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Data Smart Using Data Science to Transform Information into Insight

Jordan Goldmeier

A practical guide to understanding and implementing core data science techniques—from clustering and regression to optimization and forecasting—from scratch using the familiar environment of Microsoft Excel.

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What it’s about

Ever felt intimidated by the jargon and complex code of data science? 'Data Smart' demystifies the field by teaching you how to build powerful analytical models from first principles, using a tool you already know: Microsoft Excel. This book proves that you don't need expensive software or a PhD in statistics to transform information into insight. By walking you through step-by-step implementations of techniques like K-means clustering, logistic regression, ensemble models, and optimization, you'll gain a deep, intuitive understanding of how these algorithms work. Perfect for analysts, business professionals, and aspiring data scientists, 'Data Smart' empowers you to prototype solutions, communicate results effectively, and build a solid foundation before graduating to more advanced tools like R, which is also covered.

The through-line

Who it’s for
A business professional, analyst, or student who understands the importance of data but feels intimidated by the complex tools and jargon of data science. They want to leverage data to gain insights and make better decisions but feel they lack the necessary programming skills or access to specialized software.
The problem
They need to solve complex business problems using data (like segmenting customers, predicting outcomes, or optimizing resources) but are often limited to using Microsoft Excel. They hear buzzwords like 'AI' and 'machine learning' but don't understand the underlying mechanics. They feel overwhelmed, suffer from 'imposter syndrome,' and lack the confidence to tackle data science projects. They worry they're being left behind in a data-driven world and that their skills are becoming outdated.
The plan
  1. Master the advanced Excel features essential for data science, including Power Query, dynamic arrays, and Solver.
  2. Build foundational machine learning models from scratch, one chapter at a time, to gain a deep conceptual understanding.
  3. Apply these techniques to solve real-world business problems like customer segmentation, prediction, forecasting, and optimization.
  4. Learn to evaluate model performance and understand the trade-offs between different approaches.
  5. Transition your newfound knowledge and skills from the transparent environment of Excel to a more powerful tool like R.
The payoff
The reader becomes a confident and capable 'data head' who can prototype models, solve complex business problems, and communicate their findings effectively. · They can identify data-driven opportunities within their organization and turn information into valuable, actionable insight. · They possess a deep, tool-agnostic understanding of data science that empowers them to learn new technologies, including programming languages like R, with ease.

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