Explore these tabs to learn more about Âé¶¹APP Data Science program.

Sample Curriculum

UMA degrees are flexible. Here is an example of how you could complete your Data Science degree.

Sample 4-Year Graduation Plan: Business Analytics Concentration

Fall
  • CIS 101ÌýIntroduction to Computer Science
  • CIS 110ÌýProgramming Fundamentals
  • CIS 150ÌýIntroduction to Data Science
  • ENG 101ÌýCollege Writing
  • MAT 115ÌýElementary Statistics
    orÌýMAT 124ÌýPre-Calculus
Spring
  • CIS 135ÌýIntroduction to Information Systems & Applications Development
  • CIS 218ÌýIntroduction to SQL
    orÌýCIS 255ÌýDatabase Design
  • MAT 115ÌýElementary Statistics
    orÌýMAT 124ÌýPre-Calculus
  • PSY 100ÌýIntroduction to Psychology
  • BUA 100ÌýIntroduction to Business
Fall
  • BUA 101ÌýFinancial Accounting for Management & Decision Making (3)
  • CIS XXXÌýProgramming Language
  • GEO 101ÌýIntroduction to Geography
    orÌýSSC 1XXÌýAny 100-level Social Science course
  • MAT 125ÌýCalculus I
Spring
  • BUA 211ÌýAccounting for Management Decisions
  • CIS 218ÌýIntroduction to SQL
    orÌýCIS 255ÌýDatabase Design
  • CIS XXXÌýProgramming Language
  • CIS 352ÌýData Visualization
Summer
  • CIS 355ÌýIntroduction to Sensors
  • CIS 449ÌýIntroduction to Programming and Data Analysis
Fall
  • BUA 223ÌýPrinciples of Management
  • BUA XXXÌýBusiness Elective 1
  • CIS 450ÌýData Mining
  • Humanities Elective
  • ENG 317WÌýProfessional Writing
Spring
  • CIS 354ÌýAlgorithms and Data Structures
  • CIS 470ÌýProject Management
  • MAT 261ÌýApplied Linear Algebra
  • Lab Science
Summer
  • CIS 380ÌýInternship
    orÌýCIS 480ÌýInternship
    orÌýBUA 495ÌýInternship
Fall
  • CIS 360ÌýGeographical Information Systems
  • CIS 370ÌýStatistical Quality Control
  • COM 1XXÌýCommunications Elective
  • Humanities Elective
Spring
  • BUA 350ÌýManagerial Analytics
  • CIS 350ÌýDatabase Management
  • CIS 460ÌýComputers and Culture
  • BUA XXXÌýBusiness Elective 2
  • Fine Art Elective

Sample 4-Year Graduation Plan: Social Sciences Concentration

Fall
  • CIS 101ÌýIntroduction to Computer Science
  • CIS 150ÌýIntroduction to Data Science
  • ENG 101ÌýCollege Writing
  • MAT 115ÌýElementary Statistics
    orÌýMAT 124ÌýPre-Calculus
  • SOC 101ÌýIntroduction to Sociology
Spring
  • CIS 110ÌýProgramming Fundamentals
  • CIS 135ÌýIntroduction to Information Systems & Applications Development
  • CIS 218ÌýIntroduction to SQL
  • COM 1XXÌýCommunications Elective
  • MAT 115ÌýElementary Statistics
    orÌýMAT 124ÌýPre-Calculus
Fall
  • CIS XXXÌýProgramming Language
  • CIS 255ÌýDatabase Design
  • GEO 101ÌýIntroduction to Geography
    orÌýSSC 1XXÌýAny 100-level Social Science course
  • MAT 125ÌýCalculus I
  • Fine Art Elective
Spring
  • CIS XXXÌýProgramming Language
  • SOC 311ÌýSocial Theory
  • CIS 352ÌýData Visualization
  • MAT 261ÌýApplied Linear Algebra
  • SSC 220ÌýBasic Research Methods
Summer
  • CIS 449ÌýIntroduction to Programming and Data Analysis
Fall
  • CIS 360ÌýGeographical Information Systems
  • CIS 450ÌýData Mining
  • SSC 320ÌýResearch methods in Social Science
  • Humanities Elective
  • ENG 317WÌýProfessional Writing
Spring
  • CIS 350ÌýDatabase Management
  • CIS 354ÌýAlgorithms and Data Structures
  • CIS 461ÌýSpatio-Temporal Information Science
  • SSC 360ÌýQualitative Research Methods
  • Concentration Elective
Summer
  • CIS 355ÌýIntroduction to Sensors
Fall
  • Concentration Elective
  • Lab Science
  • Humanities Elective
Spring
  • CIS 460ÌýComputers and Culture
  • CIS 470ÌýProject Management
  • SSC 420ÌýSocial Science Senior Project
  • Concentration Elective

Learning Outcomes

Students in the BS Data Science program are required to complete an approved internship in one of three areas: Computer Information Systems, Business or Social Science or an independent experience as appropriate to the concentration.

A data science graduate will be expected to:

  1. Analyze data to identify patterns and trends.
  2. Interpret and communicate data within its interdisciplinary context.
  3. Develop and apply algorithms and processes.
  4. Participate as an active and effective member of various interdisciplinary teams.
  5. Engage scholarly literature to stay current with developments in analytics and data storage.
  6. Understand and consider the ethical challenges associated with data, including privacy and downstream impacts.
  7. Use data sets and variables in a correct and appropriate manner consistent with their limitations, informed not only by data properties, but also their domain of origin.

Upon successful completion of the program, the student will be able to:

  1. develop quantitative and qualitative analysis skills,
  2. demonstrate effective data collection and preparation techniques,
  3. interpret and communicate findings,
  4. apply problem-solving, analytical, critical thinking and decision making skills in the workplace, and
  5. demonstrate knowledge in the areas of data management and social responsibility.