Master of Science in Applied Data Science (Psychology)
About This Program
The Master of Science in Applied Data Science with and emphasis on Psychology offers a unique interdisciplinary approach, equipping students with advanced data science skills specifically tailored for psychological research and applications. Students will delve into the quantitative methods necessary to analyze complex human and animal behavioral data, including survey responses, experimental results, physiological measures, and digital interactions. The curriculum emphasizes the ethical considerations inherent in handling sensitive personal data, alongside developing expertise in areas such as predictive modeling of human and animal behavior, psychometric analysis, sentiment analysis, and the application of machine learning to understand cognitive processes and mental health outcomes. This track prepares graduates for roles in academia, market research, user experience (UX) design, public health, and other fields requiring sophisticated data-driven insights into human and animal psychology.
Competencies
- Upon completion, students will be able to apply statistical methodologies to real-world data from diverse applications.
- Upon completion, students will be able to understand computational aspects of big data analytics.
- Students will learn to apply psychological theories to interpret data, design and evaluate experiments, and handle sensitive behavioral information ethically. They will formulate research questions suited for data science methods, choose appropriate analytical techniques for diverse psychological datasets, and identify and mitigate sources of bias. Students will also interpret and communicate behavioral insights effectively to varied audiences and understand the unique structures of psychological data—including longitudinal, categorical, textual, and physiological—to adapt preprocessing and modeling strategies appropriately.
Admissions Criteria
All applicants for the Applied Data Science MS must meet UT Arlington’s graduate admission requirements. The program does not consider GRE scores in evaluating candidates for admission.
To be considered for admission, applicants must demonstrate undergraduate preparation equivalent to a baccalaureate degree in natural, physical, or social sciences, technology, engineering, mathematics, business, or related fields.
To apply for a specific track, applicants must demonstrate sufficient background in that concentration with a relevant bachelor’s degree.
Applicants must submit two letters of recommendation from evaluators who can assess the candidate’s potential for academic success.
Unconditional Admission
Applicants who demonstrate a GPA of 3.0 and strong support from references will be considered for unconditional admission. Applicants with a GPA of 2.7 or higher (but less than 3.0) who demonstrate relevant work experience and/or certification may be offered unconditional admission upon review.
All admitted students must complete the program’s self-paced, non-credit online review, “Math Foundation of Machine Learning,” prior to enrolling in classes.
Curriculum
| Foundations | ||
| ASDS 5301 | STATISTICAL THEORY AND APPLICATIONS | 3 |
| ASDS 5302 | PRINCIPLE OF DATA SCIENCE | 3 |
| ASDS 5303 | STATISTICAL AND SCIENTIFIC COMPUTING I | 3 |
| ASDS 6306 | INTERNSHIP/CAPSTONE RESEARCH PROJECT | 3 |
| Psychology Specialization | ||
| Select one of the following: | 3 | |
| RESEARCH METHODS | ||
| APPLIED RESEARCH DESIGN | ||
| Select three from the following: | 9 | |
| HEALTH PSYCHOLOGY | ||
| DATA SCIENCE IN PSYCHOLOGY | ||
| COGNITIVE PSYCHOLOGY | ||
| PERSONALITY PSYCHOLOGY | ||
| SOCIAL PSYCHOLOGY | ||
| GROUP PROCESSES | ||
| ORGANIZATIONAL BEHAVIOR | ||
| EMPLOYEE SELECTION | ||
| PERFORMANCE MANAGEMENT SYSTEMS | ||
| ADVANCE EMPLOYEE TRAINING AND DEVELOPMENT | ||
| BEHAVIORAL NEUROSCIENCE | ||
| HUMAN PHYSIOLOGY | ||
| DECISION MAKING | ||
| LEADERSHIP IN ORGANIZATIONS | ||
| HUMAN LEARNING AND MEMORY | ||
| CYBERPSYCHOLOGY BASIC CONCEPTS | ||
| CYBERPSYCHOLOGY APPLICATIONS | ||
| ADVANCED STATISTICS I | ||
| MULTIVARIATE DATA ANALYSIS | ||
| SEMINAR IN PSYCHOLOGY | ||
| SOCIAL AND PERSONALITY DEVELOPMENT | ||
| NEUROPHARMACOLOGY | ||
| COMPARATIVE PSYCHOLOGY | ||
| PSYCHOMETRIC THEORY | ||
| Data Science Electives | ||
| Select 6 hours from the following: | 6 | |
| DEEP LEARNING AND ARTIFICIAL NEURAL NETWORKS | ||
| SPECIAL TOPICS | ||
| ADVANCE REGRESSION ANALYSIS | ||
| MACHINE LEARNING WITH APPLICATIONS | ||
| DATA MINING WITH INFORMATION VISUALIZATION | ||
| OPTIMIZATION AND BIG DATA ANALYTICS | ||
| STATISTICAL AND SCIENTIFIC COMPUTING II | ||
| INDEPENDENT STUDY | ||
| Total Hours | 30 | |
Advising Resources
Undergraduate and Graduate Advising
Location:
Life Science Building Room 206A and 206B
Email:
data.advising@uta.edu
Phone:
817-272-1512
Web:
Speak to an advisor in the Division of Data Science or schedule an appointment.