Master Four Essential Platforms--One Problem at a Time
In today's data-driven business landscape, the ability to extract insights from information is no longer optional--it's essential. Yet many professionals find themselves trapped between the familiar comfort of spreadsheets and the untapped potential of more powerful analytics platforms. Data Analytics Toolkit: From Excel to Python, R, and Tableau bridges that critical gap, transforming you from a spreadsheet user into a confident, multi-platform data analyst.
Whether you're analyzing financial transactions, tracking election trends, monitoring public health crises, or evaluating investment portfolios, the tools you choose determine both the speed and quality of your insights. This book teaches you to think beyond any single platform by solving the same real-world problems using Excel, Tableau, Python, and R, revealing which tool excels in different situations and why.
Through hands-on case studies based on authentic datasets, you'll develop practical proficiency across four complementary platforms. You'll understand not just how to create visualizations and perform analyses, but why different tools handle tasks differently. This deeper comprehension prepares you to adapt quickly to emerging technologies and make strategic decisions about which platform serves your analytical goals best.
- Foundational Skills Across Platforms: Master data import, transformation, and visualization in Excel, Tableau, Python, and R using parallel examples that illuminate each tool's strengths
- Real-World Case Studies: Work with authentic datasets spanning banking, demographics, elections, public health, historical analysis, education, and financial markets
- Data Wrangling Essentials: Learn to clean, reshape, and prepare data for analysis, addressing common formatting challenges that trip up beginners
- Statistical Aggregation and Grouping: Understand how each platform handles data aggregation, from pivot tables to coding-based transformations
- Interactive Visualization Design: Create compelling graphics that tell stories, from basic charts to filtered, multi-dimensional dashboards
- Learning from Mistakes: Discover common pitfalls and debugging strategies that accelerate your journey toward proficiency
- Conceptual Foundations for Tomorrow's Tools: Build principles-based understanding that transfers to AI-enhanced analytics platforms and future software innovations
Rather than memorizing software instructions that become obsolete, you'll develop analytical thinking that transcends any single tool. By comparing approaches across platforms, you'll gain the conceptual foundation needed to guide AI tools effectively, evaluate their outputs critically, and adapt solutions to new contexts. Whether you're a student beginning your data analytics journey or a professional seeking to expand your toolkit, this book equips you to work fluently across today's platforms and approach tomorrow's with confidence and curiosity.
Ready to unlock the full potential of your data? Start your transformation today.
Table of Contents:
Preface xi
Acknowledgments xv
About the Authors xvii
Chapter 1: Bank Data 1
The Data 1
Target Graphics 2
Getting Started in Excel and Tableau 3
Getting Started with Python and Jupyter Lab 18
Bank Data Graphics in Python 27
Introduction to R: Getting Started with the Bank Data 35
Bank Data Analysis and Platform Comparison 62
Chapter 2: Countries Data 65
The Data 66
Target Graphics 68
Excel 73
Data Wrangling with Tableau Prep Builder 83 Another Tableau Approach 103
Python 113
Histograms 134
R 136
Platform Comparison 157
Chapter 3: Wisconsin Elections Data 161
Overview of the Data 161
Target Graphics 162
Excel 166
Tableau 182
Python 204
R 226
Analysis and Platform Comparison 237
Chapter 4: COVID-19 Data 241
Overview 241
Excel 244
Tableau 248
Python 261
R 274
Analysis and Platform Comparison 289
Chapter 5: Nightingale’s Rose Data 293
Overview of the Data 294
Target Graphics 296
Excel 298
Tableau 309
Python 324
R 353
Platform Comparison 361
Chapter 6: College Data 365
Overview 365
Excel 368
Tableau 376
Python 391
R 408
Analysis and Platform Comparison 422
Chapter 7: Drug Overdoses 425
Target Graphics 426
Excel 429
Tableau 437
Python 451
R 476
Analysis and Platform Comparison 491
Chapter 8: S&P 500 Data 493
Target Graphic 493
Excel 494
Tableau 498
Python 501
R 508
Analysis and Platform Comparison 513
Index 517
About the Author :
Eric Gaze directs the Quantitative Reasoning (QR) program in the Baldwin Center for Learning and Teaching at Bowdoin College and is a senior lecturer in the Mathematics Department, teaching courses in QR and data visualization. He is the author of Thinking Quantitatively: Communicating with Numbers, Third Edition (Pearson, 2023), and was the principal investigator for an NSF TUES Type I grant (2012-2014), Quantitative Literacy and Reasoning Assessment (QLRA) DUE 1140562.
McKenzie Lamb is the Lee G. Hall Distinguished Visiting Professor of Mathematical Sciences and Retention Analyst at DePauw University. He previously served as professor and chair of the Mathematical Sciences Department at Ripon College. He is a coauthor of Knots, Molecules, and the Universe: An Introduction to Topology (American Mathematical Society, 2016).
