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Data Analytics Concentrations

Computation Concentration

36-42 credits (based on choice of electives and required prerequisites)

COURSECREDITSCOURSE DESCRIPTION/PRE-REQUISITESTYPICALLY OFFERED
Cpt S 322 (M)3[M] Software Engineering Principles I.  Course Prerequisite: CPT S 215, 223 or 233, with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Introduction to software engineering; requirements analysis, definition, specification including formal methods; prototyping; design including object and function oriented design.

CPT S 215 (Discrete Structures): : ____Grade: ____ OR
CPT S 223 or 233 (AdvDataStruct): ____Grade: ____
Fall, Spring
Cpt S 3503Design and Analysis of Algorithms. Course Prerequisite: CPT S 223 or 233, with a C or better; CPT S 317 with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Analysis of data structures and algorithms; computational complexity and design of efficient data-handling procedures.

CPT S 215 (Discrete Structures): Term: ____Grade: ____ OR
CPT S 223 or 233 (AdvDataStruct): Term: ____Grade: ____

MATH 216 (Discrete Structures): Term: ____Grade: ____
CPT S 317 (Automata/Formal Lang): Term: ____Grade: ____
Fall, Spring
Math 3643Principles of Optimization. Course Prerequisite: MATH 202, MATH 220, or MATH 230. Algebra of linear inequalities; duality; graphs, transport networks; linear programming; special algorithms; nonlinear programming; selected applications.Fall, Spring
Math 4203Linear Algebra. Course Prerequisite: MATH 220 with a C or better, or MATH 230 with a C or better; MATH 301 with a C or better. Vector spaces, linear transformations, diagonalizability, normal matrices, inner product spaces, orthogonality, orthogonal projections, least-squares, SVD.

MATH 301 (Intro Math Reasoning): Term: ____Grade: ____
Fall, Spring

Choose one from this list:

Cpt S 4343Neural Network Design and Application. Course Prerequisite: CPT S 121, 131, or E E 221, with a C or better; STAT 360 with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Hands-on experience with neural network modeling of nonlinear phenomena; application to classification, forecasting, identification and control. Credit not granted for both CPT S 434 and CPT S 534. Offered at 400 and 500 level. Fall
Cpt S S4373Introduction to Machine Learning. Course Prerequisite: CPT S 215, 223, or 233, with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Topics in machine learning including linear models for regression and classification, generative models, support vector machines and kernel methods, neural networks and deep learning, decision trees, unsupervised learning, and dimension reduction. Recommended preparation: E E 221; linear algebra; multivariate calculus; probability and statistics.Spring
Cpt S 4403Artificial Intelligence. Course Prerequisite: CPT S 223 or 233, with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Hands-on experience with neural network modeling of nonlinear phenomena; application to classification, forecasting, identification and control. Credit not granted for both CPT S 434 and CPT S 534. Offered at 400 and 500 level.Fall

Choose three electives from this list:

Cpt S 4113Introduction to Parallel Computing. Course Prerequisite: CPT S 215, 223, or 233, with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Fundamental principles of parallel computing, parallel programming experience on multicore machines and cluster computers, and design of algorithms and applications in parallel computing. Recommended preparation: CPT S 350.Fall
Cpt S 4713Computational Genomics. Course Prerequisite: CPT S 223 or 233, with a C or better; CPT S 350 with a C or better; admitted to the major or minor in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or Data Analytics. Fundamental algorithms, techniques and applications. Credit not granted for both CPT S 471 and CPT S 571. Offered at 400 and 500 level.Spring
MATH 4483Numerical Analysis. Course Prerequisite: MATH 315 with a C or better; one of CPT S 121, 131, or MATH 300, with a C or better. Fundamentals of numerical computation; finding zeroes of functions, approximation and interpolation; numerical integration (quadrature); numerical solution of ordinary differential equations. (Crosslisted course offered as MATH 448, MATH 548, CPT S 430, CPT S 530). Required preparation must include differential equations and a programming course. Offered at 400 and 500 level.

MATH 315 (Differential Equations): Term: ____Grade: ____
Fall, Spring
Math 4663Optimization in Networks. Course Prerequisite: MATH 364. Formulation and solution of network optimization problems including shortest path, maximal flow, minimum cost flow, assignment, covering, postman, and salesman. Credit not granted for both MATH 466 and MATH 566. Required preparation must include linear programming. Offered at 400 and 500 level. Cooperative: Open to UI degree-seeking students.

MATH 364 (Principles of Optimization): Term: ____Grade: ____
Fall of Even Years