Data Science For Dummies PDF Free Download [Direct Link]

Data Science For Dummies PDF

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Here’s the complete overview of Data Science For Dummies PDF:

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 covering 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 aspects of the topic, including the sometimes intimidating field of big data and data science, it is not an instructional manual for hands-on implementation.

Features of Data Science For Dummies PDF

Here’s a quick overview of the essential features of this book:

  • 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 and applications 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 get started harnessing its power so you can gain a competitive edge for your organization.

Table of Contents

Below is the complete table of contents offered inside Data Science For Dummies PDF:

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 Statistical Modeling 61

Exploring Probability and Inferential Statistics 62

Probability distributions 63

Conditional probability with Naïve Bayes 65

Quantifying Correlation 66

Calculating correlation with Pearson’s r 66

Ranking variable-pairs using Spearman’s rank correlation 66

Reducing Data Dimensionality with Linear Algebra 67

Decomposing data to reduce dimensionality 67

Reducing dimensionality with factor analysis 69

Decreasing dimensionality and removing outliers with PCA 70

Modeling Decisions with Multi-Criteria Decision Making 70

Turning to traditional MCDM 71

Focusing on fuzzy MCDM 72

Introducing Regression Methods 73

Linear regression 73

Logistic regression 74

Ordinary least squares (OLS) regression methods 74

Detecting Outliers 75

Analyzing extreme values 75

Detecting outliers with univariate analysis 76

Detecting outliers with multivariate analysis 77

Introducing Time Series Analysis 78

Identifying patterns in time series 78

Modeling univariate time series data 79

Chapter 6: Using Clustering to Subdivide Data 81

Introducing Clustering Basics 81

Getting to know clustering algorithms 82

Looking at clustering similarity metrics 85

Identifying Clusters in Your Data 86

Clustering with the k-means algorithm 86

Estimating clusters with kernel density estimation (KDE) 87

Clustering with hierarchical algorithms 88

Dabbling in the DBScan neighborhood 90

Categorizing Data with Decision Tree and Random Forest Algorithms 91

Chapter 7: Modeling with Instances 93

Recognizing the Difference between Clustering and Classification 94

Reintroducing clustering concepts 94

Getting to know classification algorithms 95

Making Sense of Data with Nearest Neighbor Analysis 97

Classifying Data with Average Nearest Neighbor Algorithms 98

Classifying with K-Nearest Neighbor Algorithms 101

Understanding how the k-nearest neighbor algorithm works 102

Knowing when to use the k-nearest neighbor algorithm 103

Exploring common applications of k-nearest neighbor algorithms 104

Solving Real-World Problems with Nearest Neighbor Algorithms 104

Seeing k-nearest neighbor algorithms in action 104

Seeing average nearest neighbor algorithms in action 105

Chapter 8: Building Models That Operate Internet-of-Things Devices 107

Overviewing the Vocabulary and Technologies 108

Learning the lingo 108

Procuring IoT platforms 110

Spark streaming for the IoT 110

Getting context-aware with sensor fusion 111

Digging into the Data Science Approaches 111

Taking on time series 112

Geospatial analysis 112

Dabbling in deep learning 113

Advancing Artificial Intelligence Innovation 113

Part 3: Creating Data Visualizations That Clearly Communicate Meaning 115

Chapter 9: Following the Principles of Data Visualization Design 117

Data Visualizations: The Big Three 118

Data storytelling for organizational decision makers 118

Data showcasing for analysts 118

Designing data art for activists 119

Designing to Meet the Needs of Your Target Audience 119

Step 1: Brainstorm (about Brenda) 120

Step 2: Define the purpose 121

Step 3: Choose the most functional visualization type for your purpose 121

Picking the Most Appropriate Design Style 122

Inducing a calculating, exacting response 122

Eliciting a strong emotional response 123

Choosing How to Add Context 124

Creating context with data 125

Creating context with annotations 125

Creating context with graphical elements 125

Selecting the Appropriate Data Graphic Type 127

Standard chart graphics 127

Comparative graphics 130

Statistical plots 134

Topology structures 135

Spatial plots and maps 138

Choosing a Data Graphic 140

Chapter 10: Using D3.js for Data Visualization 141

Introducing the D3.js Library 141

Knowing When to Use D3.js (and When Not To) 142

Getting Started in D3.js 143

Bringing in the HTML and DOM 144

Bringing in the JavaScript and SVG 145

Bringing in the Cascading Style Sheets (CSS) 146

Bringing in the web servers and PHP 146

Implementing More Advanced Concepts and Practices in D3.js 147

Getting to know chain syntax 151

Getting to know scales 152

Getting to know transitions and interactions 153

Data Science For Dummies PDF Free Download

Alright, now in this part of the article, you will be able to access the free PDF download of Data Science For Dummies PDF using our direct links mentioned at the end of this article. We have uploaded a genuine PDF ebook copy of this book to our online file repository so that you can enjoy a blazing-fast and safe downloading experience.

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Data Science For Dummies PDF


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