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Pages
432
Published
2013
Data Analytics
✨ New
Data Smart
Using Data Science to Transform Information into Insight — with Excel
Learn the core algorithms of data science by working through real business problems entirely inside Excel, no programming required.
Data Smart teaches you the practical mechanics of data science without requiring a single line of code. Working entirely in Excel, you build clustering models, regression analyses, forecasting systems, and optimization solutions from scratch. Each chapter tackles a concrete business problem and walks you through the math behind the technique before automating it in a spreadsheet. By the end, you understand not just how to click a button, but why the algorithm works.
About this book
Most introductions to data science send you straight to Python or R before you understand what the algorithms are actually doing. Data Smart takes a different approach. John W. Foreman starts with Excel, a tool you almost certainly already have, and uses it to expose the inner mechanics of techniques that data scientists use every day.
The logic is deliberate. When you build a k-means clustering model by hand in a spreadsheet, you can watch each centroid move with each iteration. When you run a regression, you can see the residuals cell by cell. That visibility builds intuition that survives the transition to any tool you pick up later.
The book works through real business scenarios from customer segmentation to sales forecasting to network analysis. Each scenario is self-contained. You can follow a single chapter if that technique is the one you need right now, or work through the book front to back to build a complete mental model of the field.
- Cluster customers and transactions using k-means and hierarchical clustering
- Build regression models and understand when to trust them
- Forecast time-series data using exponential smoothing and decomposition
- Solve optimization problems with Excel Solver
- Detect patterns in text with naive Bayes classification
- Understand ensemble methods and the logic behind random forests
- Work through network graphs and social-network analysis concepts
You do not need a statistics degree or programming background. You need Excel, a willingness to read a formula carefully, and a specific business problem you want to solve. The math is present but explained plainly, and every technique lands in a working spreadsheet before the chapter ends.
Data Smart is the book that converts an analyst who knows Excel into an analyst who understands data science. It earns its place on the desk, not just the shelf.
🎯 What you'll learn
- Build k-means and hierarchical clustering models from scratch inside a spreadsheet
- Construct and evaluate linear and logistic regression models on real business data
- Forecast time-series data using exponential smoothing and seasonal decomposition
- Classify text and categorical data with naive Bayes implemented in Excel formulas
- Solve constrained optimization problems using Excel Solver
- Interpret ensemble model logic and understand why random forests outperform single decision trees
- Analyze networks and derive insight from relationship graphs without specialist software
👤 Who is this book for?
- Business analysts who work in Excel daily and want to understand the algorithms behind the dashboards they build
- Aspiring data scientists who want intuition for how models work before learning Python or R
- Marketing or operations professionals who need to segment customers, forecast demand, or optimize resources without a dedicated data team
- Students taking a first course in data analytics who want a practical, hands-on complement to a textbook
- Developers or engineers who are comfortable with logic but want a low-friction entry point into statistical modeling
Table of contents
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01
Data Science in a Nutshell
Establishes what data science actually means in a business context and previews the techniques covered in the book. You learn how each method maps to a class of real business problem.
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02
Naive Bayes and the Bare Basics
Introduces probability-based classification through naive Bayes, building the model cell by cell in Excel. You classify a real dataset and see exactly how prior and conditional probabilities combine into a prediction.
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03
Clustering with k-Means
Walks through the k-means algorithm iteration by iteration so you can watch centroids converge. You segment a customer dataset and learn how to choose the number of clusters.
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04
Regression: Predicting Continuous Outcomes
Builds linear regression from the ground up, covering how coefficients are fitted and how to measure model quality. You apply the technique to a sales forecasting scenario and interpret the results honestly.
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05
Optimization with Excel Solver
Introduces constrained optimization and shows how Excel Solver navigates the solution space. You formulate a real resource-allocation problem and walk through how the algorithm reaches an answer.
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06
Forecasting with Exponential Smoothing
Covers time-series decomposition and exponential smoothing methods for predicting future values. You build a working forecast model and learn how to tune the smoothing parameters for your data.
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07
Ensemble Methods and Decision Trees
Explains decision trees and the logic that makes ensemble methods like random forests more robust. You trace how individual trees are built and combined, and understand when to trust an ensemble prediction.
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08
Network Analysis
Introduces graph theory concepts and applies them to social-network and relationship data. You identify clusters and influential nodes in a network dataset using spreadsheet-compatible techniques.
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09
Putting It All Together
Reviews the full toolkit and guides you through choosing the right technique for a given business question. You leave with a decision framework for scoping a data project from raw data to actionable output.
Frequently asked questions
Do I need to know programming to use this book?
No programming is required. Every technique in the book is implemented entirely in Microsoft Excel. You need to be comfortable reading formulas, but no coding background is assumed.
Which version of Excel do I need?
The book was written for Excel 2010 and later on Windows. Most content works in Excel 2013 and 2016 as well. Some Solver-based chapters may behave differently in Excel for Mac or Excel Online.
Is this book still relevant given it was published in 2013?
The core algorithms covered, k-means clustering, regression, naive Bayes, and exponential smoothing, are foundational and have not changed. The Excel mechanics are stable. Readers use this book as an introduction to build intuition before moving to modern tools.
Is this a statistics textbook or a practical guide?
It is a practical guide. The math is explained clearly but the emphasis is always on applying a technique to a real business problem inside a spreadsheet, not on formal proofs.
Who should probably not buy this book?
If you already work comfortably in Python or R and want advanced machine learning techniques, this book is not aimed at you. It is an entry point, not a reference for experienced practitioners.
Are the spreadsheet files from the book available?
Companion files were made available through the publisher's website at the time of publication. Check the Wiley product page for the current download link.
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