Program Overview

Data Science and Analysis BS, Social Science Emphasis

Overview

The Data Science and Analysis BS with a Social Science Emphasis is a comprehensive program designed to equip students with the knowledge and skills necessary to apply data science techniques to social science problems. The program combines core courses in data science with emphasis area courses in social science, providing students with a unique understanding of social phenomena and the ability to analyze and interpret complex data.

General Education Requirements

Students must satisfy the university's general education requirements, which include math proficiency, information literacy, social science, and math and life
atural sciences requirements. The program recommends that students take ENGL 3130 Technical Writing or ENGL 3120 Business Writing to satisfy the Junior-Level Writing requirement.

Satisfactory/Unsatisfactory Option

Courses required for the major may not be taken on a satisfactory/unsatisfactory basis.

Degree Requirements

The BS in Data Science and Analysis consists of a set of core courses along with an emphasis area. Students must earn a minimum grade of C- in all core courses and emphasis area courses.

Core Courses

• Calculus Course: MATH 1800 Analytic Geometry and Calculus I (3-5 credits) or MATH 1100 Basic Calculus (3 credits)
• Statistics Course: Choose one of the following (3 credits)
  • SOC 3220 Quantitative Data Analysis in Social Science Research
  • BIOL 4122 Biostatistics
  • ECON 3100 Economic Data and Statistics
  • CRIMIN 2220 Statistical Analysis in Criminology and Criminal Justice
  • MATH 1320 Introduction to Probability and Statistics
  • PSYCH 2201 Psychological Statistics
  • POL SCI 3000 Political Analysis
  • SCMA 3300 Business Analytics and Statistics
• Additional Required Courses:
  • MATH 4005 Exploratory Data Analysis with R (3 credits)
  • CMP SCI 1250 Introduction to Computing (3 credits)
  • CMP SCI 4200 Python for Scientific Computing and Data Science (3 credits)
  • CMP SCI 4342 Introduction to Data Mining (3 credits) or MATH 4250 Introduction to Statistical Methods in Learning and Modeling
• Total Hours: 18-20

Emphasis Area Requirements

• Choose two of the following courses (6 credits), from at least two subject areas:
  • ANTHRO 1005 Introduction to Human Evolution
  • ANTHRO 1011 Introduction to Cultural Anthropology
  • ANTHRO 1019 Introduction to Archaeology
  • CRIMIN 1100 Introduction to Criminology and Criminal Justice
  • CRIMIN 1110 Theories of Crime
  • POL SCI 1100 Introduction to American Politics
  • POL SCI 1500 Introduction to Comparative Politics
  • POL SCI 1800 Introduction to International Politics
  • PSYCH 1003 General Psychology
  • SOC 1010 Introduction to Sociology
  • SOC 2280 Technology and Society
• Choose one of the following courses (3 credits):
  • CRIMIN 2210 Research Methods in Criminology and Criminal Justice
  • POL SCI 3000 Political Analysis
  • PSYCH 2219 Research Methods in Psychological Science
  • SOC 3230 Social Research Methods
• Choose three of the following courses (9 credits), from two subject areas:
  • ANTHRO 4310 Laboratory Methods in Archaeology
  • COMM 3150 Crisis, Disaster, and Risk Communication
  • COMM 4100 Communication Campaigns
  • POL SCI 3330 Public Opinion and Political Participation
  • POL SCI 4040 Survey Research Practicum in Political Science
  • PSYCH 3318 Industrial and Organizational Psychology
  • PSYCH 4365 Psychological Testing and Assessment
  • SOC 3344 Problems of Urban Community
  • SOC 3501 Social Mapping for Change
  • SOC 4040 Survey Research Practicum for Sociology
• Total Hours: 18

Learning Outcomes

Upon completion of the program, graduates will be able to:

• Apply knowledge of statistical data collection, analysis, and quantitative modeling techniques
• Demonstrate proficiency in industry-standard programming languages that support data acquisition, retrieval, and analysis
• Select, apply, and build data-based models and visualizations to devise solutions to data science problems
• Effectively communicate technical results and recommendations in various formats to appropriate audiences
• Identify and apply appropriate social theories to understand social phenomena
• Critically evaluate explanations of human behavior and social phenomena
• Apply statistical concepts and data science methods to analyze real-world problems in communications, political science, sociology, or psychology