Graduate Programs
Degree Programs
Master of Data Science Online and 4+1 Master of Data Science Online
Master of Data Science-Online
The M.S. in Data Science - Online (MSDSO) aims to enhance data skills for managers, practitioners, and domain scientists. Due to its asynchronous format, this program is fully online with no residency requirements. Students have up to - but no more than - five (5) years to complete the degree requirements through part-time or full-time enrollment. Students are required to complete 30 credit hours of graduate-level coursework for this degree.
Prerequisites
Students in this distance education program need to have programming experience in Python and R, as well as basic math (probability, linear algebra, calculus).
Students who lack the above prerequisites are encouraged to reach out to the Luddy Office of Online Education for recommendations.
Curriculum
Students are required to complete 6 credit hours of core coursework that covers 3 credit hours of Statistical Methods, and 3 credit hours of Machine Learning and Artificial Intelligence. Students will specialize in 6 credit hours of a Data Science Domain. The remaining 18 credit hours are 3 credit hours of capstone project and 15 credit hours of electives, selected to best suit individual interests, needs, and overall career goals.
Students may transfer no more than 9 graduate-level credit hours, with grades of B or higher, to the program from another institution or university. These credits may not have previously been utilized to award another degree or certificate; the only exception is those who previously completed the Graduate Certificate in Data Science that is comprised of 12 credit hours, in which up to a total of 21 credit hours may be transferred.
Statistical Methods (3 credit hours)
Select one course from the following:
- SPCN-V 506 Statistical Analysis for Effective Decision-making
- STAT-S 519 519 A Gentle Introduction to Statistics in R
- 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 within that specific domain:
Data Analytics and Visualization
- 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
Intelligent Systems Engineering
- ENGR-E 516 Engineering Cloud Computing
- ENGR-E 517 High Performance Computing
- ENGR-E 533 Deep Learning Systems (may be counted only once)
Cybersecurity
- 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 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 (15 credit hours)
The remaining credit hours are selected from unselected courses listed above or additional data science-related course offerings listed below. Students may not earn credit for courses taken to fulfill core, domain, 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 513 Usable Artificial Intelligence
- 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 3 credit hours of DSCI-D 591 may be earned
(**) No more than three (3) credit hours of DSCI-D 590, Data Science On-Ramp, may be earned
4+1 MS Data Science-Online
Undergraduates with the B.S. Data Science Major, Minor, or Specialization will be able to complete a bachelor’s degree and a M.S. in Data Science – Online. The students would take 120 credits for the bachelor’s degree and 15 credits for the master’s degree. The total credit hours will be 141 credits.
The program’s overall course requirements add up to as much as 9 fewer credit hours than the sum total of the bachelor’s and the master’s degrees taken individually.
Visit the Accelerated Master's & 4+1 page for more details.
Admission and Status
- For admission to the 4+1 program, students must have earned a major and program GPA of at least 3.0 at the time of admission to the program.
- To apply to the 4+1 program, students must first talk with their undergraduate advisor, who will review their academic record to ensure that they meet the admission requirements. If a student meets the requirements, the advisor will notify the Luddy Graduate Office who will provide the student with an invitation code which will give the student access to the application. The Luddy Graduate Office will also provide the student with an application fee waiver code.
- Students in the program will be classified as undergraduates through the last semester in which they are enrolled in undergraduate requirements. Students in good standing, defined as a major and program GPA of at least 3.0, must submit the standard application to the University Graduate School by January 1 prior to the academic year they want to transition to graduate status.
- Those not in good standing at any time are dropped from the program and reclassified as undergraduate students. If the transition to graduate status is delayed for any reason, The 4+1 program status will revert to undergraduate status and the student will be encouraged to apply to the M.S. program.
- Students in the 4+1 program must complete at least 15 hours of coursework while registered in graduate status. Normally, this would encompass no fewer than two semesters.
- Students are advised to check on the effect that transition to graduate status may have on existing undergraduate funding; the possibility of graduate funding is conditional upon transition to graduate status.
- Once admitted to the program, if a student switches out of the 4+1 status, they may not switch back into the program after April 15th. These students will be encouraged to apply for the M.S. program.
- Contact goluddy@iu.edu for admissions questions.
Academic Requirements
- A minimum of 141 credit hours
- Major GPA of at least 3.0; Cumulative GPA for graduate courses of at least 3.0
- All undergraduate degree requirements
- All B.S. Data Science Specialization/Minor requirements
- Students in the 4+1 program are required to complete 6 credit hours of core coursework that covers 3 credit hours of Statistical Methods and 3 credit hours of Machine Learning and Artificial Intelligence. Students will specialize in 6 credit hours of a Data Science Domain. The remaining 9 credit hours are 3 credit hours of capstone project and 6 credit hours of electives, selected to best suit 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 519 A Gentle Introduction to Statistics in R
- 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)
- INFO-I 513 Usable Artificial Intelligence
Data Science Domain (6 credit hours)
Students must select one of the following domains and complete two courses within that specific domain:
Data Analytics and Visualization
- 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 513 Usable Artificial Intelligence (may be counted only once)
- INFO-I 535 Management, Access, and Use of Big and Complex Data
- INFO-I 606 Network Science
Intelligent Systems Engineering
- ENGR-E 516 Engineering Cloud Computing
- ENGR-E 517 High Performance Computing
- ENGR-E 533 Deep Learning Systems (may be counted only once)
Cybersecurity
- 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 591 may be earned
(**) No more than three (3) credit hours total of DSCI-D 590, Data Science On-Ramp, may be earned
You can find more information about current course offerings on the Courses page of our website.
You can find more information about current Luddy course offerings on the Courses page of our website.