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• MS Program Outcomes
• BS Program Educational Objectives
• BS Student Learning Outcomes
• Annual Student Enrollment and Graduation Data

MS, Data Science

Program Outcomes

Upon completion of this program, students will be able to:

1. DS Knowledge: Create data science enabled solutions using critical thinking to integrate domain knowledge with best practices and state-of-the-art methods from statistics and computer science. Design and develop useful models and relevant code-based analyses applicable to needs in government, industry, academia, and non-profits.
2. DS Lifecycle: Execute a repeatable data science lifecycle to engage stakeholders, collect, clean, and organize large amounts of data; develop, analyze, and test models; deploy solutions, and effectively communicate results and recommendations.
3. DS Solutions: Demonstrate curiosity, creativity, and competency through a portfolio of individual and group projects, based on large-scale real-world data, employing methods and techniques from across DS specialties.
   These include Graphical Analysis, Analysis of Variance, Regression Modeling, Text Analysis, Web-App/Dashboard Development, Natural Language Processing, Machine Learning, and specialized methods in a chosen domain.
4. Collaboration: Apply state-of-the-art human and technical collaboration methods and tools, as a team leader or team member, to engage and empower diverse teams to deliver high-quality project solutions while operating with constrained resources.
5. Responsible DS: Evaluate problems for potential ethical issues across the DS lifecycle. Apply best practice methods and algorithms to mitigate ethical risk. Ensure analysis and solutions are transparent, reproducible, and developed in accordance with professional codes of conduct.
6. Community Engagement: Participate in the DS professional community as a knowledgeable practitioner aware of and engaged in diverse forums as a consumer and contributor.

BS, Data Science

Program Educational Objectives

The purpose of the AU Bachelor Degree in Data Science program is to develop undergraduate data scientists able to design and implement creative solutions to real-world problems by integrating domain knowledge with state-of-the-art methods and tools from statistics and computer science.

Our program educational objectives define how our purpose translates into outcomes for our graduates after a few years.

1. Within three years, graduates will be working in a position that uses their data science knowledge, skills and abilities while also offering opportunities to expand and strengthen their data science capabilities.
2. Within three years, graduates who desire additional education, are accepted into masters or PhD programs that leverage their data science knowledge skills and abilities for additional academic enrichment.
3. Within five years, graduates are leading data science projects or teams to produce effective, ethical solutions that effect positive change within their domain.
4. Within five years, graduates are participating in one or more professional communities where they can maintain their awareness of new capabilities, expand their networks of colleagues, and contribute new ideas and service on behalf of their chosen profession.

Student Learning Outcomes

Student Learning Outcomes describe what students are expected to know and be able to do by the time of graduation. These relate to the knowledge, skills, and behaviors students acquire as they progress through the program.

1. DS Knowledge: Create data science enabled solutions at the undergraduate level using critical thinking to integrate domain knowledge with best practices and state-of-the-art methods from statistics and computer science. Design and develop useful models and relevant code-based analyses applicable to needs in government, industry, academia, and non-profits. 
2. DS Lifecycle: Execute a repeatable data science lifecycle to engage stakeholders, collect, clean, and organize large amounts of data; develop, analyze, and test models; deploy solutions, and effectively communicate results and recommendations. 
3. DS Solutions: Demonstrate curiosity, creativity, and undergraduate-level competency through a portfolio of individual and group projects, based on large-scale real-world data, employing methods and techniques from across DS specialties. These include Graphical Analysis, Analysis of Variance, Regression Modeling, Text Analysis, Web-App/Dashboard Development, Natural Language Processing, Machine Learning, and specialized methods in a chosen domain. 
4. Collaboration: Apply state-of-the-art human and technical collaboration methods and tools, as a team leader or team member, to engage and empower diverse teams to deliver high-quality project solutions while operating with constrained resources. 
5. Responsible DS: Evaluate problems for potential ethical issues across the DS lifecycle. Ensure analysis and solutions are transparent, reproducible, and developed in accordance with professional codes of conduct. 
6. Community Engagement: Participate in the DS professional community as a knowledgeable practitioner aware of and engaged in diverse forums as a consumer and contributor.
    

Annual Student Enrollment & Graduation Data, Data Science

Annual Student Graduation Data, Data Science

Annual Student Enrollment and Graduation Data, Data Science

Academic Year	Bachelor’s Degree  w/DS Major	Bachelor’s Degree w/Minor in DS	MS in DS
Totals	21	40	135
2022-23	13	8	47
2021-22	7	11	47
2020-21	1	14	38
2019-20		5	3
2018-19		1
2017-18		1

Enrollments per Academic Year in Data Science

*Declared and intended majors at the start of his academic year.
Academic Year	BS in DS*	BS Minor in DS	MS in DS
2023-24	76	14	76
2022-23	38	15	79