CS2013-final-report

Prospective computer science majors, in particular, can benefit from learning to program in the context of
scientific applications. A computer scientist needs the same basic background in the scientific method and the
same exposure to the role of computation in science as does a biologist, an engineer, or a physicist.
Indeed, our interdisciplinary approach enables us to teach prospective computer science majors and prospective
majors in other fields of science and engineering in the same course. We cover the material prescribed by CS1, but
our focus on applications brings life to the concepts and motivates students to learn them. Our interdisciplinary
approach exposes students to problems in many different disciplines, helping them to more wisely choose a major.
Our reach has expanded beyond the sciences and engineering, to the extent that we are one of the most popular
science courses taken by students in the humanities and social sciences, as well.
Body of Knowledge coverage
KA Knowledge Unit Topics Covered Hours
SDF
Algorithms and Design
The concept and properties of algorithms.
The role of algorithms in the problem-solving process.
Problem-solving strategies. Implementation of algorithms.
Fundamental design concepts and principles
2
SDF
Fundamental
Programming Concepts
Basic syntax and semantics of a higher-level language.
Variables and primitive data types. Expressions and
assignments. I/O. Conditional and iterative control structures
3
SDF
Fundamental Data
Structures
Arrays, stacks, queues, priority queues, strings, references,
linked structures, resizable arrays. Strategies for choosing the
appropriate data structure.
3
SDF
Development Methods
Program correctness. Modern programming environments.
Debugging strategies.
Documentation and program style.
3
PL
Object-Oriented
programming
Object-oriented design, encapsulation, iterators. 2
PL
Basic Type Systems
Primitive types, reference types. Type safety, static typing.
Generic types.
2
AL
Basic Analysis
Asymptotic analysis, empirical measurements. Differences
among best, average, and worst case behaviors of an algorithm.
Complexity classes, such as constant, logarithmic, linear,
quadratic, and exponential. Time and space trade-offs in
algorithms.
2
AL
Algorithmic Strategies
Brute-force, greedy, divide-and-conquer, and recursive
algorithms. Dynamic programming, reduction.
2
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