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Master of Data Science and 4+1 Master of Data Science

The M.S. in Data Science (MSDS) is a two-year residential program offering multidisciplinary coursework in computer science, information science, informatics, statistics, engineering, and other disciplines. It prepares students to pursue a data science related career or admission to a Ph.D. program.  As an MSDS student, you have the option of our distinct tracks: Applied Data Science, Big Data Systems, Computational and Analytical, and Managerial Data Science.  Students are required to complete 30 credit hours of graduate-level coursework for this degree.

Applied Data Science Track

The Applied Data Science track offers the training in both the data science methods and their application in different domains.  This track is suitable for students with an interdisciplinary background who want to specialize in application areas of data science.

Student following the Applied Data Science track are required to complete 12 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 3 credit hours of Data Mining and Search, 3 credit hours of Data Management and Engineering, and 3 credit hours of Data Visualization and Storytelling. Students will specialize in 6 credit hours of a Data Science domain. The remaining 12 credit hours are 3 credit hours of capstone project and 9 credit hours of electives, selected to best suit individual interests, needs, and overall career goals.

Statistical Methods (3 credit hours)

• STAT-S 520 Introduction to Statistics
  • Higher level statistics course may be taken with departmental approval

Data Mining and Search (3 credit hours)

Select one course from the following:

• CSCI-B 551 Elements of Artificial Intelligence
• CSCI-B 555 Machine Learning
• CSCI-B 565 Data Mining
• CSCI-P 556 Applied Machine Learning
• ENGR-E 511 Machine Learning for Signal Processing
• ILS-Z 534 Search
• INFO-I 606 Network Science (may be counted only once)

Data Management and Engineering  (3 credit hours)

Select one course from the following:

• CSCI-B 561 Advanced Database Concepts
• DSCI-D 532 Applied Databased Technologies 
• ENGR-E 516 Engineering Cloud Computing
• INFO-I 535 Management, Access, and Use of Big and Complex Data

Data Visualization and Storytelling (3 credit hours)

Select one course from the following:

• ENGR-E 583 Information Visualization (may be counted only once)
• ENGR-E 584 Scientific Visualization
• INFO-I 590 Topics in Informatics
  • Topic: Data Visualization (may be counted only once)
• STAT-S 670 Exploratory Data Analysis

Data Science Domain (6 credit hours)

Select one of the following domains and complete two courses within that specific domain:

    Augmented and Virtual Reality

• INFO-I 590 Topic in Informatics
  • Topic: Artificial Life in Virtual Reality
  • Topic: Building Virtual Worlds
  • Topic: Creating Virtual Assets
  • Topic: Introduction to Virtual Reality

     Data Security and Privacy

• INFO-I 520 Security for Networked Systems
• INFO-I 525 Organizational Informatics and Economics of Security
• INFO-I 533 Systems and Protocol Security and Information Assurance
• INFO-I 538 Introduction to Cryptography

     Economic Data Analytics

• ECON-M 504 Econometrics I
• ECON-M 511 Microeconomic Theory II
• ECON-M 514 Econometrics II
• ECON-M 518 Econometrics: Big Data
• ECON-M 524 Financial Econometrics

     Health and Biomedical Data Science

• INFO-I 507 Introduction to Health Informatics
• INFO-I 519 Introduction to Bioinformatics
• INFO-I 529 Machine Learning in Bioinformatics

     Human Robotic Interaction

• CSCI-B 657 Computer Vision
• ENGR-E 599 Topics in Intelligent Systems Engineering
  • Topic: Autonomous Robotics
• INFO-I 513 Usable Artificial Intelligence
• INFO-I 527 Mobile and Pervasive Design
• INFO-I 540 Human Robot Interaction
• INFO-I 542 Foundations of HCI

     Social Data Science

• ENGR-E 583 Information Visualization (may be counted only once)
• ILS-Z 604 Topics in Library and Information Science
  • Topic: Music Data Mining 
• ILS-Z 639 Social Media Mining
• INFO-I 513 Usable Artificial Intelligence
• INFO-I 590 Topics in Informatics
  
  • Topic: Data Visualization (may be counted only once)
• INFO-I 606 Network Science (may be counted only once)

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 591 Graduate Internship
• DSCI-D 592 Data Science in Practice
• DSCI-D 699 Independent Study in Data Science
• ILS-Z 690 Capstone in Information Architecture

Electives (9 credit hours)

The remaining 9 credit hours are selected from unselected courses above or 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, domain, or capstone requirements.

• No more than three (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

Big Data Systems Track

The Big Data Systems track focuses on the development and engineering of software systems for collecting, managing, and mining massive data.  This is most suitable for students with a background in computer science or engineering who prefer hands-on and project-based learning.

Students following the Big Data Systems track are required to complete 21 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 6 credit hours of AI and Machine Learning, 9 credit hours of Big Data, Cloud Computing, and Visualization, and 3 credit hours of Core Engineering. The remaining 9 credit hours are electives selected to best suit individual interests, needs, and overall career goals.

Pre-requisites: Students in this program need to have a solid foundation in STEM course work, specifically the following:

• Proficient level of programming experience in C, Java or Python
• Familiarity with R and MATLAB is useful
• Calculus I and II and basic understanding or probability and elements of discrete math

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
  • Student who have completed equivalent prior coursework in statistics can opt to take an additional elective in lieu of one of the Statistical Methods courses

AI and Machine Learning for Engineering (6 credit hours)

Select two courses from the following:

• CSCI-B 555 Machine Learning
• CSCI-B 565 Data Mining
• CSCI-P 556 Applied Machine Learning
• ENGR-E 511 Machine Learning for Signal Processing
• ENGR-E 533 Deep Learning Systems
• ENGR-E 536 High Performance Graph Analytics

Big Data, Cloud Computing, and Visualization (9 credit hours)

Select three courses from the following:

• CSCI-B 561 Advanced Database Concepts
• ENGR-E 516 Engineering Cloud Computing
• ENGR-E 522 HPC and Cloud Computing for Large Scale Image Applications
• ENGR-E 534 Big Data Applications
• ENGR-E 583 Information Visualization
• ENGR-E 584 Scientific Visualization

Core Engineering (3 credit hours)

Select one course from the following:

• ENGR-E 503 Introduction to Intelligent Systems
• ENGR-E 517 High Performance Computing
• ENGR-E 535 Image Processing for Medical Applications
• ENGR-E 551 Simulating Nanoscale Systems

Electives (9 credit hours)

The remaining 9 credit hours can be selected from unselected courses above or additional data science-related course offerings within the Luddy School of Informatics, Computing, and Engineering. Students may not earn extra credit for courses taken to fulfill core requirements.

• No more than three (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

 Computational and Analytical Track

The Computational and Analytical track focuses on the foundational data science methods.  This track is most suitable for students with a background in computer science, statistics, or mathematics who wish to dive deeper into the mechanics of data science methodologies.

Students following the Computational and Analytical track are required to complete 15 credit hours of core coursework that covers 3 credit hours of Data Systems Foundation, 3 credit hours of Algorithmic Foundation, 6 credit hours of Data Analytics Foundation, and 3 credit hours of Big Data Infrastructures. The remaining 15 credit hours are electives selected to best suit individual interests, needs, and overall career goals.

Data Systems Foundation (3 credit hours)

• CSCI-B 561 Advanced Database Concepts

Algorithmic Foundation (3 credit hours)

Select one course from the following:

• CSCI-B 503 Algorithms Design and Analysis
• CSCI-B 505 Applied Algorithms

Data Analytics Foundation (6 credit hours)

• STAT-S 520 Introduction to Statistics
  • Higher level statistics course may be taken with departmental approval

Select one additional course from the following:

• CSCI-B 555 Machine Learning
• CSCI-B 565 Data Mining

Big Data Infrastructure (3 credit hours)

Select one course from the following:

• DSCI-D 532 Applied Database Technologies 
• ENGR-E 516 Engineering Cloud Computing
• INFO-I 535 Management, Access and Use of Big and Complex Data

The remaining 15 credit hours are selected from unselected courses above or additional data science-related offerings within the Luddy School of Informatics, Computing, and Engineering. Students may not earn credit for courses taken to fulfill core requirements.

• No more than three (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

Managerial Data Science Track

The managerial data science track combines advanced knowledge in database systems and programming languages with strong interpersonal and project management skills.  This track is most suitable for students with prior work experience who wish to develop organizational and project management skills.

Students following the Managerial Data Science track are required to complete 21 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 3 credit hours of Machine Learning, Data Mining, and Text Mining, 3 credit hours of Data Visualization and Storytelling, 6 credit hours of Management in Theory, and 6 credit hours of Management in Practice.  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 520 Introduction to Statistics
  • Higher level statistics course may be taken with departmental approval

Machine Learning, Data Mining, and Text Mining (3 credit hours)

Select one course from the following:

• CSCI-B 505 Applied Algorithms
• CSCI-B 551 Elements of Artificial Intelligence
• CSCI-B 555 Machine Learning
• CSCI-B 561 Advanced Database Concepts
• CSCI-B 565 Data Mining
• CSCI-B 657 Computer Vision
• CSCI-P 556 Applied Machine Learning
• ENGR-E 511 Machine Learning for Signal Processing
• ILS-Z 534 Search
• INFO-I 513 Usable Artificial Intelligence
• INFO-I 606 Network Science

Data Visualization and Storytelling (3 credit hours)

Select one course from the following:

• ENGR-E 583 Information Visualization
• ENGR-E 584 Scientific Visualization
• INFO-I 590 Topics in Informatics
  • Topic: Data Visualization

Management in Theory (6 credit hours)

Select two courses from the following:

• ILS-Z 513 Organizational Informatics
• ILS-Z 604 Topics in Library and Information Science
  • Topic: Social and Ethical Impacts of Big Data 
• ILS-Z 645 Social and Organizational Informatics of Big Data

Management in Practice (6 credit hours)

Select two courses from the following:

• ILS-Z 512 Information Systems Design
• ILS-Z 556 Systems Analysis and Design
• ILS-Z 586 Digital Curation

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 591 Graduate Internship
• DSCI-D 592 Data Science in Practice
• DSCI-D 699 Independent Study in Data Science
• ILS-Z 690 Capstone in Information Architecture

Electives (6 credit hours)

The remaining 6 credit hours are selected from unselected courses above or 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, domain, or capstone requirements.

• No more than three (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

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 21 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.
• Please contact goluddy@iu.edu if you have 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 9 credit hours of core coursework that covers 3 credit hours of Statistical Methods, 3 credit hours of Data Management and Engineering, and 3 credit hours of Machine Learning, Data Mining, and Text Mining. The remaining 12 credit hours are 3 credit hours of capstone project and 9 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 520 Introduction to Statistics
  • Higher level statistics course may be taken with departmental approval

Data Management and Engineering (3 credit hours)

 Select one course from the following:

• CSCI-B 561 Advanced Database Concepts
• DSCI-D 532 Applied Database Technologies
• ENGR-E 516 Engineering Cloud Computing
• INFO-I 535 Management, Access, and Use of Big and Complex Data

Machine Learning, Data Mining, and Text Mining (3 credit hours)

Select one course from the following:        

• CSCI-B 551 Elements of Artificial Intelligence
• CSCI-B 555 Machine Learning
• CSCI-B 565 Data Mining
• CSCI-B 657 Computer Vision
• CSCI-P 556 Applied Machine Learning
• ENGR-E 511 Machine Learning for Signal Processing
• ILS-Z 534 Search
• INFO-I 513 Usable Artificial Intelligence
• INFO-I 606 Network Science

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 591 Graduate Internship
• DSCI-D 592 Data Science in Practice
• DSCI-D 699 Independent Study in Data Science
• ILS-Z 690 Capstone in Information Architecture

Electives (9 credit hours)

The remaining 9 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.

• No more than three (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

 You can find more information about current Luddy course offerings on the Courses page of our website.