What is Reddit's opinion of Discrete and Combinatorial Mathematics: An Applied Introduction, Fifth Edition?

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O cambiar de temas e intentar entender en profunidad las implicaciones de lo visto. Si alguien le da 3 hs seguidas en la misma seccion a Grimaldi se le funde el bocho.

Eso solo de estudiar y entender los conceptos. Hacer ejercicios o resolver los problemas creo que podes meterle facil como dice u/chabon22 varias horas. Total no te sale uno y pasas a otro o cambias a otra guia y ya.

Then take a look at this book it's my favorite one to learn discrete math, you don't need any background it covers pretty much everything you'll need.

I assume people will give you very good links to precalculus books so I'll talk about something else.

When you're in a school (college, university, temple of advanced studies, church of music of higher spheres or whatever you call it) there are roughly two types of CS - classical and modern. Typically, undergrads study the basics of the former while grad students are introduced to the latter.

The most important math for CS undergrads is elementary discrete math. Some examples of books follow below. The last one is free. Note, there is a ridiculous number of books (of all levels: from high school to research level) on this topic. Just look for them and study the ones that speak to you. Also note, the listed books on discrete math are accessible to you if you know grade school math at the level of, say, 7th grade. Trigonometry, calculus and the like are not a prerequisite.

• Discrete Mathematics with Applications By Susanna S. Epp

• Mathematics: A Discrete Introduction By Edward A. Scheinerman

• Discrete Mathematics By Norman Biggs

• Discrete and Combinatorial Mathematics: An Applied Introduction By Ralph P. Grimaldi

• Practical Analysis of Algorithms By Dana Vrajitoru, William Knight

• Math Foundations of Computing By Keith Schwarz

As far as the grad students go, they should be very comfortable with probability. At this point, the basics of real analysis and measure theory become relevant. What follows is an example list of books that would be helpful to grad students.

• Probability and Statistics for Engineers and Scientists By Anthony J. Hayter

• Probability Models for Computer Science By Sheldon Ross

• What Makes Variables Random: Probability for the Applied Researcher By Peter J. Veazie

• The Lebesgue Integral for Undergraduates By William Johnston

• Probability and Computing: Randomization and Probabilistic Techniques in ... By Michael Mitzenmacher, Eli Upfal

• Foundations of Data Science By Avrim Blum, John Hopcroft, and Ravindran Kannan It's a FREE book that was last updated in June, 2017.

One subject that ties all this together and penetrates most of math is linear algebra. It comes in a million different flavors and levels for million different purposes. Some examples of books follow. One side benefit of learning abstract linear algebra is that it makes studying abstract (general, modern) algebra a pleasant walk in the park.

• Linear Algebra: Step by Step By Kuldeep Singh

• Linear Algebra Done Right By Sheldon Axler

• Advanced Linear Algebra By Steven Roman

Important: learn to be an independent thinker!

You might have a look at rook polynomials as they are connected to certain kinds of permutations called derrangements, some details about this appear here.

The place I learned about them was Grimaldi's Discrete and Combinatorial Mathematics

I recommend this: http://www.amazon.com/Discrete-Combinatorial-Mathematics-Applied-Introduction/dp/0201726343

I use Grimaldi whenever I teach Discrete.

I'd recommend Grimaldi's Discrete and Combinatorial Mathematics. I've taught several courses out of it now and have been very happy with it.

I'm using Discrete and Combinatorial Mathematics. I dug around and found some helpful things online, thanks though.