Beginning Programming All-In-One Desk Reference For Dummies
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Review by WhoAmI for Beginning Programming All-In-One Desk Reference For Dummies
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I’ve been programming in a high level language for years without having a CS background and have been looking for a “principle of programming & languages” type of book to round up the self-education. There are expensive textbooks around but this inexpensive and gentle alternative fills the bill nicely!
Review by pilotbillp for Beginning Programming All-In-One Desk Reference For Dummies
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This book explains simple programming very clearly for someone who last worked with BASIC in the 1980′s. After borrowing a library copy, we wanted a copy to keep.
Review by charlie anderson for Beginning Programming All-In-One Desk Reference For Dummies
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I am not a big fan of the Dummies Books or any “how to ” book in which the author injects so called “humor” This book-however-written by a part time comedian no less- is outstanding! Why??? Because he can tell you in a few short sentences of plain concise English just what object oriented programming is.
This is no small easy feat! And I am dead serious. 99% of programming books are total incomprehensible gibberish for the non-programmer, for they are never written through the eyes of the reader for whom they are intended. One so called reviewer gave this fine volume two stars! He would rather have you read a book on the “Psychology of Programming”!!! The Silver Edition I should mention.
Beginning Programming is a very fine book for anyone wanting an overview of programming written in a clear concise style. This one previous sentence is high praise indeed for it is a rare occurrence.
Review by Belladonna Triste for Beginning Programming All-In-One Desk Reference For Dummies
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Okay, this book is well-written, it covers a lot of very basic programming concepts in a way that I think most people would find user-friendly, but it is also full of blips of technical inaccuracy. Because of these problems, I can’t really recommend the book.
If you really want a really fast, easy to read intro to programming and you don’t care if you learn a lot of little inaccuracies on the way, well, you might find this useful. But you might find a whole lot of intro to programming books useful. Just sayin.
As to the inaccuracies: let’s take Book I, Chapter 1. Wang states that assembly language is easier to use than machine code because “one assembly language command can replace a dozen or more machine language commands.” Um, sorry, but assembly languages have an exact one-to-one correlation with machine language. The reason assembly language is easier is because it will say something like “mov b3 b4″ (move whatever is in b3 over to b4) instead of “10001 1110 0001 1100.” But each assembly language command replaces just one machine language command.
Then, in Chapter 2, we have the statement that “structured programming teaches programmers that any program can be divided into . . . sequences, branches, and loops.” Um, actually, spaghetti code also includes sequences, branches and loops. None of that stuff is in any way unique to structured programming, nor is it a sign that a program is structured.
Okay, I could list a bunch of stuff like this, and you’d say, “If I’m never going to program in assembly language, why would it matter?” “If I’m never going to have to tell anyone whether my code is structured or not, why would i care?” Well, it just seems that an entry level programming book should not give you inaccurate data. It’s always a pain to be in a conversation about programming and find you are accidentally spewing out wrong “facts.” It can easily make you look bad on the job or in a social situation. Not to mention the fact that the more serious you get about programming, the more of a pain it is to find you have to unlearn stuff you so carefully learned before.
Which leads me that other problem with this text. Serious students of computer science, including those with PhDs, can often be unduly dismissive of “amateurs,” people who have learned to code without taking formal classes or being hyper-qualified mega-geniuses. And yes, that’s a drag, because the proof of a programmer is in his/her code, not the degree(s) earned.
But Wang seems so determined to correct for this that he goes overboard. Talking about how “desire beats technical training every time” and how many important programs were written by people in other fields (some of whom had been intensively studying computer science for years on their own) does not correct the error of assuming that “amateurs” are, well, amateurish. Yes, Mr Wang does admit occasionally that computer scientists do some cool stuff, but he really seems determined to prove that education and study count for little to nothing in the real world.
The reality is that this is a field where both the serious academic and the passionate amateur can make amazing contributions — but putting down the first does not effectively balance things out.
Plus, the attitude that “anyone can program” may be okay for most readers, but heaven help the poor reader who either can’t program or the experienced programmers who must deal with the fellow who thinks that he’s an expert because he’s learned a few algorithms. The first will feel like an idiot, since after all “everyone” can do this, so he must be dumber than “everyone.” The second is not in much trouble *unless* he’s a programming manager. Nothing slows down, bogs down, and destroys programming projects like a member of non-programmer management who believes that “it’s easy — anyone can do it.”
For details on that last, check out The Mythical Man-Month: Essays on Software Engineering, Anniversary Edition (2nd Edition)and/or The Psychology of Computer Programming: Silver Anniversary Edition. Heck, check them out anyway — they give brilliant insights into the world of programming, and can tell you a lot about what makes a programming project succeed or fail. Plus, they are rumored to ward off pointy-haired management for miles around.
Review by Bjarne Stroustrup for Elements of Programming
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I have been wondering what to say about this book and now Peter G. Neumann said it better (see previous review). However, I can still say this: There are many good books, but few great ones. “Elements” is a great book in that it can change the way you think about programming in fundamental ways: If you “get it” programming will never be the same again for you.
Reading “Elements” requires maturity both with mathematics and with software development. Even then it is so different from most books on programming that it can be hard going. The frequent comparisons of “Elements” to Knuth’s “The Art of Programming” is well earned.
Review by Nicholas Charles Ralabate for Elements of Programming
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“I believe that iterator theories are as central to Computer Science as theories of rings or Banach spaces are central to Mathematics. Every time I would look at an algorithm I would try to find a structure on which it is defined. So what I wanted to do was to describe algorithms generically. That’s what I like to do. I can spend a month working on a well known algorithm trying to find its generic representation. So far, I have been singularly unsuccessful in explaining to people that this is an important activity. But, somehow, the result of the activity – STL – became quite successful.” -Stepanov
I had been waiting for this book for a while, as I greatly enjoy Stepanov’s unorthodox views on programming. His flat rejection of the object-oriented paradigm was what caught my attention, but he differed from the unwashed newsgroup naysayers in an important respspect — he offered an alternative. The fact that his alternative seemed to involve applying concepts from the realm of abstract algebra to computer programming made me realize I would be spending a lot of time and thought catching up.
This is a short, but dense book. There is little trace of Knuth’s sympathetic humor or Dijkstra’s aesthetic passion. The material is presented as a series of definitions and sample programs, written in a programming language based on C++. Importantly, there are also exercises and projects throughout each chapter. At first attempt, these puzzlers seem to contain as much insight as the prose itself.
I look at this book as a combination of the two books that Stepanov is known to prescribe to his students, hyper-distilled into a slim few hundred pages:
“The books that I recommend to my students are The Art of Computer Programming by Donald Knuth, which is the great encyclopedia of programming techniques. … It is something that they should keep studying for the rest of their lives. The other book that I urge my students to read is The Textbook of Algebra by George Chrystal. It is a massive two volume work covering most of elementary algebra. Sadly enough, nowadays even people with graduate degrees in Mathematics do not know most of the material in Chrystal.”
More to the point, I look at this book as an intentional challenge. The preface urges the reader to consider why the material absent is absent and vice versa, a sentiment I had only seen in one other place — Victor Vyssotsky’s review of MacLane and Birkhoff. A challenge like that doesn’t make for a pleasant exposition, seemingly trading approachability for a more mature understanding.
Stepanov has some great papers in the public domain — if you are reading this review I highly reccomend seeking them out. Also see the Google Tech Talk “A Possible Future of Software Development” by Sean Parent. If you like those, you will love this.
Review by Peter G. Neumann for Elements of Programming
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What could be one of the most important books for developers of low-risk
systems has come to my attention, and deserves your consideration if you are
serious about understanding the mathematical foundations of programming and
applying them sensibly to your practice. It is not an easy read, but it is
a very compelling approach. To support its mathematically oriented
crispness, the book includes the definition of a small but elegant C++
subset that has been crafted by Sean Parent and Bjarne Stroustrup for
illustrative use in the book. I believe this material should be taught
within all computer science curricula.
A long quote and a short one on the back jacket give an idea of what is
involved:
Ask a mechanical, structural, or electrical engineer how far they would
get without a heavy reliance on a firm mathematical foundation, and they
will tell you, `not far.’ Yet so-called software engineers often practice
their art with little or no idea of the mathematical underpinnings of what
they are doing. And then we wonder why software is notorious for being
delivered late and full of bugs, while other engineers routinely deliver
finished bridges, automobiles, electrical appliances, etc., on time and
with only minor defects. This book sets out to redress this imbalance.
Members of my advanced development team at Adobe who took the course based
on the same material all benefited greatly from the time invested. It may
appear as a highly technical text intended only for computer scientists,
but it should be required reading for all practicing software engineers.
— Martin Newell, Adobe Fellow
The book contains some of the most beautiful code I have ever seen.
— Bjarne Stroustrup
The bottom of the inside cover suggests that through this book you will come
to understand that mathematics is good for programming, and theory is good
for practice. I applaud that sentiment.
Review by Trevor Baca for Elements of Programming
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Because Ralabate’s review covers the most important points, this review should be read as supplemental to it.
First, the book makes substantial contributions towards regularizing the terminology of algorithm design and analysis. Concepts like regularity, transformation, accumulation and ordering that are treated as notional in most other presentations of the material are here given rigorous formalization. This matters because understanding whether a type or procedure is regular (chapter 1) helps explain the availability of the different types of iterator definable against it (chapter 6). Likewise, understanding which algebraic structures are available against the input to an algorithm (chapter 5) can clarify both the boundary cases to the algorithm and the avenues open for its optimization.
Second, because early chapters introduce terminology on which the later chapters build, the book should be read in order. (This point is made in the preface, but bears repeating here.) Chapters, and the examples they contain, are short and relatively easy to accomplish in a sitting or two, which makes sequential reading straightforward. (The exceptions to this are project suggestions, which require a different level of attention but which can be skipped working from chapter to chapter.)
Third, the math on which Stepanov and McJones rely will be familiar to second- or third-year CS or engineering undergrads. Examples are taken from set theory, matrices and the foundations of abstract algebra (groups and rings); advanced understanding of continuous mathematics is not assumed. (Willingness to work out examples with pencil and paper still important, however.)
Fourth, the subset of C++ in which the examples in the book are presented should embolden, rather than discourage, developers working primarily in other languages. With the exception of the templating instructions that wrap each of the examples, there’s nothing particularly C++-ish in the examples. The examples are kept short and they read at least as well as any sort of pseudocode that might have been devised as an alternative. (No problems coming from recent work done primarily in Python, for example.)
Fifth, antagonism between the type of algorithm analysis presented here and the fundamentals of object orientation is misguided. Among very many other things, object orientation affords a type of systems design oriented in terms of *things*. How should data and methods be bundled? Which parts of the system need to be exposed and which encapsulated? Is functionality best acquired through inheritance or aggregation? The type of analysis that Stepanov and McJones provide operates at what is arguably more fundamental a level. How are assumptions of regularity related to optimization? When is special-case optimization warranted? To get a feeling for this, read chapter two on what happens in the repeated application of a transformation to its own output. The code and presentation of the material demonstrate a closed set of possibilities *regardless of the algorithm being investigated*, along with terminology and descriptors for each of the different cases. I find it hard to see how generalization can go much further than this, and the presentation of the material is a pleasure. (Maybe best to use chapter 2 as a litmus test, in fact, to see whether you want to work the book to completion.)
Last, the whole book can be read as a challenge. To what extent can the fundamentals of algorithm design be treated rigorously? It’s probably reaching to draw a comparison to the situation before different parts of the math system were defined in terms of set theory, but, in some respects, that does seem to be where we are: many different, notional approaches to what it means to program, with decades’ worth of best practices built up, but absent a series of definitions on which to anchor different types of programming work. Stepanov and McJones help take us at least a little bit closer to such a long-term goal.
Review by W Boudville for Elements of Programming
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Did you study differential calculus? While not a prerequisite for this book, if you are the type who desires rigorous maths, you almost certainly have done so. There are two teaching methods for calculus. One uses the differential forms (“delta x”, “delta y”)) pioneered by Newton. While the other is stricter and uses the epsilon delta method and theorems. The former is favoured by scientists learning calculus, while mathematicians prefer the latter. This book is analogous to the latter, while the former can be represented by Knuth’s Art of Computer Programming, The, Volumes 1-3 Boxed Set (2nd Edition) (The Art of Computer Programming Series) (Vol 1-3).
Another point of difference is that Stepanov and McJones deal with the foundations of programming, while Knuth delves deeper into the algorithms.
The approach in the book is not difficult to follow, unlike sections of Knuth which can get very intricate. More generally, you will rarely come across a book like this, which deals closely with theorems and lemmas and yet is also tied to actual code.
One consequence however is that the audience may be somewhat exclusive (ie. limited). I can readily see a mathematician wandering into this book, to learn more about the basis of computer science. But the typical computer scientist, let alone a programmer, favours a more informal approach.