Partial book profile
Data Smart: Using Data Science to Transform Information into Insight
A hands-on guide that teaches the core algorithms of data science from scratch using spreadsheets (and finally R), so business people can understand, prototype, and deploy these techniques without first buying tools or hiring consultants.
The full book profile is not available yet.
What it’s about
Data Smart strips away the hype, tools, and code that usually obscure data science and teaches the actual techniques—clustering, naive Bayes, optimization, network graphs, regression, ensemble models, forecasting, and outlier detection—by building each one by hand in a spreadsheet. Written conversationally by MailChimp's Chief Data Scientist, the book targets marketers, analysts, and executives who feel pressure to 'do data science' but don't understand what these techniques are or how to choose the right one for a problem. By the time readers finish, they can identify data science opportunities in their own organizations, prototype solutions, correctly evaluate vendors and developers, and graduate into a programming language like R to scale their work. The book's philosophy is that understanding beats button-pushing: once you've implemented an algorithm from the barest of tools, you can implement it anywhere.
The through-line
- Who it’s for
- A business professional (marketing VP, analyst, CEO, or online marketer) who wants to use their transactional data strategically to make better decisions but doesn't understand the data science approaches being recommended to them.
- The problem
- They have valuable data but lack the knowledge to extract insight from it or to evaluate the tools, consultants, and techniques being pitched to them. They feel anxiety, intimidation, and a fear of being left behind by competitors who are 'doing data science.'
- The plan
- Shore up your spreadsheet fundamentals so you can follow along comfortably.
- Learn each technique by building it by hand on real example data in Excel.
- Understand how to prepare and standardize your data for analysis.
- Evaluate your models with statistical tests and performance metrics.
- Choose the right technique and threshold for your specific business problem.
- The payoff
- You can identify data science opportunities within your own organization. · You can prototype solutions, correctly buy data science products, and delegate the right approaches to developers. · Your data anxiety is replaced with excitement and ideas for taking your business to the next level.
See our guide
- Practice Business Intelligence & Data Science →
- The Four-S Discipline — One System, Four Capabilities →
- AI Safety, Governance & Alignment Practice (FIELD GUIDE (ALL LEVELS)) →
- Build The Data And AI Systems Behind Analytics →
- Ship Analytics, Don't Just Prototype It →
- Get The Business To Actually Use It →
- Build A Model That Holds Up Outside Your Sample →
- Get The Data Trustworthy First →
Related profiles we’ve built
- A Practical Guide To Conjoint Analysis →
- Analytics at Work: Smarter Decisions, Better Results →
- Big Data: A Revolution That Will Transform How We Live, Work, and Think →
- Big Data_ A Very Short Introduction (Very Short Introductions) →
- Business Adventures →
- Business Intelligence Guidebook: From Data Integration to Analytics →
- Data Mining for Business Analytics: Concepts, Techniques, and Applications →
- Data Warehouse and Data Mining →
Additional reading
- The AIMMS optimization modeling book · AIMMS
Recommended as an 'incredible resource' for readers who want to learn more about linear and integer programming, with particularly useful 'Tips and Tricks' chapters.
- Data Mining with R: Learning with Case Studies · Luis Torgo
Presented as a 'great next step' for learning how to implement machine learning models in the R programming language, moving beyond the spreadsheet-based prototypes in the book.
- Forecasting, Time Series, and Regression · Bowerman et al.
A recommended textbook for those wishing to dive deeper into forecasting techniques beyond the exponential smoothing methods covered in the book.
- Free online forecasting textbook (otexts.com/fpp/) · Rob J. Hyndman
Highlighted as a key free resource from a leading expert whose work and R packages are foundational to modern time series analysis.
- Beginning R: The Statistical Programming Language · Mark Gardener
Recommended as an introductory text for readers looking to learn the R programming language after the brief tour in Chapter 10.
Featured in these guides
- Ship Analytics, Don't Just Prototype It →Cited source
- Practice Business Intelligence & Data Science →Cited source
- AI Safety, Governance & Alignment Practice (FIELD GUIDE (ALL LEVELS)) →Cited source
- Get The Business To Actually Use It →Cited source
- Build A Model That Holds Up Outside Your Sample →Cited source
- Build The Data And AI Systems Behind Analytics →Cited source
- Get The Data Trustworthy First →Cited source
Co-cited books
- Data Warehouse and Data Mining · Jugnesh Kumar
- Data Science from Scratch: First Principles with Python
- An Introduction to Statistical Learning: with Applications in R
- Machine Learning and Data Science
- Business Intelligence Guidebook: From Data Integration to Analytics · Rick Sherman
- Practical Statistics for Data Scientists
- Python for Data Analysis
- Introduction to Statistical and Machine Learning Methods for Data Science