Anyone interested in adaptive learning will enjoy these seven books. None are specifically about adaptive learning but all have examples of the use of algorithms and big data in education. This is not a definitive list, but has been selected using a normally reliable data set: actual sales!
Freakonomics, and its sequel Superfreakonomics, were the breakthrough books that put big data on the map. Big on examples, low on theory, they focus on counterintuitive findings that surprise readers and experts alike. Moneyball is the hugely entertaining thriller that saw equations trump experts in baseball. Super Crunchers swings more towards predictive analytics and has far more theory about statistics, especially regression and randomised trials, although it also deals with the dark side of data. Then there’s the books on algorithms that show how they’ve come to rule our world, change the future by playing a major role in real world solutions to real world problems as well as becoming usefully predictive. Lastly there’ s Big Data, which tells the data side of the revolution , where terabytes of data are brought to bear on real problems, shaping things even further.
1. Freakonomics by Steven Levitt
Freakonomics was the breakthrough book that put big data on the map. Big on examples, low on theory, it has a dazzling set of counterintuitive findings on everything from why drug dealers still live with their moms to parenting.
2. Superfreakonomics by Steven Levitt
The sequel Superfreakonomics, although not as dazzling, follows up with global cooling, patriotic prostitutes and why suicide bombers should buy life insurance. Another fine set of results from big data that beat intuition and expert opinion. My favourite line, “on average 1643 fatalities per year during Iraq & Afghan wars but…over same stretch when the USwere fighting no wars it was more than 2000 per year”.
3. Moneyball by Michael Lewis
Bill James changed baseball forever with his data driven approach at the Oakland Athletics. He ignored the fact that players were fat or trouble off the field. It’s a stirring tale and a real page turner, even if you’re not a sports fan.
4. Super Crunchers by Ian Ayres
Super Crunchers swings more towards predictive analytics and has far more theory about statistics, especially regression and randomised trials, although it also deals with the dark side of data. My favourite line, on why Doctors don’t like computers “Unlike pilots, doctors don’t go down with their planes”.
5. Automate this. How algorithms came to rule our world by Christopher Steiner
Has an interesting history of algorithms, with a focus on algorithmic stock trading, but also case studies on predicting hit movies and music, sport, gambling, medicine and so on. Also good on why algorithms matter, what the talent is and where it’s heading.
6. Nine Algorithms That Changed the Future by John MacCormick
A neat idea that selects big-ticket, scalable algorithms that solve specific, real world problems and are used by us all, namely: 1. Search Engine Indexing; 2. Pagerank; 3. Public Key Cryptography; 4. Error Correcting Codes; 5. Pattern Recognition; 6. Data Compression; 7. Databases; 8. Digital Signatures; 9. What is computable?
7. Predictive Analytics by Eric Siegel
A more focused and detailed book on the use of analytics to predict human behaviour. The power of prediction is a valuable commodity as just tiny amounts of predictive certainty can have profound effects on decision making, outcomes and impact. The case study on IBMs ‘Watson’ winning Jeopardy! is priceless.
8. Big Data by Mayer-Schonberger & Cuckier
Big data is now big business, where megabytes mean megabucks. Non-digital data is only 2% of the total and the more searching, buying, selling, communicating, dating, banking, socialising and learning, we do, the more data there is to feed algorithms that improve with these big numbers. This is a comprehensive primer for Big Data, with profound discussions about how statistics flips when N=all, scarcity of data disappears and massive correlation replaces causation.
All of these books have several things in common. They are readable, packed with great examples, case studies and deal with real world solutions to real world problems. Refreshingly, many of them are also honest and realistic about the downsides. They are not for the techie or stats freak but give detailed accounts of why algorithms are awesome and big data is a big deal.
Forthe learning professional they all have relevant education examples but their real value is as primers on specific topics, namely; algorithms, predictive analytics and big data. You will find yourself, time and time again, seeing educational parallels when you read the non-educational examples and case studies.
Detailed reviews on all of these books coming soon….