The WSU data analytics core curriculum and specialization tracks develop strong technical skills and working knowledge of an application area, combined with strong communication skills and the ability to work in teams.
The bachelor of science in data analytics requires:
- Core courses in mathematics, statistics, computer science, and philosophy
- Completion of a specialization track
- Satisfaction of WSU UCORE general education requirements
- Electives sufficient to complete a minimum of 120 credits overall
- Calculus and linear algebra (10 credits)
- Computer science fundamentals (11 credits)
- Machine learning and data management (9 credits)
- Statistics (15 credits)
- Data analytics introduction, ethics & project-focused capstone experience (9 credits)
In addition to strong data analytics skills, our students will develop domain knowledge that will enhance their ability to translate raw data into appropriate industry applications. Each specialization track provides students with opportunities to expand their communications skills and work in team to achieve goals.
- Actuarial Science
- Agricultural & Environmental Systems
- Data Visualization
- Life Sciences
- Physical Sciences
- Social Sciences
Program Education Objectives:
Our primary goal is to train our students to be responsible data citizens and serve as the conduit between the theory (computing, mathematics, and statistics) and the field (domain that the data arises from), understanding the principles of both. Graduates of the program will have an ability to analyze and communicate various kinds and types of data from start to finish, using techniques that are: theoretically valid, practically feasible and relevant and ethically and legally viable.
Given this over-arching goal we would like to emphasize the following program education objectives (PEO).
Students graduating with a BS in DA from WSU will be able to:
- Understand data and its analysis in theory (using computing, mathematical and statistical principles), in practice (computing methods, software, analysis, coding) throughout the data lifecycle.
- Understand the context of the data, domain it comes from, type of data, questions of interest and apply methods to solve them.
- Recognize professional responsibilities as data analysts: understand ethical and legal responsibilities regarding the data one has access to; understand the concepts of security and privacy of data; have confidence in these principles to articulate misuse and abuse of data.
- Effectively communicate (verbally, written and visual) in a variety of professional contexts, understanding and appreciating their audience.
- Function effectively as a member or leader of a team engaged in activities appropriate to data analytics.