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Computer Science Minor

Division of Science & Mathematics - Adm
Division of Science and Mathematics
  • Program Type: Undergraduate minor related to major
  • Requirements for this program are current for Fall 2015
  • Required credits in this minor: 26
Objectives--The computer science curriculum is designed to provide students with a strong foundation in the diverse and rapidly changing field of computing. The science of computing is emphasized with a focus on fundamental principles and the formal underpinnings of the field. Students are encouraged to use and supplement their formal education through a variety of research opportunities, participation in discipline colloquia and student/professional organizations, and pursuit of internship experiences or international studies opportunities.
Program Delivery
This program is available:
  • via classroom (the majority of instruction is face-to-face)
Minor Requirements
No more than two courses with a grade of D or D+, offset by an equivalent number of credits of A or B grades, may be used to meet the requirements for a computer science minor. Non-elective courses taken S-N may not be counted towards the minor. A minimum GPA of 2.00 is required in the minor in order to graduate. The GPA includes all, and only, University of Minnesota coursework. Grades of "F" are included in GPA calculation until they are replaced.
Minor Required Courses
CSCI 1201 - Introduction to Digital Media Computation [M/SR] (4.0 cr)
or CSCI 1301 - Problem Solving and Algorithm Development [M/SR] (4.0 cr)
CSCI 1302 - Foundations of Computer Science [M/SR] (4.0 cr)
CSCI 2101 - Data Structures [M/SR] (5.0 cr)
Take 5 or more credit(s) from the following:
· CSCI 3402 - Computing Systems: Concepts (3.0 cr)
· CSCI 3403 - Computing Systems: Practicum (2.0 cr)
· CSCI 3501 - Algorithms and Computability [M/SR] (5.0 cr)
· CSCI 3601 - Software Design and Development [M/SR] (5.0 cr)
Take 4 or more credit(s) from the following:
· CSci 3xxx
· CSci 4xxx
Minor Elective Courses
Take 4 or more credit(s) from the following:
· CSCI 2701 - Introduction to Data Science [M/SR] (4.0 cr)
· MATH 1021 - Survey of Calculus [M/SR] (4.0 cr)
· MATH 1101 - Calculus I [M/SR] (5.0 cr)
· MATH 1102 - Calculus II [M/SR] (5.0 cr)
· MATH 2101 - Calculus III [M/SR] (4.0 cr)
· MATH 2111 - Linear Algebra [M/SR] (4.0 cr)
· MATH 2202 - Mathematical Perspectives [M/SR] (4.0 cr)
· MATH 2401 - Differential Equations [M/SR] (4.0 cr)
· MATH 2501 - Probability and Stochastic Processes [M/SR] (4.0 cr)
· MATH 3xxx
· MATH 4xxx
· STAT 1xxx
· STAT 2xxx
· STAT 3xxx
· STAT 4xxx
 
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· Division of Science and Mathematics

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CSCI 1201 - Introduction to Digital Media Computation (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Using images, sounds, and movies to introduce problem solving, data representation, data manipulation, and programming principles including recursion. Introduction to basic ideas in hardware, software, and computing.
CSCI 1301 - Problem Solving and Algorithm Development (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Introduction to different problem solving approaches, major programming paradigms, hardware, software, and data representations. Study of the functional programming paradigm, concentrating on recursion and inductively-defined data structures. Simple searching and sorting algorithms.
CSCI 1302 - Foundations of Computer Science (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Basic proof techniques, propositional and predicate logic, induction and invariants, program correctness proofs, simple Big-Oh analysis of algorithms, set theory, introductory graph theory, and basic summations.
CSCI 2101 - Data Structures (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall
Introduction to data structures, including stacks, queues, trees, and graphs; implementation of abstract data types and introduction to software testing, using object-oriented techniques and reusable libraries. (4 hrs lect, 2 hrs lab) prereq: 1201 or 1301 or instr consent
CSCI 3402 - Computing Systems: Concepts
Credits: 3.0 [max 3.0]
Course Equivalencies: 02224 - CSci 3402/CSci 3401
Prerequisites: 1302 and 2101 or #
Typically offered: Every Spring
Overview of computing systems, operating systems, and networks. Sources of complexity. Fundamental abstractions such as memory, processing, and communication; memory management and data storage; threads, processes, race conditions and deadlock; and inter-process and inter-computer communication. Modularity and organization; virtualization; protection and security; performance. prereq: 1302 and 2101 or instr consent
CSCI 3403 - Computing Systems: Practicum
Credits: 2.0 [max 2.0]
Course Equivalencies: 02225 - CSci 3403/CSci 3401
Prerequisites: 1302 and 2101 or #
Typically offered: Every Fall
Lab experience with key computing systems tools and concepts. Command-line tools; shell and system scripting; system programming. Pointers and explicit memory management. Digital logic, gates, electronics, and microcomputers. Network organization and communication; client-server programming. Processes and threads; parallel and distributed computing. Performance and profiling. prereq: 1302 and 2101 or instr consent
CSCI 3501 - Algorithms and Computability (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall
Models of computation (such as Turing machines, deterministic and non-deterministic machines); approaches to the design of algorithms, determining correctness and efficiency of algorithms; complexity classes, NP-completeness, approximation algorithms. (4 hrs lect, 2 hrs lab) prereq: 1302, 2101 or instr consent
CSCI 3601 - Software Design and Development (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Spring
Design and implementation of medium- and large-scale software systems. Principles of organizing and managing such designs and implementations throughout their lifetime. Designing for modularity and software reuse; use of libraries. Dynamics of working in groups. Group work on a substantial software project. prereq: 1302, grade of at least C- in 2101, or instr consent
CSCI 2701 - Introduction to Data Science (M/SR)
Credits: 4.0 [max 4.0]
Course Equivalencies: 02221 - CSci 2701/Stat 2701
Prerequisites: CSci 1201 or CSci 1251 or CSci 1301, Stat 1601 or Stat 2601 or Stat 2611 or #
Typically offered: Every Spring
Same as Stat 2701. Introduction to data science and informatics and their application to real world scenarios. Computational approaches to data types; database creation including technologies such as SQL/no-SQL; data visualization; data reduction, condensation, partitioning; statistical modeling; and communicating results. prereq: CSci 1201 or CSci 1251 or CSci 1301, Stat 1601 or Stat 2601 or Stat 2611 or instr consent
MATH 1021 - Survey of Calculus (M/SR)
Credits: 4.0 [max 4.0]
Prerequisites: 1012 or placement; credit will not be granted for Math 1021 if a grade of C- or higher has previously been received for Math 1101
Typically offered: Every Spring
Short course for students in social sciences, biological sciences, and other areas requiring a minimal amount of calculus. Topics include basic concepts of functions, derivatives and integrals, exponential and logarithmic functions, maxima and minima, partial derivatives; applications. prereq: 1012 or placement; credit will not be granted for Math 1021 if a grade of C- or higher has previously been received for Math 1101
MATH 1101 - Calculus I (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall & Spring
Limits and continuity; the concepts, properties, and some techniques of differentiation, antidifferentiation, and definite integration and their connection by the Fundamental Theorem. Partial differentiation. Some applications. Students learn the basics of a computer algebra system. prereq: 1012, 1013 or placement
MATH 1102 - Calculus II (M/SR)
Credits: 5.0 [max 5.0]
Typically offered: Every Fall & Spring
Techniques of integration. Further applications involving mathematical modeling and solution of simple differential equations. Taylor's Theorem. Limits of sequences. Use and theory of convergence of power series. Students use a computer algebra system. prereq: 1101
MATH 2101 - Calculus III (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Multivariable and vector calculus. Three-dimensional analytic geometry; partial differentiation; multiple integration; gradient, divergence, and curl; line and surface integrals; divergence theorem; Green and Stokes theorems; applications. prereq: 1102 or instr consent
MATH 2111 - Linear Algebra (M/SR)
Credits: 4.0 [max 4.0]
Prerequisites: 1102 or #
Typically offered: Every Fall & Spring
Matrix algebra, systems of linear equations, finite dimensional vector spaces, linear transformations, determinants, inner-product spaces, characteristic values and polynomials, eigenspaces, minimal polynomials, diagonalization of matrices, related topics; applications. prereq: 1102 or instr consent
MATH 2202 - Mathematical Perspectives (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Introduction to the methodology and subject matter of modern mathematics. Logic, sets, functions, relations, cardinality, and induction. Introductory number theory. Roots of complex polynomials. Other selected topics. prereq: 1101
MATH 2401 - Differential Equations (M/SR)
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
First-order and second-order differential equations with methods of solution and applications, Laplace transforms, systems of equations, series solutions, existence and uniqueness theorems, the qualitative theory of differential equations. prereq: 1102 or instr consent
MATH 2501 - Probability and Stochastic Processes (M/SR)
Credits: 4.0 [max 4.0]
Course Equivalencies: 00927 - Math 2501/Stat 2501
Typically offered: Every Fall
Same as Stat 2501. Probability theory; set theory, axiomatic foundations, conditional probability and independence, Bayes' rule, random variables. Transformations and expectations; expected values, moments, and moment generating functions. Common families of distributions; discrete and continuous distributions. Multiple random variables; joint and marginal distributions, conditional distributions and independence, covariance and correlation, multivariate distributions. Properties of random sample and central limit theorem. Markov chains, Poisson processes, birth and death processes, and queuing theory. prereq: 1101 or instr consent