Program Overview

Professional Master of Science in Data Science

The Professional Master of Science in Data Science is a program designed to provide students with a comprehensive education in data science. The program consists of 30 credit hours and can be completed on campus or online through Coursera.

Program Details

• The program is offered on campus and online through Coursera.
• The on-campus program consists of 30 credit hours and can be completed in two semesters.
• The online program is offered through Coursera and consists of 30 credit hours, with tuition assessed on a pay-as-you-go, per-course basis.
• The program includes courses in data science, machine learning, and statistics, as well as electives in areas such as data visualization and natural language processing.

Tuition and Fees

• On-Campus Tuition:
  • In-state tuition: $1,661 per credit hour
  • Out-of-state/international tuition: $2,019 per credit hour
  • Student fees: $149-$445 per semester
• Online Tuition (Coursera):
  • Tuition: $525 per credit hour
  • No student fees
• Additional Fees:
  • Books and supplies: $75 per credit hour
  • New student fee: $62 (domestic), $145 (international)
  • Immigration compliance fee: $40 per semester (international students)

Program Structure

• The program consists of 30 credit hours, with a combination of required and elective courses.
• Students can choose from a variety of electives to tailor their program to their interests and career goals.
• The program is designed to be completed in two semesters, but students can take courses at their own pace.

Admission Criteria

• Admission to the program is competitive and based on a variety of factors, including academic background, work experience, and letters of recommendation.
• Applicants must have a bachelor's degree from an accredited institution and a strong academic record.
• Additional requirements may include GRE scores, letters of recommendation, and a personal statement.

Research Areas

• The program offers research opportunities in a variety of areas, including data science, machine learning, and statistics.
• Students can work with faculty members on research projects and present their findings at conferences and seminars.
• The program also offers opportunities for internships and professional development.