Data visualization with excel and tableaU: A practical guide for data science students

In the age of data, where vast volumes of information are generated daily, making sense of data has become both an art and a science. Data visualization stands at the crossroads of statistics, design, and storytelling. It plays a pivotal role in how we communicate insights, make decisions, and understand the patterns hidden within raw data.
"Data Visualization with Excel and Tableau: A Practical Guide for Data Science Students" is crafted to serve as a comprehensive, hands-on textbook tailored for undergraduate and postgraduate students pursuing Data Science, Analytics, Computer Applications, and Management courses. The book's main goal is to provide a deep understanding of the concepts, techniques, and tools necessary to visualize data effectively using two of the most widely-used platforms: Microsoft Excel and Tableau.
Excel, being ubiquitous and easily accessible, is a perfect starting point for beginners. It introduces fundamental concepts such as charts, graphs, dashboards, and conditional formatting. On the other hand, Tableau is a professional-grade data visualization and Business Intelligence (BI) tool that offers robust capabilities for dynamic, interactive visual storytelling. When combined, these tools provide learners with a powerful arsenal to explore, analyze, and present data.
This book emphasizes practical learning. Each chapter includes use cases, exercises, and real-world datasets to ensure that students not only understand theoretical concepts but also gain the skills necessary to apply them in business, research, and analytics contexts.


Key Features

• Comprehensive Coverage: From the basics of data visualization to advanced dashboard design and storytelling.
• Hands-on Approach: Real datasets, industry case studies, and practical assignments.
• Tool Comparison: In-depth comparison of Excel and Tableau, guiding learners on when and how to use each.
• Step-by-Step Tutorials: Visual walkthroughs for building charts, dashboards, and data stories.
• Focus on Data Science Applications: Examples in sales, healthcare, finance, HR, and supply chain.
• Integration Guidance: Shows how to connect Excel and Tableau with each other and with other data platforms.
• Interview and Career Prep: Glossary, cheat sheets, and top interview questions on visualization tools.

Who Should Read This Book?

• Students of Data Science, Business Analytics, BCA, MCA, BBA, MBA, BSc (CS/IT).
• Educators looking for structured content to teach data visualization.
• Entry-level data analysts, business analysts, and visualization enthusiasts.
• Professionals transitioning into BI or analytics roles.

Chapter Summaries
Chapter 1: Foundations of Data Visualization
This chapter lays the groundwork by defining what data visualization is and explaining its significance in the data science lifecycle. It elaborates on the cognitive benefits of visualizing data and introduces the key principles of design thinking, storytelling, and perception in data communication.
Topics include:

• Definition and evolution of data visualization
• Importance in data-driven decision making
• Types of data (nominal, ordinal, interval, ratio)
• Choosing the right chart
• Ethical considerations and avoiding misleading visuals

Chapter 2: Tools Overview - Excel vs. Tableau
Provides a comparative introduction to Excel and Tableau. It covers user interfaces, capabilities, data connection options, and learning curves. It also includes a decision matrix to help learners choose the right tool based on the problem context.
Chapter 3-6: Data Visualization Using Excel
These chapters dive into Excel-based visualization starting from the basics