Explore these tabs to learn more about UMA’s 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 101 Introduction to Computer Science
  • CIS 110 Programming Fundamentals
  • CIS 150 Introduction to Data Science
  • ENG 101 College Writing
  • MAT 115 Elementary Statistics
    or MAT 124 Pre-Calculus

Spring

  • CIS 135 Introduction to Information Systems & Applications Development
  • CIS 218 Introduction to SQL
    or CIS 255 Database Design
  • MAT 115 Elementary Statistics
    or MAT 124 Pre-Calculus
  • PSY 100 Introduction to Psychology
  • BUA 100 Introduction to Business

Fall

  • BUA 101 Financial Accounting for Management & Decision Making (3)
  • CIS XXX Programming Language
  • GEO 101 Introduction to Geography
    or SSC 1XX Any 100-level Social Science course
  • MAT 125 Calculus I

Spring

  • BUA 211 Accounting for Management Decisions
  • CIS 218 Introduction to SQL
    or CIS 255 Database Design
  • CIS XXX Programming Language
  • CIS 352 Data Visualization

Summer

  • CIS 355 Introduction to Sensors
  • CIS 449 Introduction to Programming and Data Analysis

Fall

  • BUA 223 Principles of Management
  • BUA XXX Business Elective 1
  • CIS 450 Data Mining
  • Humanities Elective
  • ENG 317W Professional Writing

Spring

  • CIS 354 Algorithms and Data Structures
  • CIS 470 Project Management
  • MAT 261 Applied Linear Algebra
  • Lab Science

Summer

  • CIS 380 Internship
    or CIS 480 Internship
    or BUA 495 Internship

Fall

  • CIS 360 Geographical Information Systems
  • CIS 370 Statistical Quality Control
  • COM 1XX Communications Elective
  • Humanities Elective

Spring

  • BUA 350 Managerial Analytics
  • CIS 350 Database Management
  • CIS 460 Computers and Culture
  • BUA XXX Business Elective 2
  • Fine Art Elective

Sample 4-Year Graduation Plan: Social Sciences Concentration

Fall

  • CIS 101 Introduction to Computer Science
  • CIS 150 Introduction to Data Science
  • ENG 101 College Writing
  • MAT 115 Elementary Statistics
    or MAT 124 Pre-Calculus
  • SOC 101 Introduction to Sociology

Spring

  • CIS 110 Programming Fundamentals
  • CIS 135 Introduction to Information Systems & Applications Development
  • CIS 218 Introduction to SQL
  • COM 1XX Communications Elective
  • MAT 115 Elementary Statistics
    or MAT 124 Pre-Calculus

Fall

  • CIS XXX Programming Language
  • CIS 255 Database Design \
  • GEO 101 Introduction to Geography
    or SSC 1XX Any 100-level Social Science course
  • MAT 125 Calculus I
  • Fine Art Elective

Spring

  • CIS XXX Programming Language
  • SOC 311 Social Theory
  • CIS 352 Data Visualization
  • MAT 261 Applied Linear Algebra
  • SSC 220 Basic Research Methods

Summer

  • CIS 449 Introduction to Programming and Data Analysis

Fall

  • CIS 360 Geographical Information Systems
  • CIS 450 Data Mining
  • SSC 320 Research methods in Social Science
  • Humanities Elective
  • ENG 317W Professional Writing

Spring

  • CIS 350 Database Management
  • CIS 354 Algorithms and Data Structures
  • CIS 461 Spatio-Temporal Information Science
  • SSC 360 Qualitative Research Methods
  • Concentration Elective

Summer

  • CIS 355 Introduction to Sensors

Fall

  • Concentration Elective
  • Lab Science
  • Humanities Elective

Spring

  • CIS 460 Computers and Culture
  • CIS 470 Project Management
  • SSC 420 Social Science Senior Project
  • Concentration Elective

Courses are subject to change. View the official UMA Catalog here.

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.