Coming soon · 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.
A profile of this book is on the way.
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
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
- Competing on Analytics: The New Science of Winning · Thomas H. Davenport and Jeanne G. Harris
The authors' previous book, which provides the strategic context by describing the earliest and most aggressive adopters of analytics. This book builds on it by providing a 'how-to' guide for all organizations.
- Sources of Power: How People Make Decisions · Gary Klein
Discusses decision-making in high-pressure situations where there is no time for systematic data gathering, providing a contrast to the analytical approach and showing when intuition is necessary.
- The Black Swan: The Impact of the Highly Improbable · Nassim Nicholas Taleb
Argues that statistical analysis is limited because it cannot predict rare, high-impact 'black swan' events, serving as a cautionary note on the limits of analytics.
- Moneyball: The Art of Winning an Unfair Game · Michael Lewis
A popular case study of how the Oakland A's baseball team used an analytical approach to player selection to compete with richer teams, illustrating the power of competing on analytics.
- Why Great Leaders Don't Take Yes for an Answer · Michael Roberto
Describes how to foster a culture of constructive conflict and debate in decision-making processes, which is essential for an analytical culture where assumptions are tested and merit triumphs over politics.
- The Visual Display of Quantitative Information · Edward Tufte
A foundational work on how to create clear visual representations of data, a key skill for communicating analytical findings effectively.
- Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart · Ian Ayres
The book discusses how statistical analyses are replacing human intuition and expert judgment in decision-making, a core theme related to the discussion of 'Moneyball' and the demise of the expert.
- Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed · James Scott
Documents how governments' fetish for quantification and data can lead to misguided and harmful policies, providing a deep historical context for the book's warnings about the 'dictatorship of data'.
- The War Managers · Douglas Kinnard
A survey of U.S. generals' views on the Vietnam War, revealing that the 'body count' metric was seen as a worthless and inflated measure of progress, illustrating the dangers of relying on flawed data.
- Thinking, Fast and Slow · Daniel Kahneman
Explains the cognitive biases that lead humans to see illusory causal links, which the author's argue big data correlations can challenge and disprove.