All students are required to complete a core curriculum consisting of 54 credits in mathematics, computer science, data science, statistics, and philosophy bracketed by introductory and capstone courses designed for the degree.
Upper Division
- DATA 303: Introduction to SQL, The Structured Query Language
- DATA 319: Model-based and Data-based Methods for Data Analytics or CPT_S 315: Introduction to Data Mining
- DATA 324: Data Repository Systems for Data Analytics or CPT_S 451/CS 351: Intro to Database Systems
- DATA/STAT 360: Probability & Statistics
- DATA 422: Corporate Data Analytics or CPT_S 415: Big Data
- DATA 424: Data Analytics Capstone
- DATA/STAT 435: Statistical Modeling for Data Analytics
- DATA/STAT 437: High Dimensional Data Learning and Visualization
- DATA 498: Internship
- PHIL 450: Data Analytics Ethics
Recommended/Elective (not required)
- STAT 419: Multivariate Statistics
- Math 172: Calculus II
UCORE Requirements
All WSU undergraduates must satisfy the University Core Requirements [UCORE] in order to graduate.
Several courses are recommended for development of communication skills vital to career success in the data analytics field, including:
- COM 102: Public Speaking in the Digital Age [COMM]
- COM 210: Multimedia Content Creation [COMM]
- H_D 205: Developing Effective Communication and Life Skills [COMM]
- COM 105: Communication in Global Contexts [HUM]
- DTC 201: Tools and Methods for Digital Technology [ARTS]
- Add in DTC 209: Visualizing Data [COMM]
- CES 421: Intercultural Communication and Globalization [DIV]
Details and a list of course options are available on the UCORE website.
Student Learning Outcomes
At the conclusion of their bachelor of science in data analytics degree, students will be able to:
- Understand the theory and basis of data analytics (including computing, statistics and mathematics) to be able to apply in the practice of data analytics.
- Identify, locate, evaluate, collect, compile and responsibly (ethically, legally, socially, professionally, and securely) use data and associated materials from multiple sources relevant for data analytics.
- Customize and utilize data analytics and data management software packages in order to manage and apply exploratory, descriptive and inferential data analytics techniques to complex data sets.
- Appropriately define data problems, formulate questions, develop and design an analysis plan, and interpret the results of these analyses.
- Effectively communicate data analytics techniques and results in a scientifically and technologically informed way to the academic community, business/industry professionals, and the general public in writing, discussion, digital media, and other communication formats.
- Work with a team of students in consultation with a client to apply a full range of data analytics techniques drawn from computer science, mathematics and statistics to address a real-world application problem.