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Curriculum

Our residential and online programs offer multidisciplinary coursework taught by over 45 faculty with expertise in computer science, information science, informatics, statistics, engineering, and other disciplines. Our degrees and certificates prepare students to pursue a data science related career or admission to a Ph.D. program.

Residential Programs

  • Master of Science in Data Science - Applied Data Science Track

    Students interested in developing their skills across a range of roles assumed by a Data Scientist should consider the Applied Data Science track. This track fits the needs of students who are interested in techniques and methods that require some math background, as well as those who are not interested in a mathematical orientation to coursework. In this track, you will learn to:

    • Manage large volumes of data
    • Identify and apply the right set of tools and techniques to glean insight from data
    • Clean and curate data
    • Convince others of the importance of the story that data has to tell when used to address a problem of societal importance

    Students are required to complete 30 credit hours of graduate-level coursework for this degree to develop expertise through core requirements, a domain area, a project course, and electives.

    1) Core Courses (12 crd hrs)

      Statistical Methods (3 crd hrs)

    Required:

    • STAT-S 520 Introduction to Statistics
    Data Mining and Search (3 crd hrs)

       Select one:

    • 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

    Data Management and Engineering (3 crd hrs)

       Select one:

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

    Data Visualization and Storytelling (3 crd hrs)

       Select one:

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

    2) Domain Area ( 6 crd hrs)

    Students should select one of the following domain areas and complete two courses within that specific area:

    Data Security and Privacy

    • INFO-I 520 Security for Networked Systems
    • INFO-I 525 Org Info & Econ Security
    • INFO-I 533 Sys & Proto Security & Info Assurances
    • INFO-I 536 Foundational Mathematics of Cybersecurity
    • INFO-I 538 Introduction to Cryptography

    Health and Biomedical Data Science

    • CSCI-B 609 Topics: Bioinformatics for Precision Medicine
    • INFO-I 507 Introduction to Health Informatics
    • INFO-I 519 Introduction to Bioinformatics
    • INFO-I 529 Machine Learning in Bioinformatics
    • INFO-I 590 Topics: Data Science in Drug Discovery, Health and Translational Medicine
    • INFO-I 590 Topics: Real World Data Science
    • INFO-I 590 Topics: SNP Discovery and Population Genetics

    Human Robot Interaction

    • CSCI-B 657 Computer Vision
    • ENGR-E 523 Internet of Things
    • ENGR-E 599 Autonomous Robotics
    • 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
    • ILS-Z 639 Social Media Mining
    • INFO-I 590 Topics: Data and Society
    • INFO-I 590 Topics: Data Visualization
    • INFO-I 606 Network Science

    3) Project Course (3 crd hrs)

    After completing core and domain requirements, a student will select one project course on a topic related to a domain area: 

    • DSCI-D 590 Data Science in Practice
    • DSCI-D 699 Independent Study in Data Science

    4) Electives (9 crd hrs)

    The remaining credit hours should be selected from courses below. Students may not earn elective credits for courses taken to fulfil core, domain, or capstone requirements.

    • CSCI-B 505 Applied Algorithm
    • 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 609 Topics: Foundations in Data Science
    • CSCI-B 657 Computer Vision
    • CSCI-P 556 Applied Machine Learning
    • DSCI-D 591 Graduate Internship
    • ENGR-E 511 Machine Learning for Signal Processing
    • ENGR-E 516 Engineering Cloud Computing
    • ENGR-E 517 High Performance Computing
    • ENGR-E 523 Internet of Things
    • ENGR-E 533 Deep Learning Systems
    • ENGR-E 534 Big Data Applications
    • ENGR-E 583 Information Visualization
    • ENGR-E 584 Scientific Visualization
    • ENGR-E 599 Autonomous Robotics
    • ILS-Z 511 Database
    • ILS-Z 515 Information Architecture
    • ILS-Z 532 Information Architecture for the Web
    • ILS-Z 534 Search
    • ILS-Z 639 Social Media Mining
    • INFO-I 524 Big Data Software and Projects
    • INFO-I 535 Mgmt, Access, and Use of Big and Complex Data
    • INFO-I 590 Topics: Collective Intelligence
    • INFO-I 590 Topics: Data Semantics
    • INFO-I 590 Topics: Performance Analytics
    • INFO-I 590 Topics: SQL and noSQL
    • INFO-I 590 Topics: Data and Society
    • INFO-I 590 Topics: Data Visualization
    • INFO-I 590 Topics: Real World Data Science
    • INFO-I 606 Network Science
    • SPEA-P 507 Data Analysis and Modeling in Public Affairs
    • SPEA-V 506 Statistical Analysis for Effective Decision-Making
    • SPH-Q 650 Semiparametric Regression with R
    • STAT-S 626 Bayesian Data Analysis
    • STAT-S 631 Applied Linear Models
    • STAT-S 640 Multivariate Data Analysis
    • STAT-S 650 Time Series
    • STAT-S 670 Exploratory Data Analysis

  • Master of Science in Data Science - Computational and Analytical Track

    Students with a strong STEM background wishing to drive deeper into the mechanics of data science methodologies may wish to pursue a more rigorous curriculum. For those pursuing the Computational and Analytics (C&A) track, students must complete a total 30 credit hours of graduate-level coursework. More technical and theoretical expertise are developed 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
    • CSCI-B 609 Foundations in Data Science

    3)    Data Analytics Foundation (6 crd hrs)

       Required

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

       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 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 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
    • DSCI-D 591 Data Science Graduate Internship
    • DSCI-D 699 Data Science Independent Study
    • ENGR-E 511 Machine Learning Signal Processing
    • ENGR-E 517 High Performance Computing
    • ENGR-E 583/ILS-Z 637 Information Visualization
    • ENGR-E 584 Scientific Visualization
    • ILS-Z 515 Information Architecture
    • ILS-Z 532 Info Architecture for the Web
    • ILS-Z 534 Search
    • ILS-Z 636 Data Semantics
    • INFO-I 519 Introduction to Bioinformatics
    • INFO-I 523 Big Data Applications and Analytics
    • INFO-I 590 Topic: Data and Society
    • INFO-I 590 Topic: Data Visualization
    • INFO-I 601 Introduction to Complex  Systems
    • STAT-S 626 Bayesian Data Analysis
    • STAT-S 631 Applied Linear Models
    • STAT-S 670 Exploratory Data Analysis

Online Programs

  • Master of Science in 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, students have up to 5 years to complete the degree requirements as a part-time or full-time student

    Students are required to complete 30 credit hours of graduate-level coursework for this degree. Coursework requirements include two core courses (6 credit hours), two courses to form a specialization (6 credit hours), and a capstone project (3 credit hours). The remaining 15 credit hours are counted as electives, selected to best suit individual interests, needs, and overall career goals.

    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 and Artificial Intelligence (3 crd hrs)

       Select one:

    • CSCI-B 551 Elements of Artificial Intelligence
    • ENGR- E 511 Machine Learning for Signal Processing
    • INFO-I 526 Applied Machine Learning

    3)    Data Science Application Area (6 crd hrs)

    Students must select one of the following application areas and complete two courses within that specific area:

    Data Analytics and Visualization

    Select any two:

    • ENGE-E 516 Engineering Cloud Computing
    • ENGE-E 533 Deep Learning Systems (if not used above)
    • ENGE-E 583 Information Visualization
    • ILS-Z 534 Search
    • INFO-I 523 Big Data Applications and Analytics
    • INFO-I 535 Management, Access, and Use of Big and Complex Data
    • INFO-I 590 Topic: Introduction to Business Analytics Modeling
    • INFO-I 590 Topic: Data Visualization
    • INFO-I 606 Network Science

    Intelligent Systems Engineering

    Select any two:

    • ENGE-E 523 Internet of Things
    • ENGE-E 599 Autonomous Robots

    Precision Health

    Select any two:

    • ENGE-E 541 Simulating Cancer as an Intelligent System
    • INFO-I 590 Topic: Data Science for Drug Discovery, Health and Translational Medicine
    • INFO-I 590 Topic: Real World Data Science

    Cybersecurity

    Select any two:

    • 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

    4)    Data Science Capstone (3 crd hrs)

    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. The aim of this capstone is to demonstrate students' capabilities to prospective employers and inspire innovation.

    DSCI-D 590 Topic: Data Science in Practice

    The remaining 15 credit hours are selected from courses or individual data science-related course offerings found on the Data Science Program website. Students may not earn credit for courses taken to fulfill core, application, or capstone requirements. Be creative in your course strategies.

    Sample Electives:

    • CSCI-B 505 Applied Algorithms
    • CSCI-B 561 Advanced Database Concepts
    • CSC-B 657 Computer Vision
    • DSCI-D 591 Data Science Graduate Internship
    • DSCI-D 699 Data Science Independent Study
    • ENGR-E 517 High Performance Computing
    • ILS-Z 639 Social Media Mining
    • INFO-I 524 Big Data Projects and Software
    • INFO-I 590 Topic: Applied Data Science
    • INFO-I 590 Topic: Data Science OnRamp Basics
    • INFO-I 590 Topic: Data Science OnRamp Advanced
    • INFO-I 590 Topic: Data Semantics
    • INFO-I 590 Topic: Python
    • INFO-I 590 Topic: SQL and noSQL
    • SPEA-P 507 Data Analysis and Modeling in Public Affairs

  • Graduate Certificate in Data Science - Online

    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 their 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.

Minor

  • 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.

** Students admitted in prior terms should consult the Academic Bulletin for specific academic year degree requirements - Fall 2017, Fall 2016, Fall 2015. **