1st Edition
Data Visualization and Storytelling with Tableau
Tableau, one of the most widely used visualization tools, is used to illustrate the ideas of data visualization and storytelling. Through Tableau's Data Visualization and Storytelling feature, aspiring data scientists and analysts can develop their visual analytics skills and use them in both academic and business contexts.
Data Visualization and Storytelling with Tableau enables budding data analysts and data scientists to continue sharpen their skills in the field of visual analytics and apply them in business scenarios as well as academic settings. This book approaches the Data Visualization workflow from a practical point of view, emphasizing the steps involved and the outcomes attained. A major focus of this book is the application and deployment of real-time case studies. Later chapters in this book provide comprehensive coverage for advanced topics such as Data Storytelling, Data Insights, Color Selection in Graphs, Publishing in Tableau Public, and Misleading Visualizations. Thus, this book emphasizes the need to visually examine and evaluate data through stories and interactive dashboards that are made up of appropriate graphs and charts. The case studies covered in this book are a natural extension of the visualization topics that are covered in each chapter. The intention is to empower readers to generate various dashboards, stories, graphs, charts, and maps to visualize and analyze data and support decision-making in business. Advanced charts that are pertinent to project management operations are also thoroughly explored, including comparison charts, distribution charts, composition charts, and maps. All these concepts will lay a solid foundation for data visualization applications in the minds of readers.
This book is meant for data analysts, computer scientists/engineers, and industry professionals who are interested in creating different types of visualization graphs for a given data problem and drawing interesting insights from the plotted trends in order to make better business decisions in the future.
Features:
- Introduces the world of Business Intelligence to readers through visualizations in Tableau.
- Discusses the need and relevance of each business graph with the help of a corresponding real-time case study.
- Explores the art of picking a suitable graph with an appropriate color scheme for a given scenario.
- Establishes the process of gaining relevant insights from the analysis of visualizations created.
- Provides guidance in creating innovative dashboards and driving the readers through the process of innovative storytelling with data in Tableau.
- Implements the concept of Exploratory Data Analysis (EDA) in Tableau.
Chapter 1: Getting Started with Data Visualization. 1.1 Introduction to Data and Its Types. 1.2 Data Analysis Lifecycle. 1.3 Data Visualization in Data Analysis. 1.4 Popular Tools for Data Visualization. Key Notes. Test Your Skills. References. Chapter 2: Tableau for Visualization. 2.1 Introducing Tableau. 2.2 Different Tableau Products. 2.3 Tableau Server Architecture. 2.4 Tableau Download and Installation. 2.5 Tableau Data Types. 2.6 Tableau File Types. 2.7 Data Preparation Tasks. 2.8 Publishing in Tableau Public. Key Notes. Test Your Skills. References. Chapter 3: Connecting Data in Tableau. 3.1 Different Data Sources in Tableau. 3.2 Extracting Data in Tableau. 3.3 RDBMS Basics and Types of Keys. 3.4 Data Joins in Tableau. 3.5 Data Import and Blending in Tableau. 3.6 Data Sorting in Tableau. 3.7 Data Pre-Processing Using Tableau Prep. Key Notes. Test Your Skills. References. Chapter 4: Table Calculations and Level of Detail. 4.1 Introduction to Calculations. 4.2 Tableau Functions. 4.3 Tableau Operators. 4.4 Tableau Calculations. 4.5 Level of Detail (LOD) Expressions. Key Notes. Test Your Skills. References. Chapter 5: Sorting and Filters in Tableau. 5.1 Filters in Tableau. 5.2 Sorting in Tableau. 5.3 Group, Hierarchy, and Set in Tableau. Key Notes. Test Your Skills. References. Chapter 6: Charts in Tableau. 6.1 Introducing Charts in Tableau. 6.2 Color Schemes and Palettes in Tableau. 6.3 Colour Choosing Best Practices. Key Notes. Test Your Skills. References. Chapter 7: Comparison Charts in Tableau. 7.1 Introduction to Comparison Charts. 7.2 Studying Changes across Time: Trends and Forecasting. 7.3 Trend Lines and Forecasting. 7.4 Statistical Models for Trend Analysis. Key Notes. Practice Case Study. Test Your Skills. References. Chapter 8: Distribution Charts in Tableau. 8.1. Introduction to Distribution Charts. 8.2. Histogram with Its Types and Components. 8.3. Scatter Plot and Matrix. 8.4. Bubble Chart for Distribution. 8.5. Radar Chart for Multivariate Data. 8.6. Heat Map with Color Variations. 8.7. Box Plot and Quartiles. Key Notes. Practice Case Study. Test Your Skills. References. Chapter 9: Part-to-Whole Relationship: Composition Charts. 9.1. Introduction to Composition Charts. 9.2. Charts for Static Composition. 9.3. Charts for Dynamic Composition over Time. Key Notes. Practice Case Study. Test Your Skills. References. Chapter 10: Project Management with Evaluation Charts. 10.1. Introduction Project and Project Management. 10.2. Project Management Charts. 10.3. Focus on Project Management Activities (RAM). Key Notes. Practice Case Study. Test Your Skills. References. Chapter 11: Maps in Tableau. 11.1. Introduction to Maps. 11.2. Proportional Symbol Maps. 11.3. Tableau Choropleth Maps (Filled Maps). 11.4. Point Distribution Maps. 11.5. Flow Maps (Path Maps). 11.6. Spider Maps (Origin-Destination Maps). Key Notes. Practice Case Study. Test Your Skills. References. Chapter 12: Designing Stories through Data. 12.1. Introduction to Storytelling Concepts. 12.2. Components of a Business Story. 12.3. Storytelling Participants. 12.4. Decision-Making Steps in Storytelling Framework. 12.5. Drawing Insights from a Story. 12.6. Types of Insights. Key Notes. Practice Case Study. Test Your Skills. References. Chapter 13: Exploratory Data Analysis (EDA) in Tableau. 13.1. Introduction to Exploratory Data Analysis (EDA). 13.2. Types of Exploratory Data Analysis. 13.3. Explanatory Data Analysis. 13.4. Combine Exploratory and Explanatory Analysis for Storytelling. Key Notes. Practice Case Study. Test Your Skills. References. Chapter 14: Misleading Visualizations. 14.1. Introducing Misleading Data Visualizations. 14.2. Types of Misleading Visualizations. 14.3. Impact of Misleading Visualizations. 14.4. Mistakes to Be Avoided during Visualization and Storytelling. Key Notes. Test Your Skills. References
Biography
Dr. Mamta Mittal is working as Associate Professor and Programme Anchor for Data Analytics and Data Science at Delhi Skill and Entrepreneur University, New Delhi (under the Government of NCT Delhi). She has received a PhD in Computer Science and Engineering from Thapar University, Patiala; a MTech (Honors) in Computer Science & Engineering from YMCA, Faridabad; and a BTech in Computer Science & Engineering from Kurukshetra University, Kurukshetra. She has been teaching for the last 21 years with an emphasis on Data Mining, Machine Learning, DBMS, and Data Structure. Dr. Mittal is a lifetime member of CSI and has published more than 110 research papers in SCI, SCIE, and Scopus‑indexed journals. She holds five patents and two copyrights in the area of artificial intelligence, IoT, and deep learning. Dr. Mittal is working on the DST‑approved project “Development of IoT based hybrid navigation module for midsized autonomous vehicles” with a research grant of 25 lakhs. Currently, she is guiding PhD scholars in the field of Machine Learning and Deep Learning. She is the editor of the book series Edge AI in Future Computing with CRC Press, Taylor & Francis, USA.
Mrs. Nidhi Grover Raheja is actively working as a Technical Trainer in the domains of Python Programming, Data Analytics, and Visualization Tools. She is currently associated as Guest Faculty in the Data Analytics Department, Bhai Parmanand DSEU Shakarpur Campus‑II, New Delhi (under Govt. of NCT Delhi). She has over a decade of experience and is associated with numerous reputed educational and training institutions in the role of Technical Trainer and Guest Lecturer. She qualified UGC‑NET (Lectureship) and GATE in Computer Science. After completing her MCA from GGSIPU, Delhi, she accomplished MTech (CSE) from DCRUST, Sonipat, with distinction. Her interest areas include Python programming with machine learning, deep learning, natural language processing, statistical analysis, and visualization tools, including Tableau and Microsoft Power BI. She not only endeavors to train students with an experiential learning approach but also continuously tries to shape up their careers with the best of skills and knowledge as per standards.
