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Exam (elaborations)

Data Analytics & Visualization All-in-One | 6 Books in 1 | Master Data Science, Dashboards & Insights

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Unlock the power of data analytics and visualization with this 6-in-1 All-in-One guide by Academic Hub, designed for students, professionals, and anyone seeking to make sense of complex data. This book provides everything you need to build data-driven insights, create meaningful visualizations, and master modern analytics tools. 6 Books in One – Comprehensive coverage of data analysis, statistics, visualization, BI dashboards, storytelling with data, and applied machine learning basics. Practical & Hands-On – Step-by-step instructions with real-world examples and exercises. Visualization Tools – Learn to work with Excel, Tableau, Power BI, Python & R for effective data storytelling. Career & Exam Ready – Build essential skills for careers in business analytics, data science, and decision-making. Updated for 2025/2026 Trends – Covers the latest tools and practices in modern analytics and data visualization. This all-in-one reference helps you transform raw data into actionable insights and communicate them effectively through impactful dashboards and visual stories. data analytics all-in-one, data visualization book, business analytics study guide, tableau power bi textbook, data analytics for beginners 2025, academic hub data visualization, storytelling with data guide, machine learning basics book, business intelligence dashboards, python data analysis book, excel data analytics textbook, data analytics and visualization study, R programming data analysis, analytics all-in-one 2025, statistics for data analytics, decision-making with data, applied data science book, data-driven insights guide, academic hub analytics, learn data visualization fast

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Institution
Data Analysis
Course
Data Analysis











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Institution
Data Analysis
Course
Data Analysis

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Uploaded on
September 28, 2025
Number of pages
832
Written in
2025/2026
Type
Exam (elaborations)
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,Contents at a Glance
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Book 1: Learning Data Analytics & Visualizations
Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
CHAPTER 1: Exploring Definitions and Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
CHAPTER 2: Delving into Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
CHAPTER 3: Understanding Data Lakes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
CHAPTER 4: Wrapping Your Head Around Data Science . . . . . . . . . . . . . . . . . . . . . . . . 51
CHAPTER 5: Telling Powerful Stories with Data Visualization . . . . . . . . . . . . . . . . . . . . 81

Book 2: Using Power BI for Data Analytics &
Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
CHAPTER 1: Power BI Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
CHAPTER 2: The Quick Tour of Power BI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
CHAPTER 3: Prepping Data for Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
CHAPTER 4: Tweaking Data for Primetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
CHAPTER 5: Designing and Deploying Data Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
CHAPTER 6: Tackling Visualization Basics in Power BI . . . . . . . . . . . . . . . . . . . . . . . . . 203
CHAPTER 7: Digging into Complex Visualization and Table Data . . . . . . . . . . . . . . . . 227
CHAPTER 8: Sharing and Collaborating with Power BI . . . . . . . . . . . . . . . . . . . . . . . . . 247

Book 3: Using Tableau for Data Analytics &
Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
CHAPTER 1: Tableau Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
CHAPTER 2: Connecting Your Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
CHAPTER 3: Diving into the Tableau Prep Lifecycle . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
CHAPTER 4: Advanced Data Prep Approaches in Tableau . . . . . . . . . . . . . . . . . . . . . 337
CHAPTER 5: Touring Tableau Desktop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
CHAPTER 6: Storytelling Foundations in Tableau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371
CHAPTER 7: Visualizing Data in Tableau . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
CHAPTER 8: Collaborating and Publishing with Tableau Cloud . . . . . . . . . . . . . . . . . 425

Book 4: Extracting Information with SQL . . . . . . . . . . . . . . . . . . . 443
CHAPTER 1: SQL Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
CHAPTER 2: Drilling Down to the SQL Nitty-Gritty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455
CHAPTER 3: Values, Variables, Functions, and Expressions . . . . . . . . . . . . . . . . . . . . 487
CHAPTER 4: SELECT Statements and Modifying Clauses . . . . . . . . . . . . . . . . . . . . . . . 513
CHAPTER 5: Tuning Queries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539

,CHAPTER 6: Complex Query Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557
CHAPTER 7: Joining Data Together in SQL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591

Book 5: Performing Statistical Data Analysis &
Visualization with R Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . 605
CHAPTER 1: Using Open Source R for Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . . 607
CHAPTER 2: R: What It Does and How It Does It . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623
CHAPTER 3: Getting Graphical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651
CHAPTER 4: Kicking It Up a Notch to ggplot2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671

Book 6: Applying Python Programming
to Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689
CHAPTER 1: Discovering the Match between Data Science and Python . . . . . . . . . . 691
CHAPTER 2: Using Python for Data Science and Visualization . . . . . . . . . . . . . . . . . . 703
CHAPTER 3: Getting a Crash Course in Matplotlib . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721
CHAPTER 4: Visualizing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 739

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .761

, Table of Contents
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
About This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Foolish Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Icons Used in This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Beyond the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Where to Go from Here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

BOOK 1: LEARNING DATA ANALYTICS &
VISUALIZATIONS FOUNDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
CHAPTER 1: Exploring Definitions and Roles . . . . . . . . . . . . . . . . . . . . . . . . 9
What Is Data, Really? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10
Working with structured data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10
Looking at unstructured data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
Adding semi-structured data to the mix . . . . . . . . . . . . . . . . . . . . . .11
Discovering Business Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12
Understanding Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13
Exploring Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
Diving into Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
Cooking raw data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
Dealing with data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
Building data models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17
Performing what-if analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17
Visualizing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

CHAPTER 2: Delving into Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Identifying the Roles of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20
Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
Decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22
Measuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23
Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23
Insight management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
Other roles for data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26
Grappling with data volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
Handling data velocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
Dealing with data variety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29




Table of Contents v

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