Science And Insight
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The Art of Insight in Science and Engineering
Tools to make hard problems easier to solve. In this book, Sanjoy Mahajan shows us that the way to master complexity is through insight rather than precision. Precision can overwhelm us with information, whereas insight connects seemingly disparate pieces of information into a simple picture. Unlike computers, humans depend on insight. Based on the author's fifteen years of teaching at MIT, Cambridge University, and Olin College, The Art of Insight in Science and Engineering shows us how to build insight and find understanding, giving readers tools to help them solve any problem in science and engineering. To master complexity, we can organize it or discard it. The Art of Insight in Science and Engineering first teaches the tools for organizing complexity, then distinguishes the two paths for discarding complexity: with and without loss of information. Questions and problems throughout the text help readers master and apply these groups of tools. Armed with this three-part toolchest, and without complicated mathematics, readers can estimate the flight range of birds and planes and the strength of chemical bonds, understand the physics of pianos and xylophones, and explain why skies are blue and sunsets are red. The Art of Insight in Science and Engineering will appear in print and online under a Creative Commons Noncommercial Share Alike license.
Science from Sight to Insight
Author: Alan G. Gross
language: en
Publisher: University of Chicago Press
Release Date: 2013-11-25
John Dalton’s molecular structures. Scatter plots and geometric diagrams. Watson and Crick’s double helix. The way in which scientists understand the world—and the key concepts that explain it—is undeniably bound up in not only words, but images. Moreover, from PowerPoint presentations to articles in academic journals, scientific communication routinely relies on the relationship between words and pictures. In Science from Sight to Insight, Alan G. Gross and Joseph E. Harmon present a short history of the scientific visual, and then formulate a theory about the interaction between the visual and textual. With great insight and admirable rigor, the authors argue that scientific meaning itself comes from the complex interplay between the verbal and the visual in the form of graphs, diagrams, maps, drawings, and photographs. The authors use a variety of tools to probe the nature of scientific images, from Heidegger’s philosophy of science to Peirce’s semiotics of visual communication. Their synthesis of these elements offers readers an examination of scientific visuals at a much deeper and more meaningful level than ever before.
Data Smart
Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data. Each chapter will cover a different technique in a spreadsheet so you can follow along: Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.