Data Analytics & Visualization All-in-One For Dummies

Data Analytics & Visualization All-in-One For Dummies

by Jack A. Hyman, Luca Massaron, Paul McFedries, John Paul Mueller, Lillian Pierson, Jonathan Reichental, Joseph Schmuller, Alan R. Simon, Allen G. Taylor
Released April 2024
Publisher(s): For Dummies
ISBN: 9781394244096

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Install data analytics into your brain with this comprehensive introduction

Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey.

  • Mine data from data sources
  • Organize and analyze data 
  • Use data to tell a story with Tableau
  • Expand your know-how with Python and R

New and novice data analysts will love this All-in-One reference on how to make sense of data. Get ready to watch as your career in data takes off.

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Introduction
    1. About This Book
    2. Foolish Assumptions
    3. Icons Used in This Book
    4. Beyond the Book
    5. Where to Go from Here
  5. Book 1: Learning Data Analytics & Visualizations Foundations
    1. Chapter 1: Exploring Definitions and Roles
      1. What Is Data, Really?
      2. Discovering Business Intelligence
      3. Understanding Data Analytics
      4. Exploring Data Management
      5. Diving into Data Analysis
      6. Visualizing Data
    2. Chapter 2: Delving into Big Data
      1. Identifying the Roles of Data
      2. What’s All the Fuss about Data?
      3. Identifying Important Data Sources
      4. Role of Big Data in Data Science and Engineering
      5. Connecting Big Data with Business Intelligence
      6. Analyzing Data with Enterprise Business Intelligence Practices
    3. Chapter 3: Understanding Data Lakes
      1. Rock-Solid Water
      2. A Really Great Lake
      3. Expanding the Data Lake
      4. More Than Just the Water
      5. Different Types of Data
      6. Different Water, Different Data
      7. Refilling the Data Lake
      8. Everyone Visits the Data Lake
    4. Chapter 4: Wrapping Your Head Around Data Science
      1. Inspecting the Pieces of the Data Science Puzzle
      2. Choosing the Best Tools for Your Data Science Strategy
      3. Getting a Handle on SQL and Relational Databases
      4. Investing Some Effort into Database Design
      5. Narrowing the Focus with SQL Functions
      6. Making Life Easier with Excel
    5. Chapter 5: Telling Powerful Stories with Data Visualization
      1. Data Visualizations: The Big Three
      2. Designing to Meet the Needs of Your Target Audience
      3. Picking the Most Appropriate Design Style
      4. Selecting the Appropriate Data Graphic Type
      5. Testing Data Graphics
      6. Adding Context
  6. Book 2: Using Power BI for Data Analytics & Visualization
    1. Chapter 1: Power BI Foundations
      1. Looking Under the Power BI Hood
      2. Knowing Your Power BI Terminology
      3. Power BI Products in a Nutshell
    2. Chapter 2: The Quick Tour of Power BI
      1. Power BI Desktop: A Top-Down View
      2. Services: Far and Wide
    3. Chapter 3: Prepping Data for Visualization
      1. Getting Data from the Source
      2. Managing Data Source Settings
      3. Working with Shared versus Local Datasets
      4. Storage and Connection Modes
      5. Data Sources Oh My!
      6. Cleansing, Transforming, and Loading Your Data
    4. Chapter 4: Tweaking Data for Primetime
      1. Stepping through the Data Lifecycle
      2. Resolving Inconsistencies
      3. Evaluating and Transforming Column Data Types
      4. Configuring Queries for Data Loading
      5. Resolving Errors During Data Import
    5. Chapter 5: Designing and Deploying Data Models
      1. Creating a Data Model Masterpiece
      2. Managing Relationships
      3. Arranging Data
      4. Publishing Data Models
    6. Chapter 6: Tackling Visualization Basics in Power BI
      1. Looking at Report Fundamentals and Visualizations
      2. Choosing the Best Visualization for the Job
    7. Chapter 7: Digging into Complex Visualization and Table Data
      1. Dealing with Table-Based and Complex Visualizations
      2. Using AI Tools to Create Questions and Answers
      3. Formatting and Configuring Report Visualizations
      4. Diving into Dashboards
    8. Chapter 8: Sharing and Collaborating with Power BI
      1. Working Together in a Workspace
      2. Slicing and Dicing Data
      3. Troubleshooting the Use of Data Lineage
      4. Datasets, Dataflows, and Lineage
      5. Defending Your Data Turf
  7. Book 3: Using Tableau for Data Analytics & Visualization
    1. Chapter 1: Tableau Foundations
      1. Understanding Key Tableau Terms
      2. Getting to Know the Tableau Product Line
      3. Choosing the Right Version
      4. Knowing What Tools You Need in Each Stage of the Data Life Cycle
      5. Understanding User Types and Their Capabilities
    2. Chapter 2: Connecting Your Data
      1. Understanding Data Source Options
      2. Connecting to Data
      3. Setting Up and Planning the Data Source
      4. Relating and Combining Data Sources
      5. Working with Data Relationships
      6. Joining Data
    3. Chapter 3: Diving into the Tableau Prep Lifecycle
      1. Dabbling in Data Flows
      2. Saving Prep Data
    4. Chapter 4: Advanced Data Prep Approaches in Tableau
      1. Peering into Data Structures
      2. Structuring for Data Visualization
      3. Normalizing Data
    5. Chapter 5: Touring Tableau Desktop
      1. Getting Hands-On in the Tableau Desktop Workspace
      2. Making Use of the Tableau Desktop Menus
      3. Tooling Around in the Toolbar
      4. Understanding Sheets versus Workbooks
    6. Chapter 6: Storytelling Foundations in Tableau
      1. Working with Dashboards
      2. Creating a Compelling Story
    7. Chapter 7: Visualizing Data in Tableau
      1. Introducing the Visualizations
      2. Converting a Visualization to a Crosstab
      3. Publishing Visualizations
    8. Chapter 8: Collaborating and Publishing with Tableau Cloud
      1. Strolling through the Tableau Cloud Experience
      2. Evaluating Personal Features in Tableau Cloud
      3. Sharing Experiences and Collaborating with Others
  8. Book 4: Extracting Information with SQL
    1. Chapter 1: SQL Foundations
      1. SQL and the Relational Model
      2. Sets, Relations, Multisets, and Tables
      3. Functional Dependencies
      4. Keys
      5. Views
      6. Users
      7. Privileges
      8. Schemas
      9. Catalogs
      10. Connections, Sessions, and Transactions
      11. Routines
      12. Paths
    2. Chapter 2: Drilling Down to the SQL Nitty-Gritty
      1. Executing SQL Statements
      2. Using Reserved Words Correctly
      3. SQL’s Data Types
      4. Handling Null Values
      5. Applying Constraints
    3. Chapter 3: Values, Variables, Functions, and Expressions
      1. Entering Data Values
      2. Working with Functions
      3. Using Expressions
    4. Chapter 4: SELECT Statements and Modifying Clauses
      1. Finding Needles in Haystacks with the SELECT Statement
      2. Modifying Clauses
    5. Chapter 5: Tuning Queries
      1. SELECT DISTINCT
      2. Temporary Tables
      3. The ORDER BY Clause
      4. The HAVING Clause
      5. The OR Logical Connective
    6. Chapter 6: Complex Query Design
      1. What Is a Subquery?
      2. What Subqueries Do
      3. Using Subqueries in INSERT, DELETE, and UPDATE Statements
      4. Tuning Considerations for Statements Containing Nested Queries
      5. Tuning Correlated Subqueries
      6. UNION
      7. INTERSECT
      8. EXCEPT
    7. Chapter 7: Joining Data Together in SQL
      1. JOINS
      2. ON versus WHERE
      3. Join Conditions and Clustering Indexes
  9. Book 5: Performing Statistical Data Analysis & Visualization with R Programming
    1. Chapter 1: Using Open Source R for Data Science
      1. Downloading Open Source R
      2. Comprehending R’s Basic Vocabulary
      3. Delving into Functions and Operators
      4. Iterating in R
      5. Observing How Objects Work
      6. Sorting Out R’s Popular Statistical Analysis Packages
      7. Examining Packages for Visualizing, Mapping, and Graphing in R
    2. Chapter 2: R: What It Does and How It Does It
      1. The Statistical (and Related) Ideas You Just Have to Know
      2. Getting R
      3. Getting RStudio
      4. A Session with R
      5. R Functions
      6. User-Defined Functions
      7. Comments
      8. R Structures
      9. for Loops and if Statements
    3. Chapter 3: Getting Graphical
      1. Finding Patterns
      2. Doing the Basics: Base R Graphics, That Is
    4. Chapter 4: Kicking It Up a Notch to ggplot2
      1. Histograms
      2. Bar Plots
      3. Dot Charts
      4. Bar Plots Re-revisited
      5. Scatter Plots
      6. Scatter Plot Matrix
      7. Box Plots
  10. Book 6: Applying Python Programming to Data Science
    1. Chapter 1: Discovering the Match between Data Science and Python
      1. Creating the Data Science Pipeline
      2. Understanding Python’s Role in Data Science
      3. Learning to Use Python Fast
      4. Working with Python
      5. Using the Python Ecosystem for Data Science
    2. Chapter 2: Using Python for Data Science and Visualization
      1. Using Python for Data Science
      2. Sorting Out the Various Python Data Types
      3. Putting Loops to Good Use in Python
      4. Having Fun with Functions
      5. Keeping Cool with Classes
      6. Checking Out Some Useful Python Libraries
    3. Chapter 3: Getting a Crash Course in Matplotlib
      1. Starting with a Graph
      2. Setting the Axis, Ticks, and Grids
      3. Defining the Line Appearance
      4. Using Labels, Annotations, and Legends
    4. Chapter 4: Visualizing the Data
      1. Choosing the Right Graph
      2. Creating Advanced Scatterplots
      3. Plotting Time Series
      4. Plotting Geographical Data
      5. Visualizing Graphs
  11. Index
  12. About the Authors
  13. Advertisement Page
  14. Connect with Dummies
  15. End User License Agreement

Product information

  • Title: Data Analytics & Visualization All-in-One For Dummies
  • Author(s): Jack A. Hyman, Luca Massaron, Paul McFedries, John Paul Mueller, Lillian Pierson, Jonathan Reichental, Joseph Schmuller, Alan R. Simon, Allen G. Taylor
  • Release date: April 2024
  • Publisher(s): For Dummies
  • ISBN: 9781394244096