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.
Program in Data Analytics
cas.vancouver.wsu.edu/data-analytics
Science & Engineering (VSCI) 130
360-546-9620
Program Leader and Teaching Professor (Career) L. Rosio Sotomayor
cas.vancouver.wsu.edu/data-analytics
Science & Engineering (VSCI) 130
360-546-9620
Data analytics is the application of powerful new methods—drawn from computer science, mathematics and statistics, and domain sciences—to collect, curate, analyze, discover and communicate knowledge from “big data.”
There has been an explosion of demand for skilled data analysts who can communicate, solve problems, and work effectively in teams. Data analytics tools and techniques are used by many different industries to create, manage, explore, and analyze large, complex datasets in order to evaluate past performance, predict future trends, and make better decisions.
Our students are trained in advanced statistical, data, and computer science skills as well as concentrated domain knowledge. This combination enables WSU graduates to effectively work in teams and easily communicate with colleagues and managers to solve problems. Students are able to specialize in Business Analytics, Actuarial Analytics, or Data Visualization.
Student Learning Outcomes
- Demonstrate competency in data literacy—dynamic, structured, unstructured, numerical, and text data.
- Demonstrate competency with quantitative reasoning to make decisions in a logical, mathematical manner.
- Demonstrate competency with data wrangling and exploratory data analysis.
- Demonstrate and articulate an understanding of domain literacy and nuances of data.
- Utilize an interdisciplinary perspective in order to understand the elements of data analytics.
- Effectively communicate through writing and speech the story of the data from start to finish.
Schedules of Studies
Honors students complete the Honors College requirements which replace the UCORE requirements.
Actuarial Science Option (120 Credits)
| First Year | |
| First Term | Credits |
| CPT S 121 or 131, or CS 1211 | 4 |
| DATA 115 | 3 |
| ENGLISH 101 [WRTG] | 3 |
| MATH 171 [QUAN] | 4 |
| Second Term | Credits |
| ECONS 101 [SSCI] | 3 |
| HISTORY 105 [ROOT] | 3 |
| MATH 172 | 4 |
| Electives | 5 |
| Second Year | |
| First Term | Credits |
| DATA 219 | 3 |
| ECONS 102 | 3 |
| MATH 220 or DATA 225 | 2 or 3 |
| STAT 360 | 3 |
| UCORE Inquiry2 | 4 |
| Second Term | Credits |
| ACCTG 230 | 3 |
| DATA 303 | 1 |
| DATA 319 | 3 |
| FIN 325 | 3 |
| UCORE Inquiry2 | 3 |
| Electives | 2 |
| Complete Writing Portfolio | |
| Third Year | |
| First Term | Credits |
| DATA 3241 | 3 |
| MATH 405 | 3 |
| STAT 435 [M] | 3 |
| UCORE Inquiry2 | 3 |
| Electives or MATH 3003 | 3 |
| Second Term | Credits |
| Communication [COMM] or Written Communication [WRTG] | 3 |
| FIN 350 | 3 |
| STAT 437 | 3 |
| Electives | 6 |
| Fourth Year | |
| First Term | Credits |
| DATA 422 | 3 |
| DATA 498 Internship | 3 |
| STAT 443 | 3 |
| UCORE Inquiry2 | 3 |
| Electives or B LAW 210, or MATH 3003 | 3 |
| Second Term | Credits |
| DATA 424 [CAPS] [M] | 3 |
| PHIL 450 [HUM] | 3 |
| STAT 446 | 3 |
| Electives or B LAW 210, or MATH 3003 | 7 |
Footnotes
| 1 | CS courses offered at Vancouver only. |
| 2 | Must complete 4 of these 5 UCORE designations: ARTS, BSCI, DIVR, EQJS, PSCI. One lab science (BSCI or PSCI) must be completed. |
| 3 | B LAW 210 and MATH 300 are recommended electives. |
Business Option (120 Credits)
| First Year | |
| First Term | Credits |
| CPT S 121 or 131, or CS 1211 | 4 |
| DATA 115 | 3 |
| ENGLISH 101 [WRTG] | 3 |
| MATH 171 [QUAN] | 4 |
| Electives | 1 |
| Second Term | Credits |
| ECONS 101 [SSCI] | 3 |
| HISTORY 105 [ROOT] | 3 |
| MATH 220 or DATA 225 | 2 or 3 |
| Electives | 7 |
| Second Year | |
| First Term | Credits |
| ACCTG 230 | 3 |
| DATA 219 | 3 |
| STAT 360 | 3 |
| UCORE Inquiry2 | 4 |
| Electives | 2 |
| Second Term | Credits |
| Communication [COMM] or Written Communication [WRTG] | 3 |
| DATA 303 | 1 |
| DATA 319 | 3 |
| MIS 250 | 3 |
| UCORE Inquiry2 | 3 |
| Electives | 2 |
| Complete Writing Portfolio | |
| Third Year | |
| First Term | Credits |
| DATA 324 | 3 |
| ECONS 311 | 3 |
| STAT 435 [M] | 3 |
| UCORE Inquiry2 | 3 |
| Electives | 3 |
| Second Term | Credits |
| MIS 372 | 3 |
| STAT 437 | 3 |
| UCORE Inquiry2 | 3 |
| Electives | 6 |
| Fourth Year | |
| First Term | Credits |
| DATA 422 | 3 |
| DATA 498 Internship | 3 |
| Choice Pair Course3 | 3 |
| Electives | 6 |
| Second Term | Credits |
| DATA 424 [CAPS] [M] | 3 |
| PHIL 450 [HUM] | 3 |
| Choice Pair Course3 | 3 |
| Electives | 6 |
Footnotes
| 1 | CS courses offered at Vancouver only. |
| 2 | Must complete 4 of these 5 UCORE designations: ARTS, BSCI, DIVR, EQJS, PSCI. One lab science (BSCI or PSCI) must be completed. |
| 3 | Choice Pair Courses (6 credits): Choose 1 pair from FIN 325 and FIN 421; FIN 325 and FIN 425; FIN 325 and FIN 427; MIS 325 and MIS 420; or MKTG 360 and MKTG 368. |
Data Visualization Option (120 Credits)
| First Year | |
| First Term | Credits |
| CPT S 121 or 131, or CS 1211 | 4 |
| DATA 115 | 3 |
| ENGLISH 101 [WRTG] | 3 |
| MATH 171 [QUAN] | 4 |
| Electives | 1 |
| Second Term | Credits |
| HISTORY 105 [ROOT] | 3 |
| MATH 220 or DATA 225 | 2 or 3 |
| UCORE Inquiry2 | 3 |
| Electives | 7 |
| Second Year | |
| First Term | Credits |
| DATA 219 | 3 |
| DTC 201 [ARTS] | 3 |
| STAT 360 | 3 |
| UCORE Inquiry2 | 4 |
| Electives | 2 |
| Second Term | Credits |
| Communication [COMM] or Written Communication [WRTG] | 3 |
| DATA 303 | 1 |
| DATA 319 | 3 |
| DTC 209 | 3 |
| UCORE Inquiry2 | 3 |
| Electives | 2 |
| Complete Writing Portfolio | |
| Third Year | |
| First Term | Credits |
| DATA 324 | 3 |
| STAT 435 [M] | 3 |
| Option Courses3 | 6 |
| Electives | 3 |
| Second Term | Credits |
| STAT 437 | 3 |
| UCORE Inquiry2 | 3 |
| Option Courses3 | 6 |
| Electives | 3 |
| Fourth Year | |
| First Term | Credits |
| DATA 422 | 3 |
| DATA 498 Internship | 3 |
| Electives | 9 |
| Second Term | Credits |
| DATA 424 [CAPS] [M] | 3 |
| PHIL 450 [HUM] | 3 |
| Electives | 9 |
Footnotes
| 1 | CS courses offered at Vancouver only. |
| 2 | Must complete 4 of these 5 UCORE designations: BSCI, DIVR, EQJS, PSCI, SSCI. One lab science (BSCI or PSCI) must be completed. |
| 3 | Option Courses (12 credits): Choose four from: DTC 335, 336, 354, 355, 435, 477, 478. |
General Option (120 Credits)
| First Year | |
| First Term | Credits |
| CPT S 121 or 131, or CS 1211 | 4 |
| DATA 115 | 3 |
| ENGLISH 101 [WRTG] | 3 |
| MATH 171 [QUAN] | 4 |
| Second Term | Credits |
| HISTORY 105 [ROOT] | 3 |
| MATH 220 or DATA 225 | 2 or 3 |
| UCORE Inquiry2 | 3 |
| Electives | 5 |
| Second Year | |
| First Term | Credits |
| DATA 219 | 3 |
| STAT 360 | 3 |
| UCORE Inquiry2 | 4 |
| Minor Course3 | 3 |
| Electives | 3 |
| Second Term | Credits |
| Communication [COMM] or Written Communication [WRTG] | 3 |
| DATA 303 | 1 |
| DATA 319 | 3 |
| UCORE Inquiry2 | 3 |
| Minor Courses3 | 6 |
| Complete Writing Portfolio | |
| Third Year | |
| First Term | Credits |
| DATA 324 | 3 |
| STAT 435 [M] | 3 |
| UCORE Inquiry2 | 3 |
| Minor Courses or Electives3 | 9 |
| Second Term | Credits |
| STAT 437 | 3 |
| UCORE Inquiry2 | 3 |
| Minor Courses or Electives3 | 6 |
| Fourth Year | |
| First Term | Credits |
| DATA 422 | 3 |
| DATA 498 Internship | 3 |
| Electives | 9 |
| Second Term | Credits |
| DATA 424 [CAPS] [M] | 3 |
| PHIL 450 [HUM] | 3 |
| Electives | 10 |
Footnotes
| 1 | CS courses offered at Vancouver only. |
| 2 | Must complete 5 of these 6 UCORE designations: ARTS, BSCI, DIVR, EQJS, PSCI, SSCI. One lab science (BSCI or PSCI) must be completed. |
| 3 | In consultation with their advisor, students are encouraged to fulfill the requirements for this option by completing a minor, additional major, or dual degree. Post baccalaureate students may use coursework from a prior degree. Courses must include sufficient 300-400-level coursework to meet the University requirement of 40 upper-division credits. |
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 [QUAN] Introduction to Data Analytics 3 Basic concepts, principles, and tools used in data analytics.
115 (Effective through Spring 2025) 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; DATA 115 or concurrent enrollment. Programming techniques including data structures, sorting and searching, object-oriented design, and an introduction to algorithmic analysis.
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. 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. 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. 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.
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.
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.) 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.
422 Corporate Data Analytics 3 Course Prerequisite: DATA 324; STAT 360; 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.
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.)
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.)
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.
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. 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. S, F grading.
501 Data Science Primer 3 Foundational methods, techniques, and knowledge in the field of Data Science, including an introduction to software, coding, and documentation habits.
- Data Analytics
Courses
- Data Analytics
Schedules of Studies
- Actuarial Science Option
- Business Option
- Data Visualization Option
- General Option