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Author:   Mamta Mittal (G. B. Pant Engineering College, New Delhi, India) ,  Nidhi Grover Raheja
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.453kg
ISBN:  

9781032552224

ISBN 10:   1032552220
Pages:   460
Publication Date:   28 June 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

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

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Author Information

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.

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