Coming soon · Book Profile
Machine Learning and Data Science
A practical, math-light introduction to applying statistical learning and machine learning methods using the R programming environment across the full data science workflow.
A profile of this book is on the way.
What it’s about
Written for analysts, software developers, and researchers who want to move beyond spreadsheet-level analytics, this book casts machine learning as the scientific method applied to data. Organized to mirror an actual data science project, it walks readers step-by-step from data access and munging, through exploratory data analysis, into supervised methods (regression and a broad battery of classifiers), model performance evaluation, and finally unsupervised learning. Every concept is grounded in runnable R code using base packages and well-known data sets, deliberately omitting heavy mathematics so newcomers can become productive quickly while still understanding the workflow, the pitfalls (overfitting, bias-variance, confounders, data leakage), and how to iterate toward better predictive power.
The through-line
- Who it’s for
- An analyst, software developer, or researcher who wants to expand beyond basic tools and become productive doing machine learning and data science.
- The problem
- They need to access, clean, model, and predict from real data but lack a practical end-to-end methodology and a usable toolset. They feel intimidated by the mathematics and overwhelmed by the hype and jargon surrounding data science.
- The plan
- Understand the problem and access an appropriate data set into R.
- Mung the data into a tidy, reproducible form.
- Explore the data with numeric summaries and plots.
- Engineer features and select a supervised or unsupervised model.
- Train, validate, and evaluate the model with cross validation and proper error metrics.
- The payoff
- The reader can confidently take a raw business problem, build and evaluate a predictive model in R, and communicate actionable results. · They have a reusable data pipeline and a growing personal library of machine learning techniques. · They can recognize and avoid overfitting, confounders, and data leakage.
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.