Data Science For Dummies

Discover how data science can help you gain in-depth insight into your business - the easy way!

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here's what to expect:

• Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value

• Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL

• Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things

• Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate

It's a big, big data world out there--let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

Autorentext

Lillian Pierson, P.E. is a data scientist, professional environmental engineer, and leading data science consultant to global leaders in IT, major governmental and non-governmental entities, prestigious media corporations, and not-for-profit technology groups.

Inhalt
Foreword xv

Introduction 1

About This Book 2

Foolish Assumptions 2

Icons Used in This Book 3

Beyond the Book 3

Where to Go from Here 4

Part 1: Getting Started with Data Science 5

Chapter 1: Wrapping Your Head around Data Science 7

Seeing Who Can Make Use of Data Science 8

Analyzing the Pieces of the Data Science Puzzle 10

Collecting, querying, and consuming data 10

Applying mathematical modeling to data science tasks 11

Deriving insights from statistical methods 12

Coding, coding, coding it's just part of the game 12

Applying data science to a subject area 12

Communicating data insights 14

Exploring the Data Science Solution Alternatives 14

Assembling your own in-house team 14

Outsourcing requirements to private data science consultants 15

Leveraging cloud-based platform solutions 15

Letting Data Science Make You More Marketable 16

Chapter 2: Exploring Data Engineering Pipelines and Infrastructure 17

Defining Big Data by the Three Vs 18

Grappling with data volume 18

Handling data velocity 18

Dealing with data variety 19

Identifying Big Data Sources 20

Grasping the Difference between Data Science and Data Engineering 21

Defining data science 21

Defining data engineering 22

Comparing data scientists and data engineers 23

Making Sense of Data in Hadoop 24

Digging into MapReduce 24

Stepping into real-time processing 26

Storing data on the Hadoop distributed file system (HDFS) 27

Putting it all together on the Hadoop platform 28

Identifying Alternative Big Data Solutions 28

Introducing massively parallel processing (MPP) platforms 29

Introducing NoSQL databases 29

Data Engineering in Action: A Case Study 30

Identifying the business challenge 30

Solving business problems with data engineering 32

Boasting about benefits 32

Chapter 3: Applying Data-Driven Insights to Business and Industry 33

Benefiting from Business-Centric Data Science 34

Converting Raw Data into Actionable Insights with Data Analytics 35

Types of analytics 35

Common challenges in analytics 36

Data wrangling 36

Taking Action on Business Insights 37

Distinguishing between Business Intelligence and Data Science 39

Business intelligence, defined 39

The kinds of data used in business intelligence 40

Technologies and skillsets that are useful in business intelligence 40

Defining Business-Centric Data Science 41

Kinds of data that are useful in business-centric data science 42

Technologies and skillsets that are useful in business-centric data science 43

Making business value from machine learning methods 43

Differentiating between Business Intelligence and Business-Centric Data Science 44

Knowing Whom to Call to Get the Job Done Right 45

Exploring Data Science in Business: A Data-Driven Business Success Story 46

Part 2: Using Data Science to Extract Meaning from Your Data 49

Chapter 4: Machine Learning: Learning from Data with Your Machine 51

Defining Machine Learning and Its Processes 51

Walking through the steps of the machine learning process 52

Getting familiar with machine learning terms 52

Considering Learning Styles 53

Learning with supervised algorithms 53

Learning with unsupervised algorithms 53

Learning with reinforcement 54

Seeing What You Can Do 54

Selecting algorithms based on function 54

Using Spark to generate real-time big data analytics 58 **Chapter 5: Math, Probability, and ...