About the University of Waterloo's computer science programs... (Sort've off-topic)

[Caeliferum]

Hey. I'm nearing the big decision myself, and have heard some good things about the compsci courses at Waterloo. Now, I know a good deal of the community here attends the school, so this isn't completely off-topic. My big question is: what exactly are the differences between Waterloo's Computer Science (WCS) and Software Engineering (WSO) programs?

[Cervantes]

There's a lot of differences. The big difference is that CS is part of the Math Faculty, whereas SE is jointly part of the Math and Engineering faculties. As such, software engineers get a lot of engineering courses, including the professional engineering courses. Also, this means that software engineers have way less electives than CS majors. I think the first elective comes in 2A or 2B for software engineers, whereas CS majors get 2 electives in 1A and 2 more in 1B. Software engineers have to take courses like physics and chemistry in 1A and 1B, respectively.

Since SE is part of engineering, software engineers have to be co-op. Also, they are automatically Stream 8, which is nice. (That means you start your first co-op term 8 months after you get here. So you start after two learning terms, in the summer. It gives you more knowledge to go out into the workforce with.) CS majors don't have to be co-op, and also don't have to be stream 8.

If you want a philosophy of the two majors, then I'd tend to say that software engineering is more focussed on actually creating things--that could mean that a software engineer is a programmer, or, more likely, a leader of a team of programmers or perhaps something bigger. CS majors would be less likely to be the coordinator, simply because they have less professional motivation. They aren't engineers, and haven't taken the professional engineering courses, and aren't even necessarily in co-op. (The professional engineering courses teach you things like how to write reports... you know, business stuff.) CS majors are more theory oriented. I tend to think of CS majors as designing the algorithms for a program rather than actually coding something. Really, a lot of the coding is probably done by some college graduate or by some intern or co-op student.

Keep in mind I'm in Math, not CS or SoftEng, so others are probably more credible than I.

[haskell]

Well, it all boils down to the differences between the two fields.

Computer Science

Computer Science, or computing science, is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. Computer science has many sub-fields; some emphasize the computation of specific results (such as computer graphics), while others (such as computational complexity theory) relate to properties of computational problems. Still others focus on the challenges in implementing computations. For example, programming language theory studies approaches to describing computations, while computer programming applies specific programming languages to solve specific computational problems with solutions. A further subfield, human-computer interaction, focuses on the challenges in making computers and computations useful, usable and universally accessible to people.

Despite its name, much of computer science does not involve the study of computers themselves. In fact, the renowned computer scientist Edsger Dijkstra is often quoted as saying, "Computer science is no more about computers than astronomy is about telescopes." The design and deployment of computers and computer systems is generally considered the province of disciplines other than computer science. For example, the study of computer hardware is usually considered part of computer engineering, while the study of commercial computer systems and their deployment is often called information technology or information systems. Computer science is sometimes criticized as being insufficiently scientific, a view espoused in the statement "Science is to computer science as hydrodynamics is to plumbing" credited to Stan Kelly-Bootle[12] and others. However, there has been much cross-fertilization of ideas between the various computer-related disciplines. Computer science research has also often crossed into other disciplines, such as artificial intelligence, cognitive science, physics (see quantum computing), and linguistics.

Fields of computer science

Computer science searches for concepts and proofs to explain and describe computational systems of interest. It is a science; because given a system of interest, it performs /analysis/ and seeks general principles to explain that system[citation needed]. As with all sciences, these theories can then be utilised to synthesize practical engineering applications, which in turn may suggest new systems to be studied and analysed. While the ACM Computing Classification System can be used to split computer science up into different topics of fields a more descriptive break down follows:

Mathematical foundations

Mathematical logic
Boolean logic and other ways of modeling logical queries; the uses and limitations of formal proof methods
Number theory
Theory of proofs and heuristics for finding proofs in the simple domain of integers. Used in cryptography as well as a test domain in artificial intelligence.
Graph theory
Foundations for data structures and searching algorithms.
Type Theory
Formal analysis of the types of data, and the use of these types to understand properties of programs — especially program safety.

Theory of computation

Automata theory
Different logical structures for solving problems.
Computability theory
What is calculable with the current models of computers. Proofs developed by Alan Turing and others provide insight into the possibilities of what may be computed and what may not.
Computational complexity theory
Fundamental bounds (especially time and storage space) on classes of computations.
Quantum computing theory

Algorithms and data structures

Analysis of algorithms
Time and space complexity of algorithms.
Algorithms
Formal logical processes used for computation, and the efficiency of these processes.
Data structures
The organization of and rules for the manipulation of data.

Programming languages and compilers

Compilers
Ways of translating computer programs, usually from higher level languages to lower level ones.
Programming languages
Formal language paradigms for expressing algorithms, and the properties of these languages (e.g. what problems they are suited to solve).

Concurrent, parallel, and distributed systems

Concurrency
The theory and practice of simultaneous computation; data safety in any multitasking or multithreaded environment.
Distributed computing
Computing using multiple computing devices over a network to accomplish a common objective or task and there by reducing the latency involved in single processor contributions for any task.
Parallel computing
Computing using multiple concurrent threads of execution.

Software engineering

Formal methods
Mathematical approaches for describing and reasoning about software designs.
Software engineering
The principles and practice of designing, developing, and testing programs, as well as proper engineering practices.
Reverse engineering
The application of the scientific method to the understanding of arbitrary existing software
Algorithm design
Using ideas from algorithm theory to creatively design solutions to real tasks
Computer programming
The practice of using a programming language to implement algorithms

Computer architecture

Computer architecture
The design, organization, optimization and verification of a computer system, mostly about CPUs and Memory subsystem (and the bus connecting them).
Computer organization
The implementation of computer architectures, in terms of descriptions of their specific electrical circuitry
Operating systems
Systems for managing computer programs and providing the basis of a useable system.

Communications

Game theory
Recently game theory has drawn attention from computer scientists because of its use in artificial intelligence and cybernetics.

Networking
Algorithms and protocols for reliably communicating data across different shared or dedicated media, often including error correction.

Cryptography
Applies results from complexity, probability and number theory to invent and break codes.

Computer Audio
Algorithms and data structures for the creation, manipulation, storage, and transmission of digital audio recordings. Also important in voice recognition applications.

Databases

Relational databases
Data mining
Study of algorithms for searching and processing information in documents and databases; closely related to information retrieval.

Artificial intelligence

Artificial intelligence
The implementation and study of systems that exhibit an autonomous intelligence or behaviour of their own.
Automated reasoning
Solving engines, such as used in Prolog, which produce steps to a result given a query on a fact and rule database.
Robotics
Algorithms for controlling the behavior of robots.
Computer vision
Algorithms for identifying three dimensional objects from a two dimensional picture.
Machine learning
Automated creation of a set of rules and axioms based on input.

Soft computing

A collective term for techniques used in solving specific problems. See the main article.

Computer graphics

Computer graphics
Algorithms both for generating visual images synthetically, and for integrating or altering visual and spatial information sampled from the real world.
Image processing
Determining information from an image through computation.

Human-Computer Interaction

Human computer interaction
The study of making computers and computations useful, usable and universally accessible to people, including the study and design of computer interfaces through which people use computers.

Scientific computing

Numerical algorithms
Numerical solution of mathematical problems such as root-finding, integration, the solution of ordinary differential equations and the approximation of special functions.
Symbolic mathematics
Manipulation and solution of expressions in symbolic form, also known as Computer algebra.
Computational physics
Numerical simulations of large non-analytic systems
Computational chemistry
Computational modelling of theoretical chemistry in order to determine chemical structures and properties
Bioinformatics
The use of computer science to maintain, analyse, store biological data and to assist in solving biological problems such as Protein folding, function prediction and Phylogeny.
Computational neuroscience
Computational modelling of real brains
Cognitive Science
Computational modelling of real minds

Other colleges and universities, as well as secondary schools and vocational programs that teach computer science, emphasize the practice of advanced computer programming rather than the theory of algorithms and computation in their computer science curricula. Such curricula tend to focus on those skills that are important to workers entering the software industry. The practical aspects of computer programming are often referred to as software engineering. However, there is a lot of disagreement over what the term "software engineering" actually means, and whether it is the same thing as programming.

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Computer Engineering

Computer engineering(sometimes also called electronic and computer engineering) is a discipline that combines elements of both electrical engineering and computer science. Computer engineers are electrical engineers that have additional training in the areas of software design and hardware-software integration. In turn, they focus less on power electronics and physics. Computer engineers are involved on all aspects of computing, from the design of individual microprocessors, personal computers, and supercomputers, to circuit design, as well as the integration of computer systems into other kinds of systems (a motor vehicle, for example, has a number of subsystems that are computer and digitally oriented). Common computer engineering tasks include writing embedded software for real-time microcontrollers, designing VLSI chips, working with analog sensors, designing mixed signal circuit boards, and designing operating systems. Computer engineers are also well-suited for research in the field of robotics, which relies on using computers together with other electrical systems.

Besides having a sound knowledge of the mathematics and the sciences which form an integral part of any engineering discipline, computer engineering encompasses topics that are more unique to the discipline. For example, the joint IEEE/ACM Curriculum Guidelines for Undergraduate Degree Programs in Computer Engineering defines the core knowledge areas of computer engineering as

* Algorithms
* Computer architecture and organization
* Computer systems engineering
* Circuits and signals
* Database systems
* Digital logic
* Digital signal processing
* Electronics
* Embedded systems
* Human-computer interaction
* Operating systems
* Programming fundamentals
* Social and professional issues
* Software engineering
* VLSI design and fabrication

Many of the areas of electronic engineering and computer engineering overlap, such as electronics and digital systems which form the basis of electronic components.

Differences Between Computer Science and Computer Engineering

* What are the differences between Computer Science and Computer Engineering?
* The Differences as Described by York University
* Differences as Describes by Duke University

And here are the differences as described by the University of Waterloo:

Quote:

What’s the difference between Computer Engineering, Computer Science, and Software Engineering?

All three disciplines are about computers, including hardware and software systems. Although graduates may be qualified for similar jobs, the programs offered are quite different.

*

Computer Science (CS), offered by the Faculty of Mathematics, covers the broadest spectrum, ranging from mathematical underpinnings, design and development of programs and computer systems to applications such as graphics, databases, and artificial intelligence, to name but a few. CS students obtain a solid foundational core and are encouraged to follow their interests in selecting from the wide range of elective courses. The CS program is more flexible than the CE and SE programs, allowing students to customize their studies.

*

Software Engineering (SE), offered jointly by the Faculties of Engineering and Mathematics, deals with building and maintaining software systems. It is more software-oriented than computer engineering, and puts a greater emphasis on the design and development of large software systems than computer science does. See the Software Engineering web site for more information.

*

Computer Engineering (CE), offered by the Faculty of Engineering, deals with the design, development, and application of computer systems. It is more focused on problems in digital hardware and at the hardware/software interface, and it has a greater emphasis on adapting standard designs and on using tools to solve problems. For more information, visit the Computer Engineering web site.

I hope this has been helpful.

[Hikaru79]

I'm in Computer Science at the University of Waterloo; I also considered SE for a while, but chose CS after all; I have quite a number of friends in SE though, so I know what their side of the pond is like.

Basically, CS gives you a lot more flexibility. In Software Engineering, as Cervantes mentioned, you MUST be in Co-op, and you MUST take certain math courses, and you MUST start with CS133 (the "beginner" CS course). You also get no electives, but you must take Chem and Physics.

CS, on the other hand, gives you a lot of customizability. In first term, you get a choice of 3 Calculus sections (normal, Physics-based, or Advanced), 2 Algebra sections (normal, Advanced), that SE students don't. Your first CS course is also a choice between 125 ("CS stands for Counter Strike, right?"), 133 (the one you MUST take as an SE), 134 ("I r l33t h4xor"), and 135 (the one taught in Scheme; the one Cervantes and I both took and enjoyed. The other two credits are electives. You COULD take Physics and Chemistry like the SE's are *forced* to take (Cervantes and I both did), but you don't have to. I have friends who've taken everything from philosophy to music to absolutely nothing.

In terms of philosophy, Cervantes just about covers it. If you like the thought of coding at some user-level application in the future, go with SE. You'll be getting courses in things like Design Patterns, User Interface Guidelines, etc. If you like the thought of developing algorithms to do voice recognition or something along those lines, go with CS. Of course these are oversimplifications and in fact the two are closely related for most purposes.

[Andy]

If you like programming, take SE. But if you enjoy problem solving through advanced algorithms, go for CS.