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Books

We are testing out a policy. When we go to the shelf for a work related book, we make note of it in the list below, including a brief annotation of what's there and how the book might be useful. The books on the list range from classics to think pieces to recipe books on a particular subject. The criteria for appearing here is that we found the book useful in some way and want to capture that. We are sharing this on our web site because the information might be useful to others as well. Books are listed alphabetically by title, but Ctrl-F is your friend.


Advanced Engineering Mathematics, Erwin Kreysig, Wiley, 1972 (newer editions exist).
The classic book on mathematical methods in engineering. Not a bad book. It is a compendium. Everything is there, though nothing is particularly deep. Often has just the thing you were looking for. 

An Introduction to Mathematical Modeling, Edward A. Bender, Dover, 2000 (original edition 1978).
Aging but still very useful book on modeling. Good discussion of stability theory.

Analysis of Numerical Methods, Eugene Isaacson and Herbert Keller, Dover, 2012 (original edition 1966).
Old and out of date except for the classic preface. If you string together the first letters of each sentence of the preface they spell out, "Down with computers and their lackeys". That alone makes it worth having a copy. 

Approximation Theory and Approximation Practice, Lloyd N. Trefethen, SIAM, 2012.
A good book that explains how to use several classes of polynomials (particularly Chebyshev polynomials) to approximate other functions, and why these approximations have better properties than the functions they approximate. This is a really good book, but relies a bit too much on Trefethen's chebfun library.

Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, Mariner Books, 2013.
Lays out a good taxonomy of data science and identifies different value propositions. Kind of a "big think" book but highly recommended. 

Bioinformatics for Dummies, Jean-Michel Claverie and Cedric Nortedame, Wiley, 2003.
Mostly a how to on bioinformatics tools for Windows. Good so far as that goes.

Chebyshev and Fourier Spectral Methods, John P. Boyd, Dover 1969 (Dover edition 2001).
A bit sloppy discussion of spectral co-location methods, but a reasonable complement to Trefethen's book. The code samples here tend to be old-style Fortran. 

Differential Equations with Mathematica, Martha L. Abell and James P. Braselton, Academic Press, 1997.
Seems to be a companion book to Mathematica By Example, except all of the examples are for problems in differential equations. Useful to get a jump start on a problem. Which Mathematica function do I use? What are the appropriate parameters to pass to it, Some of it is dated. For instance, the functions for doing phase portraits have changed since publication, but useful nonetheless.

Distribution Theory and Transform Analysis: An introduction to Generalized Functions with Applications, A.H. Zemanian, Dover, 1965 (Dover edition 1987).
A reasonably tough slog through Schwartz functions. Useful, but definitely start with Osgood's lectures on Fourier Analysis. 

Elementary Numerical Computing with Mathematica, Robert D. Skeel and Jerry B. Keiper, McGraw-Hill, 1993 (later editons exist).
Misleading title; this book has little to do with Mathematica (but some) and a lot to do with numerics. 

Fact-based Branding in the Real World: A Simple Survival Guide for CMOs and Brand Managers, Rolf Wulfsberg, Siegel and Gale, 2012.
A surprisingly good look at the dimensions of branding problems. Lays out a useful framework. Very useful if you are wondering how to use analytics to understand and improve your brand.

The Fifth Discipline: The Art and Practice of the Learning Organization, Peter M. Senge, Doubleday, 1990.
The classic treatment on what an organization has to do to become a "learning organization", and what a learning organization is.

Functional Programming in Scala, Paul Chiusano and Runar Bjarnason, Manning, 2015.
This is a very unusual book for Manning. It is difficult material on the theory and practice of functional programming using Scala as the target language. Deals with the monads, monoids, functors, and all that. It is not an easy book but it is a useful one. It has seen use as a textbook in functional programming courses at universities.

Introduction to Ordinary Differential Equations, Shepley L. Ross, Xerox, 1966.
Originally, this old book was recommended by Prof. Don Short, who had a very strong interest in ODEs. The book is pretty good and covers all of the standard methods of solution. It also has a good chapter on systems of ODEs. 

Linear Algebra Done Right, Sheldon Axler, Springer, 2004.
A fairly simple treatment of the subject whose most noteworthy element is the absence of determinants. Determinants are difficult to contend with numerically, but are sometimes useful theoretically. That leaves a bit of a gap; you prove theorems one way and implement another. Axler tries, with some success, to do a determinant-free course on linear algebra to close that gap. 

Mathematica by Example, Martha L. Abell and James P. Braselton, Academic Press, 1997.
"By Example" is an appropriate title. Lots of little examples. Easier stuff than Mathematica Navigator. Not as deep by any means. 

Mathematica Data Visualization, Nazmus Saquib, Packt, 2014.
Current. Takes over where Mathematica Graphics leaves off, but Mathematica Graphics an easier read.

Mathematica Navigator: Graphics and Methods of Applied Mathematics, Heikki Ruskeepaa, Academic Press, 1999.
Old but still really useful. If you are only going to have one book about Mathematica, this one is a good choice.

Mathematica Graphics: Techniques and Applications, Tom Wickham-Jones, Springer, 1994.
Old, but still has lots of good examples of how to get Mathematica plots (and other graphics) to do what you want. Mathematica Data Visualization is a newer book that is not as rich, but fills in some gaps.

Matrix Computations, Gene Golub and Charles Van Loan, 2012 (several earlier editions cover almost identical material).
A good reference on the subject but notoriously difficult to learn from. Trefethen's Numerical Linear Algebra is a good source for learning the material. 

Max-Plus Methods for Nonlinear Control and Estimation, William M. McEneaney, Birkhauser, 2006.
"The Green Monster". An exceedingly difficult but important book if you are interested in non-linear control problems for which earlier methods were computationally unfeasible.

Neo4J in Action, Aleksa Vukotic and Nicki Watt, Manning, 2015.
A very useful book on getting up and running with Neo4J. Examples are in Java, but we found that we could get the same functionality in Scala without too much trouble.

Numerical Analysis: Mathematics of Scientific Computing, David Kincaid and Ward Cheney, Brooks/Cole, 2002.
A reasonably good one stop shop on numerical analysis. If you only have room for one book on numerical analysis on your bookshelf, Kincaid and Cheney is a reasonable choice.


Numerical Linear Algebra, Lloyd Trefethen and David Bau, SIAM, 1997. 
A "lecture-oriented" approach to the subject, and a pretty good compliment to Golub and VanLoan.

PowerShift: Knowledge, Wealth, and Violence at the Edge of the 21st Century, Alvin Toffler, Bantam, 1991.
A think piece. Well ahead of its time. Presents a powerful paradigm. 

Practical Data Science with R, Nina Zumel and John Mount, Manning, 2014.
By comparison to R in Action, this book focuses more on data science and less on R. Some good tutorials. 

R in Action: Data analysis and graphics with R, Robert I. Kabacoff, Manning, 2015. 
A cookbook, but a reasonably good cookbook for all kinds of things you can do with R.

Spectral Methods in Matlab, Lloyd N. Trefethen, SIAM 2000. 
This book is like a companion to his later Approximation Theory and Approximation Practice. The Approximation book is about using Fourier and Chebyshev (mostly Chebyshev) polynomials to approximate functions. This book is about a way to apply approximation functions to solve differential equations using spectral methods that give super-polynomial accuracy. It is Matlab centric, but the ideas flow to other computational systems. 

Why Information Grows: The Evolution of Order, from Atoms to Economies, Cesar Hidalgo, Basic Books, 2015.
Hidalgo is an MIT professor but there is essentially no math here. It's a think piece. He considers information to be one of the essential components of the universe along with matter and energy. He adds that there are places in the universe where matter is concentrated, like black holes, and places where energy is concentrated, like pulsars, and there are also places where information is concentrated, like Earth. Understanding Earth as a focal point of information concentration provides insights into such phenomena as economic inequality, manufacturing, securities values, etc.

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