The online catalog includes the most recent changes to courses and degree requirements that have been approved by the Faculty Senate, including changes that are not yet effective.
Courses
The online catalog includes the most recent changes to courses and degree requirements that have been approved by the Faculty Senate, including changes that are not yet effective. Courses showing two entries of the same number indicate that the course information is changing. The most recently approved version is shown first, followed by the older version, in gray, with its last-effective term preceding the course title. Courses shown in gray with only one entry of the course number are being discontinued. Course offerings by term can be accessed by clicking on the term links when viewing a specific campus catalog.
Data Analytics (DATA)
115 Introduction to Data Analytics 3 Basic concepts, principles, and tools used in data analytics.
204 Introduction to Text Analysis 3 Introduction to computational and statistical text analysis using the open source programming language R; designed for students with no prior experience with programming but who wish to extend their methodological tool kit to include quantitative and computational approaches to the study of text. (Crosslisted course offered as DTC 204, DATA 204.)
209 [COMM] Visualizing Data 3 Introduction to the tools and methods of visually communicating data for diverse audiences and scenarios. (Crosslisted course offered as DTC 209, DATA 209.)
219 Data Structures for Data Analytics 3 Course Prerequisite: CPT S 121, CPT S 131, or CS 121. Programming techniques including data structures, sorting and searching, object-oriented design, and an introduction to algorithmic analysis. Typically offered Fall and Spring.
225 Linear Algebra with Modern Applications 3 Course Prerequisite: MATH 106 or 201 with a C or better, or MATH 140, 171, 202 or higher or concurrent enrollment, or a minimum ALEKS math placement score of 80%. Solving linear systems, matrices, determinants, subspaces, eigenvalues, orthogonality, machine learning, AI, computer graphics, and economic models. (Crosslisted course offered as MATH 225, DATA 225.) Credit not granted for more than one of MATH 225, 220, and 230.
301 Introduction to R 1 Hands-on knowledge and skills for programming, handling different types of data, data cleaning, and visualization; excellent foundation for courses or projects that involve coding in R. Typically offered Fall and Spring. S, F grading.
302 Introduction to Python 1 Hands-on knowledge and skills for working with real data and the Python programming language; an excellent foundation for later coursework in the Data Analytics major. Typically offered Fall and Spring. S, F grading.
303 Introduction to SQL - The Structured Query Language 1 Hands-on knowledge and skills for basic-to-advanced aspects of the SQL system. Typically offered Fall, Spring, and Summer. S, F grading.
319 Model-based and Data-based Methods for Data Analytics 3 Course Prerequisite: DATA 219, CPT S 215, CPT S 223, or CPT S 233; MATH 220 or MATH/DATA 225; STAT 360. Modeling methods for data analysis with high dimensional data, including theoretical and practical concerns. Typically offered Fall and Spring.
324 [M] Data Repository Systems for Data Analytics 3 Course Prerequisite: CPT S 215, CPT S 223, CPT S 233, or DATA 219; DATA 303; MATH 220, or MATH/DATA 225; admitted to the major in Data Analytics; junior standing. Introduction to repository systems and use of data repositories for data wrangling. Typically offered Spring.
324 (Effective through Spring 2024) [M] Data Repository Systems for Data Analytics 3 Course Prerequisite: CPT S 215, CPT S 223, CPT S 233, DATA 219, or DATA 303; MATH 220, or MATH/DATA 225; STAT 360; admitted to the major in Data Analytics; junior standing. Introduction to repository systems and use of data repositories for data wrangling. Typically offered Spring.
360 Probability and Statistics 3 Course Prerequisite: MATH 140, 171, or 202, each with a C or better, or MATH 172 or 182. Probability models, sample spaces, random variables, distributions, moments, comparative experiments, tests, correlation and regression in engineering applications. Credit not granted for both STAT 360 and 370. (Crosslisted course offered as STAT 360, DATA 360). Typically offered Fall, Spring, and Summer. Cooperative: Open to UI degree-seeking students.
390 Special Topics I V 1-4 May be repeated for credit; cumulative maximum 4 credits. Course prerequisite: Admitted to the major in Data Analytics; junior standing. Skills and concepts for analyzing real data using coding software. Typically offered Fall and Spring.
422 Corporate Data Analytics 3 Course Prerequisite: DATA 219; STAT 360; DATA 324 or concurrent enrollment; STAT 435 or concurrent enrollment; admitted to the major in Data Analytics; junior standing. Project-based class that integrates the main aspects of data analytics. Typically offered Fall.
424 [CAPS] [M] Data Analytics Capstone 3 Course Prerequisite: CPT S/CS 315 or DATA 319; STAT 360; STAT 435 or 437, either with concurrent enrollment; CPT S 451/CS 351 or concurrent enrollment, or DATA 324 or concurrent enrollment; admitted to the major in Data Analytics; junior standing. Team-based project that integrates the main aspects of data analytics.
435 [M] Statistical Modeling for Data Analytics 3 (2-2) Course Prerequisite: STAT 360 or STAT 370, either with a C or better. Multiple linear regression with model selection, dealing with multicolinearity, assessing model assumptions, the LASSO, ridge regression, elastic nets, Loess smoothing, logistic regression, Poisson regression, and the application of the bootstrap to regression modeling. (Crosslisted course offered as STAT 435, DATA 435). Typically offered Fall.
435 (Effective through Summer 2024) [M] Statistical Modeling for Data Analytics 3 (2-2) Course Prerequisite: STAT 360. Multiple linear regression with model selection, dealing with multicolinearity, assessing model assumptions, the LASSO, ridge regression, elastic nets, Loess smoothing, logistic regression, Poisson regression, and the application of the bootstrap to regression modeling. (Crosslisted course offered as STAT 435, DATA 435). Typically offered Fall.
437 High Dimensional Data Learning and Visualization 3 Course Prerequisite: STAT 435. Data visualization, metric-based clustering, probabilistic and metric-based classification, algebraic and probabilistic dimension reduction, scalable inferential methods, analysis of non-Euclidean data. (Crosslisted course offered as STAT 437, DATA 437). Typically offered Spring.
490 Special Topics II V 1-4 May be repeated for credit; cumulative maximum 4 credits. Course prerequisite: Admitted to the major in Data Analytics; junior standing. Skills and concepts for analyzing real data using coding software. Typically offered Fall and Spring.
498 Internship V 1-6 May be repeated for credit; cumulative maximum 6 credits. Course Prerequisite: By department permission; admitted to the major in Data Analytics; junior standing. Experiential learning and career development through professional practice. Typically offered Fall, Spring, and Summer. S, F grading.
499 Special Problems V 1-3 May be repeated for credit; cumulative maximum 6 credits. Course Prerequisite: By department permission. Independent study conducted under the jurisdiction of an approving faculty member; may include independent research studies in technical or specialized problems; selection and analysis of specified readings; development of a creative project; or field experiences. Typically offered Fall, Spring, and Summer. S, F grading.
501 Data Science Primer 3 (2-2) Foundational methods, techniques, and knowledge in the field of Data Science, including an introduction to software, coding, and documentation habits. Typically offered Fall.
520 Communication with Data 1 May be repeated for credit; cumulative maximum 3 credits. Aspects of communication in data science are addressed in successive enrollments: verbal communication in a meeting or to an audience; technical writing and the peer review process; and storytelling with data. Typically offered Fall.
521 Responsible Data Science 3 (2-2) The intersection of quantitative analysis with ethical considerations; topics in the context of AI and machine learning. Typically offered Spring.