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, by Scott E. Page
PDF Download , by Scott E. Page
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Product details
File Size: 12456 KB
Print Length: 398 pages
Page Numbers Source ISBN: 0465094627
Publisher: Basic Books (November 27, 2018)
Publication Date: November 27, 2018
Language: English
ASIN: B07B8LHBVZ
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Amazon Best Sellers Rank:
#178,201 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
In this well-organized and well-edited book, Page clearly demonstrates why his course in Coursera is a hugely popular one. Anyone who has to deal with (or would like a more formal approach for thinking about) strategy, policy, processes, or systems will significantly benefit from this book. One could argue that the models presented are not new - and thats true; what is beneficial to the reader is the well-orchestrated progression of types of models and their assumptions, structure, limitations and examples.Page first makes a well-reasoned plea in favor of approaching any problem with a diverse set of models - each have various assumptions and may therefore implicitly capture (or discard) certain aspects of the system. After motivating the reader with basics of distributions, Page covers an extensive range of models ( a glimpse of the table of contents is sufficient for a potential reader to understand that various modeling techniques and learning mechanisms are covered). Page uses a variety of examples drawn from various domains, though skewed to policy/social sciences. The common theme is mostly "think strategically, in a tractable, explainable way'. The earlier chapters discussing aspects of a model in itself is a useful discussion. The book's organization largely follows his approach in his online course – a “visit to the zoo†- the order in which you visit the exhibits is not that crtitical (nor is this is a book to be necessarily read linearly).While the editing of the book is excellent (no extraneous information, fillers), the book is essentially a text book or a reference source. A student in engineering, political sciences, business/management and any practitioner in these fields will benefit from this book. Most of the content (if not all) is available for free in slightly different formats via his course. However, the utility of having a well-produced book that one can leaf through and pick models to investigate is a huge bonus. There are some changes that makes the content more streamlined too. One wishes there were more examples or challenges to the reader as a "homework'. Nevertheless, this is a must-have book for any one interested in modeling strategic and operational decisions and informatics/machine learning students.(Review Based on an ARC via NetGalley)
the most important thing to know about this 'textbook-like' work is that it takes the 'many model/ensemble' approach very, very seriously and pushes readers hard to think through the fundamentals of how they want to get value from their data....that said, page does an excellent job of addressing/describing those fundamentals....speaking as someone whose exposure to 'model building' came from horribly taught 'econometrics' classes and computationally-intensive engineering classes, i think page's approach far better than what i see in most university econ/engineering curricula.....and, honestly, this would be a terrific text for aspiring MBAs/execMBAs with 'equation-free' educations.....
I have been waiting for this book for 20 years. Too often, leaders of businesses and nonprofits are unaware of the many ways of seeing the world, of using different lenses. The book is not just about data and (clearly described, intuitive) math, it is about thinking logically and better understanding every aspect of the world. It should go beside Lars Skyttner's book on Systems Theory and the books Turning Numbers into Knowledge and Data Science for Business on the thinking person's bookshelf. It is never too late to exercise your brain!
Great book for anyone to read. You might also consider taking the MOOC Modeling Thinking course -- the two go really well together!
At first I thought this was an odd, over the top skim that lacked real meat. But as the author surveys more methods and starts to contrast them, the book turns into a sort of dictionary of models. It can serve as an important reference to anyone who builds models, especially as a source of alternatives, ways to branch out and look at your data in a new way, to see the strengths and limitations of methods you may be using -- or intend to use -- as defaults. The coverage is so broad, that even veterans may find new perspectives.
I’ve been a huge fan of the author. I watched several of his lecture series on Coursera and The Great Courses. The man is a genius. And this book opened my eyes to a whole new way of looking at the world. A must read.
Well written, very crafted design in the structure, carefully balanced between math formulas, intuition insight, and practical applications. Very unique in its kind.
Excellent book that has practical use.It covers many great topics and models for dealing with complex systems, such as convexity, game theory, network models, etc.Well worth a read and as a reference for future work.
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