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Overview
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
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