Graduate

Curriculum

The M.S. in Data Science (MSDS) is a two-year program offering multidisciplinary coursework taught by over 45 faculty with expertise in computer science, information science, informatics, statistics, engineering, and other disciplines. This degree prepares students to pursue a data science-related career or admission to a Ph.D. program. In addition, MSDS students may elect to specialize in the Computational and Analytical Data Science track.


  • Master of Science in Data Science

    IU’s general M.S. in Data Science is ideal for all students who want to work at the intersection of data science approaches, methods, and real world problems. This requires some experience in programming and quantitative methods, but calculus and heavy computer science are not prerequisites. Students are required to complete 30 credit hours of graduate-level coursework for this degree to develop expertise in four areas (15 credit hours):

    1)    Statistics (3 crd hrs)

       Select one:

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

    2)    Machine Learning, Data Mining, Text Mining (6 crd hrs)

       Select two:

    • CSCI-B 505 Applied Algorithms
    • CSCI-B 551 Elements of Artificial Intelligence
    • CSCI-B 555 Machine Learning
    • CSCI-B 565 Data Mining
    • CSCI-B 657 Computer Vision
    • ILS-Z 534 Search
    • INFO-I 526/CSCI-P 556 Applied Machine Learning
    • INFO-I 590 Topic: Applied Data Mining
    • INFO-I 606 Network Science

    3)    Data Engineering and Stewardship (3 crd hrs)

       Select one:

    • ENGR-E 599/516 Engineering Cloud Computing
    • ENGR-E 599/517 High Performance Computing
    • ENGR-E 534/INFO-I 523 Big Data Applications and Analytics
    • INFO-I 524 Big Data Software and Projects
    • INFO-I 535 Management, Access, and Use of Big and Complex Data

    4)    Visualization and Storytelling (3 crd hrs)

       Select one:

    • ENGR-E 583/ILS-Z 637 Information Visualization
    • ENGR-E 584 Scientific Visualization
    • INFO-I 590 Topic: Data Visualization
    • INFO-I 590 Topic: Data and Society

    The remaining 15 credit hours are electives selected from the courses above or additional data science-related course offerings. In consultation with a Data Science faculty advisor, students may choose to pursue an independent study or relevant internship opportunity that blends data science learning with a major project or a custom specialization.

    Sample Electives

    • CSCI-B 561 Advanced Database Concepts
    • ENGR-E 599 Topic: Machine Learning Signal Processing
    • ILS-Z 515 Information Architecture
    • ILS-Z 532 Info Architecture for the Web
    • ILS-Z 639 Social Media Mining
    • INFO-I 536 Foundational Mathematics of Cybersecurity
    • INFO-I 538 Introduction to Cryptography
    • INFO-I 519 Introduction to Bioinformatics
    • INFO-I 520 Security for Networked Systems
    • INFO-I 525 Organizational Informatics & Economics of Security
    • INFO-I 533 Systems & Protocol Security & Info Assurance
    • INFO-I 540 Human Robot
    • INFO-I 542 Foundations of HCI
    • INFO-I 590 Topic: Applied Data Science
    • INFO-I 590 Topic: Data Science for Drug Discovery
    • INFO-I 590 Topic: Data Science Onramp
    • INFO-I 590 Topic: Data Semantics
    • INFO-I 590 Topic: Intro to Business Analytics Modeling
    • INFO-I 590 Topic: Python
    • INFO-I 590 Topic: Real World Data Science
    • INFO-I 590 Topic: SQL and noSQL
    • INFO-I 591 Data Science Graduate Internship
    • INFO-I 601 Introduction to Complex Systems
    • INFO-I 699 Data Science Independent Study
    • SPEA-P 507 Data Analysis and Modeling in Public Affairs
    • SPH-Q 650 Semiparametric Regression with R
    • STAT-S 626 Bayesian Data Analysis
    • STAT-S 631 Applied Linear Models
    • STAT-S 670 Exploratory Data Analysis
  • Master of Science in Data Science – Computational and Analytical Track

    Residential students with a strong STEM background wishing to dive deeper into the mechanics of data science methodologies may wish to pursue a more rigorous curriculum. For those taking the Computational and Analytics (C&A) track, students must complete more technical and theoretical coursework in four areas (15 credit hours):

    1)    Data Systems Foundation (3 crd hrs)

       Required

    • CSCI-B 561 Advanced Database Concepts

    2)    Algorithmic Foundation (3 crd hrs)

       Select one:

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

    3)    Data Analytics Foundation (6 crd hrs)

       Required

    • STAT-S 520 Introduction to Statistics

       Select one:

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

    4)    Big Data Infrastructure (3 crd hrs)

        Select one:

    • INFO-I 535 Management, Access and Use of Big and Complex Data
    • ENGR-E 599/516 Engineering Cloud Computing

    The remaining 15 credit hours are electives and can be selected from courses listed above or a wide range of data science-related course offerings. A course in data ethics or a major project is highly encouraged. Students can be advised on their individual study plans with the assistance of a Computational & Analytical faculty advisor.

    Sample Electives

    • CSCI-B 534 Distributed Systems
    • CSCI-B 551 Elements of AI
    • CSCI-B 554 Probabilistic Approaches to Artificial Intelligence
    • CSCI-B 603 Advanced Algorithms Analysis
    • CSCI-B 609 Foundations in Data Science
    • CSCI-B 649 Advanced Topics in Systems
    • CSCI-B 652 Symbolic Models of Machine Learning
    • CSCI-B 662 Database Systems & Internal Design
    • CSCI-P 538 Computer Networks
    • CSCI-Y 799: Computer Science Colloquium
    • ENGR-E 534/INFO-I 523 Big Data Applications and Analytics
      • ENGR-E 583/ILS-Z 637 Information Visualization
      • ENGR-E 584 Scientific Visualization
      • ENGR-E 599 Topic: Machine Learning Signal Processing
      • ENGR-E 599/517 High Performance Computing
      • ILS-Z 515 Information Architecture
      • ILS-Z 532 Info Architecture for the Web
        • ILS-Z 534 Search
        • INFO-I 519 Introduction to Bioinformatics
        • INFO-I 535 Management, Access and Use of Big and Complex Data
        • INFO-I 590 Topic: Data and Society
          • INFO-I 590 Topic: Data Semantics
          • INFO-I 590 Topic: Data Visualization
          • INFO-I 591 Data Science Graduate Internship
            • INFO-I 601 Introduction to Complex  Systems
            • INFO-I 699 Data Science Independent Study
              • STAT-S 626 Bayesian Data Analysis
              • STAT-S 631 Applied Linear Models
              • STAT-S 670 Exploratory Data Analysis
  • Graduate Certificate in Data Science

    The Graduate Certificate in Data Science (GCDS) is a 100% online program encompassing a broad range of topics such as cloud computing, health and medicine, high-performance computing, data mining, and data analysis. This professional certificate allows students the opportunity to tailor a curriculum to suit their interests.

    Students must complete 12 graduate-level credit hours. Courses must be selected from the approved list of graduate courses listed within the master's curriculum; any four courses may be taken for the certificate. Students are encouraged to consult a faculty advisor for course recommendations, etc.

    Coursework must be completed within two (2) years of entering the certificate program.  No credits may be transferred from another graduate or undergraduate program in order to satisfy the requirements for 12 credit hours of coursework.

  • Ph.D. Minor in Data Science

    Any students pursuing a doctorate at Indiana University Bloomington can deepen their research expertise with a doctoral minor in Data Science. With the guidance of a Data Science Faculty advisor, students may select and complete 12 credit hours of coursework from the approved MS curriculum. All courses must be completed with a grade of “B” or higher to fulfill Ph.D. minor requirements. A minor field written qualifying exam is not required.