Data Analytics Minor

The Data Analytics Minor, approved for AUT 2023, is interdisciplinary and will allow students to develop computational, statistical and interpretive skills necessary to apply data analysis to problems in their home discipline, while also carefully considering critiques of and the social context for such methods.

Admission Requirements

Prerequisite Courses: Prior to declaring the Minor, students must complete a course or course option in the following categories:

  • Introductory statistics course: BBUS 215, BIS 215, BMATH 215, STMATH 341, STMATH 390, STMATH 392, or equivalent.

Completion Requirements

The Data Analytics Minor requires a total of 5 courses (25 credits); 3 required courses (15 credits) and 2 electives (10 credits). 

Required courses (15 credits): 

CreditsCourse NumberCourse TitlePrerequisites
5B DATA 200 Introduction to Data StudiesNone
5BIS 232Introduction to Data VisualizationNone
5B BUS 301Data ManagementBIS 215 (Understanding Statistics),B BUS 215 (Intro to Business Statistics), B MATH 215 (Statistics for Health Sciences), STMATH 341 (Intro to Statistical Inference), or equivalent.

No more than 10 credits a student counts towards the Minor may count towards the student’s major. Students must earn a minimum of a 2.0 grade in all prerequisite, required, and elective courses for the Minor. Students must complete at least 15 credits of the required or elective courses for the Minor at UW Bothell.

Approved Elective Courses

Below is the current list of approved courses:

               Elective Courses (10 credits)

CreditsNumberCourse TitlePrerequisites
5B BIO 340Computational BiologyB BIO 180
5B BIO 383 /CSS 383BioinformaticsNone
5B BIO 471Plant EcologyB BIO 180
5CSS 112Introduction to Programming for Scientific ApplicationsRecommend: STMATH 124 or BMATH 144 and BPHYS 115 or BPHYS 122.
5CSS 132Computer Programming for Engineers INone
5CSS 133Computer Programming for Engineers IICSS 132
5CSS 142Computer Programming I None
5CSS 143Computer Programming II CSS 142
5BIS 342 Geographic Information SystemsNone
5BIS 343 Geographic VisualizationNone
5BIS 344 Intermediate Geographic Analysis and Applications None
5BIS 352Mapping Communities None
5BIS 411Network Analysis & VisualizationEither BIS 215, BIS 232, B BUS 215, B MATH 215, B DATA 200, or STAT 220
5BIS 412Advanced VisualizationEither BIS 215, BIS 232, B BUS 215, or B MATH 215
5BIS 442 Advanced GIS Analysis and ApplicationsBIS 342 or BIS 343
5BIS 447 Topics in Quantitative InquiryNone
5B BUS 330Information Management and AnalysisNone
5B BECN 382Introduction to EconometricsB BECN 300 and B MATH 144
5B BUS 423Market IntelligenceB BUS 320 
5B BUS 454InvestmentsB BUS 350
5B BUS 455 Financial Risk ManagementB BUS 454
5B BECN 458Risk ModelingB BECN 300
5B BUS 464New Product MarketingB BUS 320

Transfer courses or other courses to substitute for Minor requirements will be evaluated by the  Data Analytics Oversight Committee.  

At least 60% of credits for the minor must not already count towards the student’s major. For many students, the three major requirements, B DATA 200 Intro to Data Studies, BIS 232 Intro to Data Visualization, and B BUS 301 Data Management will satisfy this requirement. If BIS 232 is required for a student’s major, the student must choose at least one elective that is not already being counted towards their major.

The program has the following learning objectives:

1. Use mathematics, statistics, and data analysis to investigate complex, multi-disciplinary cases including those found in societal issues, community partners, politics, business, industry,

healthcare, and scientific research.

2. Communicate data analysis work in a clear and effective manner.

3. Follow data analytics methodology.

4. Appreciate the value of data as a decision-support tool needed in interdisciplinary settings.

5. Connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, including ethical considerations and responding to needs.

6. Work in diverse teams to create or develop data models and visualize data using appropriate technologies.

7. Synthesize and communicate their work in the minor to potential employers and/or graduate schools.

8. Cite examples of data analysis produced by persons from diverse backgrounds including but not limited to women, underrepresented minorities, and persons with disabilities.

9. Understand the data analysis process and the differing roles involved the process

10. Assess influences on data quality such as context, human bias, discrimination, random and systematic errors

11. Choose and implement the appropriate software tool for analyzing a given data set.

Related minors: Data Science Minor, Geographic Information System