Mathematics
Mathematics is the study of patterns and the logical connections between them. The patterns can be numerical, algebraic, or geometric. The logical connections are typically computations and proofs. When the patterns come from the real world, we get applied mathematics. The logical connections might then take the form of a differential equation that predicts how a disease outbreak will unfold, a statistical model that allows an actuary to assess risks, or a geometric algorithm that displays a three-dimensional object on a flat computer screen. When the patterns come from building up theory from axiomatic truths, we get the myriad sub-disciplines of pure mathematics: real analysis, abstract algebra, topology, non-Euclidean geometry, probability, and many others.
The mathematics major can tailor upper-level courses to his interests (including pure mathematics, applied mathematics and statistics) and career goals (including actuarial science, computer science and secondary education).
Advanced Placement
- A student who gets a 4 or 5 on the AB calculus exam receives immediate credit for MAT-111 Calculus I and receives a placement of MAT-112 Calculus II.
- Any student starting in MAT-112 Calculus II (by the AP exam or our internal placement) who gets a B- or better will receive retroactive credit for MAT-111 Calculus I.
- A student who gets a 4 or 5 on the BC calculus exam receives immediate credit for MAT-111 Calculus I and MAT-112 Calculus II, and receives a placement of MAT-223 Linear Algebra.
- A student who gets a 4 or 5 on the statistics AP exam receives immediate credit for MAT-103 Probability and MAT-104 Statistics.
- A student who gets a 4 or 5 on the Computer Science A AP exam receives immediate credit for CSC-111 Intro to Programming.
- A student who gets a 4 or 5 on the Computer Science principles AP exam receives immediate credit for CSC-101 Intro to Computer Science.
Student Learning Goals
To give all students who take mathematics courses a sense of the nature of mathematics and its place in society.
To give mathematics majors an understanding and appreciation of the fundamental nature of mathematics, and to prepare them to become effective users of mathematics in their careers.
To give mathematics minors an understanding and appreciation of the fundamental nature of mathematics, and to prepare them to become effective users of mathematics in their careers.
To give students interested in pursuing graduate study in mathematics (or related disciplines) an adequate preparation to succeed in that study.
To prepare students to excel in their academic pursuits beyond mathematics. This includes students who take mathematics distribution courses, math minors, and students with additional majors, who will gain deeper insights into their other majors.
Mathematics majors may opt for the Pure Mathematics track, the Computational Mathematics track, or the Financial Mathematics track. There is a great deal of overlap among these choices, and all include the four core courses.
Major in Mathematics
| Code | Title | Credits |
|---|---|---|
| Mathematics Core Courses | ||
| MAT-111 | Calculus I | 1 |
| MAT-112 | Calculus II | 1 |
| MAT-223 | Linear Algebra | 1 |
| MAT-331 | Abstract Algebra I | 1 |
| Track | ||
| Select one of the following tracks: | 5 | |
Pure Mathematics | ||
Computational Mathematics | ||
Financial Mathematics | ||
| Total Credits | 9 | |
Mathematics majors should complete the four core courses by the end of the sophomore year, if possible; they must be completed by the end of the junior year.
Incoming freshmen interested in pursuing mathematics at Wabash College will typically take MAT-111 Calculus I or MAT-112 Calculus II in the fall (depending on placement) and MAT-112 Calculus II or MAT-223 Linear Algebra in the spring. Course choices in the fall of the sophomore year will usually depend on the direction the student sees himself headed. Students should plan to take MAT-331 Abstract Algebra I in the spring of their sophomore year. Potential mathematics majors should discuss their plans with a member of the department and review the flow chart describing prerequisites among the courses for the major. Several courses are offered in alternate years; majors must plan accordingly.
Pure Mathematics Track
| Code | Title | Credits |
|---|---|---|
| MAT-333 | Funct Real Variable I | 1 |
| or MAT-341 | Topology | |
| Mathematics Electives | 4 | |
| Total Credits | 5 | |
Computational Mathematics Track
| Code | Title | Credits |
|---|---|---|
| CSC-111 | Intro to Programming 1 | 1 |
| MAT-337 | Numerical Analysis | 1 |
| or MAT-338 | Topics Computational Math | |
| Mathematics Electives | 4 | |
| Total Credits | 5 | |
- 1
This does not count toward the major, but it is a prerequisite for MAT-337 Numerical Analysis and MAT-338 Topics Computational Math, and should be taken by the sophomore year, if possible.
Financial Mathematics Track
| Code | Title | Credits |
|---|---|---|
| MAT-251 | Mathematical Finance | 0.5 |
| MAT-252 | Mathematical Interest Theory | 0.5 |
| MAT-253 | Probability Models | 0.5 |
| MAT-353 | Probability Models II | 0.5 |
| MAT-254 | Statistical Models | 0.5 |
| MAT-354 | Mathematical Statistics | 0.5 |
| or MAT-355 | Regression Models | |
| Mathematics Electives | 2 | |
| Total Credits | 5 | |
The requirements for the financial mathematics major are good preparation for the initial actuarial exams.
Electives may not include MAT-100 Math Modeling and Precalculus, MAT-103 Probability, MAT-104 Statistics, MAT-106 Topics in Contemporary Math, or MAT-108 Intro to Discrete Structures.
Additional Courses
Additional courses to consider, especially for students who are considering graduate school:
Pure Mathematics
| Code | Title | Credits |
|---|---|---|
| MAT-219 | Combinatorics | 1 |
| MAT-221 | Geometry | 1 |
| MAT-222 | Number Theory | 1 |
| MAT-224 | Differential Equations | 1 |
| MAT-225 | Multivariable Calculus | 1 |
| MAT-323 | Topics in Linear Algebra | 1 |
| MAT-324 | Topics in Differential Equations | 1 |
| MAT-332 | Abstract Algebra II | 1 |
| MAT-334 | Funct Real Variable II | 1 |
| MAT-344 | Complex Analysis | 1 |
Computational Mathematics
| Code | Title | Credits |
|---|---|---|
| MAT-219 | Combinatorics | 1 |
| MAT-222 | Number Theory | 1 |
| MAT-224 | Differential Equations | 1 |
| MAT-225 | Multivariable Calculus | 1 |
| MAT-226 | Operations Research | 1 |
| MAT-235 | Stochastic Simulation | 1 |
| MAT-314 | Modeling With Differential Equations | 1 |
| MAT-324 | Topics in Differential Equations | 1 |
| MAT-332 | Abstract Algebra II | 1 |
Financial Mathematics
| Code | Title | Credits |
|---|---|---|
| MAT-224 | Differential Equations | 1 |
| MAT-324 | Topics in Differential Equations | 1 |
| MAT-333 | Funct Real Variable I | 1 |
Mathematics Minor
| Code | Title | Credits |
|---|---|---|
| MAT-111 | Calculus I | 1 |
| MAT-112 | Calculus II | 1 |
| MAT-223 | Linear Algebra | 1 |
| Mathematics Electives 1 | 2 | |
| Total Credits | 5 | |
MAT-100 Math Modeling and Precalculus
This course develops problem solving skills
fundamental to further study in higher mathematics
through mathematical modeling and applications.
Students will study algebraic and graphical
properties of polynomial, rational, exponential,
logarithmic, and trigonometric functions, with a
focus on using these to build and understand
mathematical models. With a dual emphasis on
sharpening core skills and understanding
applications, this course provides a review of
material relevant for continuing to a full course
in calculus. This course is limited to students
who intend to continue to MAT-111 as a requirement
for his major, but whose placement indicates that
a precalculus course is advisable. While it
satisfies the Quantitative Literacy (QL)
distribution requirement, enrollment in MAT 100 is
only available through instructor permission. For
students who need distribution credit in QL but do
not require a subsequent course in calculus,
MAT-103, MAT-104, MAT-106, and MAT-108 are
recommended. MAT-100 does not count toward a major
or minor in mathematics.
Prerequisites: none
Corequisites: Prerequisite: MAT-100 placement
Credit: 1
Distribution: Quantitative Literacy
MAT-103 Probability
The course introduces students to key measures of
uncertainty (probability) and long-run average
(expected value). Probabilistic reasoning is
applied to a wide variety of interesting in the
areas of medical testing, gambling, game theory,
sports, asset-price modelling, financial
derivatives, insurance, and retirement annuities.
MAT-103 does not count toward the mathematics
major or minor. Credit cannot be given for both
MAT-103 and MAT-253. The course is offered most
semesters.
Prerequisites: none
Credits: 0.5
Distribution: Quantitative Literacy
MAT-104 Statistics
The course looks briefly at some standard
statistics: averages, variances, standard
deviations, medians, and proportions. Correlation
coefficients are introduced and used for
prediction. The classical p-value approach to
claim testing is presented and applied to a wide
variety of testing situations. In addition, the
classical confidence interval approach to
estimation is examined. MAT-104 does not count
toward the mathematics major or minor. (MAT-103 is
not a prerequisite for MAT-104). Credit cannot be
given for both MAT-104 and MAT-254. The course is
offered most semesters.
Prerequisites: none
Credits: 0.5
Distribution: Quantitative Literacy
MAT-106 Topics in Contemporary Math
A study of selected topics dealing with the nature
of mathematical ideas. This course focuses on
mathematics as a creative endeavor. Through
participation and discovery, students will
consider an articulation of mathematics that
focuses on patterns, abstraction, and inquiry.
Topics will vary, but could include logic,
Euclidean geometry, algorithms, etc. This course
does not count toward the major or minor in
mathematics.Topics vary with each scheduled
offering. Refer to Student Planning's section
information for descriptions of individual
offerings, and applicability to distribution
requirements.
Prerequisites: none
Credit: 1
Distribution: Quantitative Literacy
MAT-108 Intro to Discrete Structures
An introduction to discrete mathematics for
students not planning to major in mathematics.
Topics include sets and logic, proof methods,
counting arguments, recurrence relations, graphs,
and trees. This course may be used to meet the
mathematics requirement for the computer science
minor. However, it does not count toward the
mathematics major or minor. Students may not
present both MAT 108 and 219 for credit toward
graduation.
Prerequisites: none
Credit: 1
Distribution: Quantitative Literacy
MAT-111 Calculus I
This course studies the fundamentals of
single-variable calculus, developing analytical
and computational skills appropriate for students
in quantitatively rigorous disciplines. Topics
include limits, continuity, techniques of
differentiation, applications of derivatives, the
Mean Value Theorem, the Intermediate Value
Theorem, the Fundamental Theorem of Calculus, and
the method of substitution for integration.
Prerequisites: MAT-100 with a minimum grade of C-, or MAT-111 placement, or
permission of the instructor
Credit: 1
Distribution: Quantitative Literacy
Equated Courses: MAT-110
MAT-112 Calculus II
This course continues the study of calculus from
MAT-111, developing analytical and computational
skills appropriate for students in quantitatively
rigorous disciplines. Topics include techniques
and applications of integration, numerical
integration, improper integrals, infinite
sequences and series, Taylor series, and an
introduction to multivariable calculus including
partial derivatives and multiple integrals.
Prerequisites: MAT-110 or MAT-111 with a minimum grade of C-, or MAT-112
placement
Credit: 1
Distribution: Quantitative Literacy
Equated Courses: APCR
MAT-178 Special Topics
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credits: 0.5-1
Distribution: Quantitative Literacy
MAT-219 Combinatorics
This course is an introduction to combinatorial
reasoning and discrete mathematics. Topics include
enumeration, combinatorial identities, graph
theory, generating functions, and recurrence
relations. Additional topics may include graph
algorithms, partitions, and partially ordered
sets. Students may not present both MAT 108 and
219 for credit towards graduation. This course is
offered in the spring semester.
Prerequisites: MAT-223
Credit: 1
MAT-221 Geometry
This course studies aspects of the development of
Euclidean and non-Euclidean geometries from a
modern and/or historical viewpoint.
Prerequisites: MAT-112
Credit: 1
MAT-222 Number Theory
A study of elementary number theory. Topics
include divisibility, congruences, properties of
prime numbers, linear Diophantine equations, the
Euler phi function, primitive roots, and
additional topics. Such topics may include public
key cryptography, quadratic reciprocity, and
Pythagorean triples. This course is offered in the
spring semester.
Prerequisites: MAT-112
Credit: 1
MAT-223 Linear Algebra
An introduction to linear equations and vector
spaces. Topics include solving linear equations,
matrix algebra, row operations, determinants,
vector spaces, bases and dimension, linear
transformations, eigenvalues and eigenvectors, and
orthogonality. Optional topics include least
squares problems, matrix factorization, and other
applications. An important aspect of the course is
to introduce the student to abstract thinking and
proofs.
Prerequisites: MAT-112 with a minimum grade of C-, or MAT-223 placement
Credit: 1
Distribution: Quantitative Literacy
Equated Courses: CR
MAT-224 Differential Equations
An introduction to ordinary differential
equations. Special solution techniques and some
theory for first-order and linear equations
including integrating factors, constant
coefficients, undetermined coefficients, variation
of parameters, power series solutions, Laplace
transforms, and systems of differential equations
with applications. This course is offered in the
spring semester.
Prerequisites: MAT-112 with a minimum grade of C-, and MAT-223.
Credit: 1
Equated Courses: CR
MAT-225 Multivariable Calculus
This course builds on the introduction to calculus
in higher dimensions in MAT-112. Topics covered
include limits, continuity, differentiability,
directional derivatives, constrained and
unconstrained optimization, geometry of curves,
multiple integrals, general coordinate systems,
path and surface integrals, vector calculus,
theorems of Green and Stokes, and applications.
This course is offered in the fall semester.
Prerequisites: MAT-112 with a minimum grade of C-, and MAT-223
Credit: 1
Distribution: Quantitative Literacy
MAT-226 Operations Research
Linear and nonlinear optimization, linear
programming, integer programming, duality,
combinatorics, the simplex method and related
algorithms, game theory, Markov chains, queuing
theory.
Prerequisites: MAT-223
Credit: 1
MAT-235 Stochastic Simulation
Interesting real world phenomena often involve
randomness at some level, and this course
develops mathematical and computational tools for
studying these systems. In particular, students
will study and implement computer simulation
models of continuous and discrete stochastic
processes with potential applications in physics,
economics, epidemiology, networks, sports,
elections, and industrial engineering. Specific
topics for study include: basic probability
models, pseudo-random number generation, queueing
models, discrete event simulations, Poisson
processes, random walks, Markov chains, Monte
Carlo methods, and statistical analysis of
simulated data.
Prerequisites: MAT-112 and CSC-111
Credit: 1
MAT-251 Mathematical Finance
This course gives an overview of the mathematical
reasoning behind the pricing of financial
derivatives. Special emphasis is given to
replication arguments and using risk-neutral
distributions in the binomial pricing model and
using risk neutral distributions in the geometric
Brownian motion model. A probabilistic derivation
of the Black-Scholes pricing formula for gap call
options is given. Other topics covered include
put-call parity, delta hedging, value at risk, and
compound options. The course is typically offered
every fall semester.
Prerequisites: MAT-112
Credits: 0.5
MAT-252 Mathematical Interest Theory
This course gives a thorough treatment of the
mathematical theory of interest, with special
attention paid to calculating present and
accumulation values for annuities (series of
payments made at regular time intervals). Some
topics include nominal and effective rates of
interest and discount, force of interest,
amortization schedules, sinking funds, bonds,
duration, and the use of modified duration to
measure bonds' sensitivity to changes in the yield
rate. This course is typically offered every fall
semester.
Prerequisites: MAT-112
Credits: 0.5
MAT-253 Probability Models
This course is a standard calculus-based
introduction to discrete and continuous random
variables. Discrete distributions considered
include the hypergeometric, binomial, geometric,
Poisson, and discrete uniform. Continuous
distributions considered include the gamma,
chi-square, normal, beta, t and F. The Central
Limit Theorem is covered, as well as multivariate
distributions (including the bivariate normal and
multinomial distributions), and transformations of
random variables. Credit cannot be given for both
MAT-103 and MAT-253. This course is typically
offered in the fall semester.
Prerequisites: MAT-112
Credits: 0.5
MAT-254 Statistical Models
This course gives an overview of confidence
intervals and classical hypothesis testing
procedures: z-tests, t-tests, F-tests, Chi-square
tests, and regression. An intuitive but
mathematical treatment is given for all the
distributions and procedures involved. Credit
cannot be given for both MAT-104 and MAT-254. This
course is typically offered in the spring
semester.
Prerequisites: MAT-112
Credits: 0.5
MAT-277 Special Topics
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credit: 1
MAT-287 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
MAT-314 Modeling With Differential Equations
A course to develop the basic skills of
formulation, simplification, and analysis of
mathematical models for describing and predicting
phenomena in the natural and social sciences,
with special emphasis in modeling with
differential equations. Topics may be taken from
fields such as physics, chemistry, biology,
psychology, economics, and political science.
This course is offered in the fall semester of
even-numbred years.
Prerequisites: MAT-224
Credit: 1
MAT-323 Topics in Linear Algebra
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: MAT-223
Credit: 1
MAT-324 Topics in Differential Equations
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: MAT-224
Credit: 1
MAT-331 Abstract Algebra I
This course is a first course in algebraic
structures and higher abstract mathematics. The
algebraic structures studied are groups and rings,
which generalize symmetry and familiar number
systems like the integers or real numbers. Topics
include modular arithmetic, subgroups, quotient
groups, isomorphism theorems, and permutation
groups. This course has a strong emphasis placed
on writing and reading mathematical proofs. This
course is offered in the spring semester.
Prerequisites: MAT-223 with a minimum grade of C-
Credit: 1
MAT-332 Abstract Algebra II
This course is a continuation of MAT-331. Topics
will depend on the instructor but may include
fields, modules, Galois theory, algebraic
geometry, Gröbner bases, or advanced topics in
groups and rings. This course has a strong
emphasis placed on writing and reading
mathematical proofs.
Prerequisites: MAT-331
Credit: 1
MAT-333 Funct Real Variable I
A first course in the foundations of modern
analysis. Topics include set theory, topology of
the real numbers, sequences, series,
differentiation, integration, and rigorous proofs
of the major theorems of single-variable
calculus. This course is offered in the fall
semester.
Prerequisites: MAT-223
Credit: 1
MAT-334 Funct Real Variable II
A continuation of MAT 333. Topics will depend on
the instructor but may include sequences and
series of functions, Fourier analysis, elementary
functional analysis, advanced multivariable
calculus or metric spaces.
Prerequisites: MAT-333
Credit: 1
MAT-337 Numerical Analysis
This course provides a broad introduction to the
field of numerical analysis. Topics of study
include rootfinding, numerical linear algebra,
function approximation, numerical differentiation
and integration, and numerical methods for
differential equations. The primary focus
involves the derivation, analysis and
implementation of numerical methods, but the
course also includes discussion of uses and
implications of these methods in applications.
This course is offered in the fall semester of
even-numbered years.
Prerequisites: CSC-111 and MAT-223
Credit: 1
MAT-338 Topics Computational Math
This course develops mathematical and
computational techniques in areas of mathematics
or interdisciplinary study in which computation
plays a central and essential role. Topics vary by
semester but they may include computational
geometry, computer algebra, scientific computing,
and symbolic computation. This course is offered
in the fall. Topics vary with each scheduled
offering. Refer to Student Planning's section
information for descriptions of individual
offerings, and applicability to distribution
requirements.
Prerequisites: CSC-111 and MAT-112
Credit: 1
MAT-341 Topology
An introduction to point-set topology. Topics
include topological spaces, continuous functions,
product and quotient spaces, metric spaces,
connectedness, and compactness.
Prerequisites: MAT-223
Credit: 1
MAT-344 Complex Analysis
This course develops the core analytical framework
for complex functions of one variable. Topics
include basic operations and properties of the
complex plane, transformations of elementary
functions, analytic functions, contour integrals,
theory of residues, and conformal mapping. This
course is offered in the spring semester of
odd-numbered years.
Prerequisites: MAT-223
Credit: 1
MAT-353 Probability Models II
This course is a continuation of MAT-253
(Probability Models) with a focus on applications
to financial problems. Brownian motion and Ito
integrals are introduced and used for ruin theory
calculations and applied to some simple investment
models with continuous trading. The compound
Poisson, mixed, and mixture distributions are used
for some insurance settings. Expected present
value and variance of present value are calculated
for a wide variety of life insurance and annuity
problems. The course is typically offered in the
fall semester.
Prerequisites: MAT-253
Credits: 0.5
MAT-354 Mathematical Statistics
This course takes a more theoretical look at
estimation and hypothesis testing than MAT-254
(Statistical Models). Classical estimation topics
include method of moment estimators, maximum
likelihood estimators (MLE's), the information
inequality, and the asymptotic theory of MLE's.
Classical hypothesis testing topics include using
the Neyman-Pearson Lemma to find most powerful
tests and uniformly most powerful tests,
Likelihood ratio tests (LRT's), and the asymptotic
theory of LRT's. The course also looks at the
Bayesian approach to statistical inference, in
particular, the situation with binomial data and
beta priors. This course is typically offered in
the spring semester, loosely alternating with
MAT-355 Regression Models.
Prerequisites: MAT-253 and MAT-254
Credits: 0.5
MAT-355 Regression Models
This course takes a mathematical, matrix-based
look at regression (introduced in MAT-254,
Statistical Models). The probabilistic machinery
needed when working with linear combinations of
normal random variables is developed, including
orthant probability calculation and several
results involving the chi-square distribution. A
general method for hypothesis testing is presented
and used in a variety of testing situations. Time
series models are also looked at and maximum
likelihood estimation in both regression and time
series settings is considered. This course is
typically offered in the spring semester, loosely
alternating with MAT-354 Mathematical Statistics.
Prerequisites: MAT-223, MAT-253, and MAT-254
Credits: 0.5
MAT-377 Special Topics
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credits: 0.5-1
MAT-378 Special Topics
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credits: 0.5-1
MAT-387 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
MAT-388 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
MAT-400 Senior Seminar
Topics in the history and foundations of
mathematics, the special emphasis varying from
year to year. Every student will be expected to
write a term paper. Please refer to the
Registrar's page for course description.
Prerequisites: none
Credits: 0.5
Computer Science (CSC)
CSC-101 Intro to Computer Science
An introduction to the field of computer science:
the study of algorithmic processes and the
machines that implement them. Students will study
the history of computing as well as ethical issues
raised by computing and automation. Students will
study fundamental areas of the discipline,
including basic digital circuits, computer
hardware and architecture, data representation,
issues of computability, and algorithm design and
analysis. Students will also engage in hands-on
activities involving basic digital circuits,
hardware and programming.
Prerequisites: none
Credit: 1
Distribution: Quantitative Literacy
Equated Courses: APCR
CSC-106 Topics in Introduct Comp Sci
A reflective examination of basic ideas in
contemporary computer science. Through
participation and discovery, students will
consider an articulation of computer science that
focuses on procedural units, algorithms, and
abstractions. Topics will vary, but could include
programming in various contexts, history of
computing, etc. This course does not count toward
the major or minor in computer science. This
course will suffice as a pre-requisite for
CSC-111. Topics vary with each scheduled offering.
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credits: 0.5-1
Distribution: Quantitative Literacy
CSC-111 Intro to Programming
This course provides an introduction to
programming and problem solving in a higher-level,
general-purpose language. Programming topics
include primitive data types, simple data types
such as arrays, program constructs such as
conditionals, loops, and functions, and the
fundamentals of object-oriented programming.
(Note: CSC-111 does not count as a laboratory
science.)
Prerequisites: CSC-101, CSC-106, or MAT-112; or permission of the
instructor.
Credit: 1
Distribution: Quantitative Literacy
CSC-171 Special Topics in Comp. Sci.
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credit: 1
Distribution: Quantitative Literacy
CSC-187 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-188 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-211 Intro to Data Structures
This course studies structured ways of storing and
organizing data and algorithms designed for these
structures. Attention is given to both theory and
practical implementation of data structures and
algorithms. Analytical techniques will be
developed to study algorithm complexity,
comparisons between iterative and recursive
algorithms, and theory for searching, sorting, and
traversing data. Computational studies will
provide practical validation of analytical results
and will develop an intuition for understanding
tradeoffs between competing methods. Data
structures covered include lists, stacks, queues,
trees, hash tables, graphs, and related data
types.
Prerequisites: CSC-111 with a minimum grade of C-
Credit: 1
CSC-235 Stochastic Simulation
Interesting real world phenomena often involve
randomness at some level, and this course
develops mathematical and computational tools for
studying these systems. In particular, students
will study and implement computer simulation
models of continuous and discrete stochastic
processes with potential applications in physics,
economics, epidemiology, networks, sports,
elections, and industrial engineering. Specific
topics for study include: basic probability
models, pseudo-random number generation, queueing
models, discrete event simulations, Poisson
processes, random walks, Markov chains, Monte
Carlo methods, and statistical analysis of
simulated data.
Prerequisites: Prereq of MAT-112 and CSC-111
Credit: 1
CSC-241 Introduction to Machine Organization
This course studies the various levels at which a
computer can be studied, both in hardware and
software. These levels include transistor level
digital circuits, higher-level architectural
circuits, and the hierarchy of machine code,
assembly code, and high-level programming
languages. Students will comparatively study
different modern and historical computer
architectures, including examples of both RISC and
CISC architectures. Students will become
proficient in programming in a modern assembly
language (e.g. ARM64 or x86-64). This course is
offered in the fall semester.
Prerequisites: CSC-111 with a minimum grade of C-
Credit: 1
Distribution: Quantitative Literacy
Equated Courses: CSC-311
CSC-242 Theory of Programming Languages
A study of the paradigms of programming languages,
including procedural languages such as Pascal or
'C', object-oriented languages such as C++ or
Smalltalk, functional languages such as ML or
Scheme, logic-oriented languages such as Prolog,
and concurrency such as in Ada. Consideration of
how concepts are implemented, such as modules,
parameter passing, function evaluation, data types
and type checking, memory management, exception
handling, and threads. This course is offered in
the spring semester.
Prerequisites: CSC-111
Credit: 1
Equated Courses: CSC-321
CSC-243 Algorithm Design and Analysis
This course studies how algorithms are designed,
analyzed, implemented and proven to work
correctly. Common algorithmic design paradigms
will be examined -- divide and conquer, dynamic
programming, greedy, as well as the strategy of
reducing from one type of problem to another.
Standard techniques for studying algorithmic
efficiency will be utilized throughout the course,
including asymptotic analysis and recurrence
relations. Additional specialized topics may be
surveyed such as graph algorithms, linear
programming, parallel algorithms, approximation
algorithms, randomized algorithms, computational
geometry and lower bound analysis. This course is
offered in the spring semester.
Prerequisites: MAT-111 or equivalent, CSC-211
Corequisites: Either MAT-108 (previously) or MAT-219 (previously or
concurrently)
Credit: 1
Equated Courses: CSC-331
CSC-244 Theory of Computing
How do we know if a problem is computationally
hard to solve? In this course, computational
problems will be studied as formal languages and
classified according to their solvability under
various theoretical computation models and
resource constraints. The models to be studied
will include finite-state automata, pushdown
automata, linear-bounded automata and Turing
Machines. Alternative characterizations of these
models will also be examined, such as those of
grammars, circuits, restricted programming
languages and Kleene-Godel-Herbrand functions.
Complexity classes (e.g., L, P, NP, Co-NP and
PSPACE) will be introduced to study time and space
constraints, along with the notions of complete
problems, efficient reductions and hierarchy
theorems. Along the way, many difficult open
problems that continue to vex theoretical computer
scientists will be explored, such as the infamous
P versus NP problem. This course is offered in the
fall semester.
Prerequisites: CSC-111 with a minimum grade of C-; CSC-243 with a minimum
grade of C-; either MAT-108 or MAT-219 with a minimum grade
of C-.
Credit: 1
Equated Courses: CSC-341
CSC-271 Special Topics in Computer Science
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: CSC-111 or permission of the instructor
Credits: 0.5-1
CSC-287 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-288 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-337 Numerical Analysis
Advanced-This course will address topics such as
numerical solution of non-linear equations in one
variable, interpolation, approximation,
differentiation, integration, difference
equations, differential equations and their
applications, boundary value problems, linear
systems, matrices, and optimization. This course
is offered in the fall semester of even-numbered
years.
Prerequisites: CSC-111 and MAT-223
Credit: 1
CSC-338 Topics in Computational Math
An advanced course to develop mathematical and
computational techniques in areas of mathematics
or interdisciplinary study in which computation
plays a central and essential role. Topics vary
by semester but may include computational
geometry, computer algebra, scientific computing,
and symbolic computation. Refer to the Course
Descriptions document on the Registrar's webpage
for topics and descriptions of current offerings.
This course is typically
offered in the fall semesters of odd-numbered
years.
Prerequisites: CSC-111 and MAT-112
Credit: 1
CSC-361 Database System Design
Database management is a central component of a
modern computing environment. This course
introduces the fundamental concepts of database
design and database languages. Topics include
relational databases, SQL, formal relational query
languages, the E-R model, relational database
design, storage and file structures, indexing and
hashing, query processing, transactions, and data
warehousing and mining.
Prerequisites: Take CSC-211 with a minimum grade of C-
Credit: 1
CSC-362 Operating Systems
At age 21, Linus Torvalds had created his own
operating system. Do you want to follow in his
footsteps? This course explores the design and
implementation of computer operating systems.
Topics may include historical aspects of operating
systems development, systems programming, process
scheduling, synchronization of concurrent
processes, virtual machines, memory management and
virtual memory, I/O and file systems, system
security, OS/architecture interaction, and
distributed operating systems. This course will
involve working on projects with large amounts of
code written in the C programming language.
Prerequisites: CSC-211 with a minimum grade of C-; CSC-241 with a minimum
grade of C-
Credit: 1
CSC-363 Compiler Design
This course explores principles and practices used
for designing and implementing compilers and
interpreters. Students will build a compiler for a
programming language designed for the course. The
major stages of compilation will be studied
in-depth -- lexical analysis, syntax analysis,
semantic analysis, and code generation. Additional
topics such as advanced parsing techniques and
specific compiler-construction tools may be
covered at the instructor's discretion.
Prerequisites: Take CSC-211 with a minimum grade of C-
Credit: 1
CSC-364 Parallel Programming
This course provides an introduction to
high-performance computing through the study of
different ways that a large problem can be divided
into separate tasks which are solved
simultaneously by parallel processing elements.
Topics include the study of different types of
parallel computing, the design and implementation
of parallel algorithms, hardware that supports
parallelism, and analysis of scalability.
Prerequisites: Take CSC-211 with a minimum grade of C-
Credit: 1
CSC-371 Special Topics in Comp. Sci.
Topics vary with each scheduled offering. Refer to
Student Planning's section information for
descriptions of individual offerings, and
applicability to distribution requirements.
Prerequisites: none
Credit: 1
CSC-387 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-388 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-400 Senior Capstone
This course is a senior capstone course, which all
computer science majors should take in their
senior year. This is a project-based course that
develops skills in individual and team software
development, including reading, documenting,
presenting, and critiquing software systems.
Prerequisites: CSC-211 with a minimum grade of C-
Credit: 1
CSC-487 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1
CSC-488 Independent Study
Individual research projects. The manner of study
will be determined by the student in consultation
with the instructor. Students must receive
written approval of their project proposal from a
department Chair before registering for the
course.
Prerequisites: none
Credits: 0.5-1