$39.99
Right arrow icon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

How do I buy and download an eBook? Chevron down icon Chevron up icon

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

  • You may make copies of your eBook for your own use onto any machine
  • You may not pass copies of the eBook on to anyone else
How can I make a purchase on your website? Chevron down icon Chevron up icon

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

  1. Register on our website using your email address and the password.
  2. Search for the title by name or ISBN using the search option.
  3. Select the title you want to purchase.
  4. Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title. 
  5. Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)
Where can I access support around an eBook? Chevron down icon Chevron up icon
  • If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
  • To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
  • To view your account details or to download a new copy of the book go to www.packtpub.com/account
  • To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.