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. For course descriptions and per-requisite list, see the linked PDF core and track course sheets.
Find the four-year data analytics degree plan here: Data Analytics Core Curriculum 4-year Plan
• Math 171: Calculus I
• Math 172: Calculus II (Recommended, not required)
• DATA/MATH 225 Linear Algebra with Modern Applications or Math 220: Linear Algebra
• DATA 115: Intro to Data Analytics
• DATA 219 Data Structures for DATA Analytics or CPTS 215: Data Analytics Systems & Algorithms
And one of:
• CPTS 121, 122: C++ design and data structures
• CPTS 131, 132: Java design and data structures
• STAT 360: Probability & Statistics
• STAT 419: Multivariate Statistics (Recommended, not required)
• STAT 435: Statistical Modeling for Data Analytics
• STAT 437: High Dimensional Data Learning and Visualization
Computer Science/Data Science:
• CPTS 315: Intro to Data Mining (Recommended, not required)
• CPTS 415: Big Data
• CPTS 451/CS 351: Intro to Database Systems
• PHIL 450: Data Analytics Ethics
• DATA 319: Model-based and Data-based Methods for Data Analytics
• DATA 424: Data Analytics Capstone
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]
- CES 421: Intercultural Communication and Globalization [DIV]
Details and a list of course options are available on the UCORE website.
- 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.