Master of Science 4+1 — Online

Add a M.S. in Data Science to your bachelor’s degree in just one year

Get the best of both worlds: finish your undergraduate degree on-campus in four years, then add an M.S. in data science-online in as little as one additional year. 

Stand out in the career search with an advanced degree in data science—one of the hottest career fields in the world. If you are an undergraduate student with a major, minor, or specialization in data science, you can earn a master’s degree in just one additional year. 

The 4+1 program saves you time and money. You’ll take up to nine fewer credit hours than if you earned a B.S. and M.S. separately. And you’ll be enrolled in some graduate courses while paying the undergraduate rate.

How it works

The program is designed so that highly motivated students can earn a master’s degree in just one additional year online. To do that, you’ll take at least one graduate-level course during your senior year, when you’re still classified as an undergraduate. You need to request permission from your advisor. 

You’ll be classified as an undergraduate through the last semester you are enrolled in undergrad requirements. You have to complete at least 21 hours of coursework while classified as a grad student.

Undergraduate scholarships and funding

Transitioning to graduate student status may affect any undergrad scholarships or other funding you have. Make sure to check on this and plan for it.

When you’ll get your degrees

You’ll receive your B.S. and M.S. simultaneously after you’ve completed both degrees.

Program overview

Academic requirements

Students in this program are required to complete six credit hours of core coursework that covers three credit hours of Statistical Methods, three credit hours of Machine Learning and Artificial Intelligence, and three credit hours of capstone project. The 12 remaining credit hours include six credit hours of a data science domain and six credit hours of electives to best suite your individual interests, needs, and overall career goals.

Statistical Methods (3 credit hours)

Select one course from the following:

  • SPEA-V 506 Statistical Analysis for Effective Decision-Making
  • STAT-S 520 Introduction to Statistics
    • Higher level statistics course may be taken with departmental approval

Machine Learning and Artificial Intelligence (3 credit hours)

Select one course from the following: 

  • CSCI-B 551 Elements of Artificial Intelligence
  • CSCI-P 556 Applied Machine Learning
  • ENGR-E 511 Machine Learning for Signal Processing
  • ENGR-E 533 Deep Learning Systems (may be counted only once)

Data Science Domain (6 credit hours)

Students must select one of the following domains and complete two courses in that domain.

  • DSCI-D 532 Applied Database Technologies
    • DSCI-D 590 Topics in Data Science
    • Topic: Optimization and Simulation for Business Analytics
    • Topic: Data Visualization
  • ENGR-E 534 Big Data Applications
  • ENGR-E 583 Information Visualization
  • ILS-Z 534 Search
  • INFO-I 535 Management, Access, and Use of Big and Complex Data
  • INFO-I 606 Network Science

  • ENGR-E 516 Engineering Cloud Computing
  • ENGR-E 517 High Performance Computing
  • ENGR-E 533 Deep Learning Systems (may be counted only once)

  • INFO-I 520 Security in Networked Systems
  • INFO-I 525 Organizational Informatics and Economics of Security
  • INFO-I 533 Systems and Protocol Security and Information Assurance

Capstone Project (3 credit hours)

Students will be required to work on a project that applies the knowledge and skills learned to solve real-world problems for a company, organization, or individual. This may be fulfilled through a capstone course, an internship, or an independent study project. The aim of this requirement is to demonstrate students' capabilities to prospective employers and inspire innovation. 

  • DSCI-D 590 Topics in Data Science
    • Topic: Faculty Assistance in Data Science
  • DSCI-D 592 Data Science in Practice
  • DSCI-D 699 Independent Study in Data Science

Electives (6 credit hours)

The remaining credit hours are selected from additional data science-related course offerings within the Luddy School of Informatics, Computing, and Engineering. Students may not earn credit for courses taken to fulfill the core or capstone requirements. 

  • CSCI-B 505 Applied Algorithms
  • CSCI-B 561 Advanced Database Concepts
  • CSCI-B 657 Computer Vision
  • DSCI-D 590 Topics in Data Science
    • Topic: Applied Data Science
    • Topic: Data Science On-Ramp *
    • Topic: Introduction to NLP for Data Science
    • Topic: Introduction to Python Programming
    • Topic: Time Series Analysis
  • DSCI-D 591 Graduate Internship **
  • INFO-I 529 Machine Learning in Bioinformatics
  • ILS-Z 639 Social Media Mining
  • SPCN-P 507 Data Analysis and Modeling for Public Affairs
  • STAT-S 580 Introduction to Regression Models and Nonparametrics 

** No more than three (3) credit hours of DSCI-D 590, Data Science On-Ramp, may be earned 

* No more than three (3) credit hours of DSCI-D 591 may be earned

Sample degree map

This sample degree map shows a path a typical student can take after completing the 120 credits required for an undergraduate degree.

Sample degree map
SummerElective3 Credits

2 Core
1 Domain
1 Elective

9 Credits

1 Core
1 Domain
1 Elective

9 Credits

Ready to get started?

  • Talk to your advisor as early as possible to make sure you’re on the right path.
  • You can apply to the program after you complete 12 credit hours towards a data science major, minor, or specialization.
  • Maintain a minimum grade point average of 3.0 in your major and program.