Applied Data Science Ph.D. Minor

The Ph.D. minor in Applied Data Science teaches doctoral students how to manage, analyze, and visualize complex data while emphasizing ethics and security. Students learn to link research to datasets, improving reproducibility and driving discovery across fields.

What you'll learn

The Ph.D. minor in Applied Data Science is a 12-credit program that builds skills in statistics, data management, and visualization. Students complete core courses in statistical learning, cloud computing, and data visualization, then select a specialization aligned with their research. The program prepares students to handle complex data ethically and improve the impact of their research.

The Ph.D. minor in Applied Data Science is a 12-credit hour program comprised of four (4) courses, each of which is 3 credit hours.

Prerequisites (6 cr.)

A graduate course in inferential statistics, such as

• • • ECON-E 570 Fundamentals of Statistics and Econometrics (3 cr.)
    • HPER-T 591 Introduction to Statistics in Public Health (3 cr.)
    • PBHL-B 561 Introduction to Biostatistics I (3 cr.)
    • PSY 60000 Statistical Inference (3 cr.)

A graduate course in databases, such as

• • • CIT 58800 Advanced Data Management (3 cr.)
    • INFO-B 512 Scientific and Clinical Data Management (3 cr.)
    • INFO-B 556 Biological Database Management (3 cr.)

Required courses (9 cr.)

• • • DSCI-D 515 Statistical Learning (3 cr., was INFO-H 515)
    • DSCI-D 516 Cloud Computing for Data Science (3 cr., was INFO-H 516)
    • DSCI-D 517 Visualization Design, Analysis, and Evaluation (3 cr., was INFO-H 517)

Specialization course

Select one from the following courses in consultation with the minor advisor:

• • ECON-E 570 Fundamentals of Statistics and Econometrics (3 cr.)
  • ECON-E 574 Applied Econometrics and Forecasting (3 cr.)
  • DSCI-D 518 Deep Learning Neural Networks (3 cr., was INFO-H 518)
  • INFO-I 575 Informatics Research Design (3 cr.)
  • LIS-S 506 Introduction to Research (3 cr.)
  • LIS-S 541 Information Policy (3 cr.)
  • PBHL-B 572 Biostatistics Method II: Categorical Data Analysis (4 cr.)
  • PBHL-B 574 Biostatistics Methods IV: Applied Longitudinal Data Analysis (3 cr.)
  • PBHL-B 582 Introduction to Clinical Trials (3 cr.)
  • PBHL-B 583 Applied Multivariate Analysis for Public Health (3 cr.)
  • PBHL-B 585 Analysis of Observational Studies (3 cr.)
• PSY 60100 Correlation and Experimental Design (3 cr.)
• PSY 60500 Applied Multivariate Analysis (3 cr.)
• PSY 60800 Measurement Theory and the Interpretation of Data (3 cr.)

Grading policy

A minimum grade of B (3.0) is required in each course that is to count toward the minor. If a minimum grade of B (3.0) is not earned in a course, that course must be retaken. A course may only be retaken once. Students who fail to achieve the minimum grade of B (3.0) the second time they take a course will not be able to earn the Ph.D. minor.

A maximum of three credits from another institution may be applied toward the minor. No undergraduate courses may be applied to the minor.

Qualifying exam

The Ph.D. minor in Applied Data Science does not require a qualifying exam.

To be admitted to the Ph.D. Minor, the applicant must be currently enrolled at Indiana University in a doctoral program other than Data Science. The student must also be in good academic standing and possess knowledge and skills in calculus, linear algebra, and programming.

Application deadline

• Fall Admission: August 1
• Spring Admission: December 1

Application procedure

If you would like to apply to the Ph.D. minor, please submit an application package (PDF) via email to luddygrd@iu.edu. It must contain

• University student ID
• A one-page statement explaining the student’s (1) preparation for the Ph.D. minor and (2) how the minor contributes to the student’s doctoral research and (3) career goals
• A resume or curriculum vitae (CV)
• Transcripts highlighting courses in calculus, linear algebra, and programming
• Approval from the student’s primary advisor

Accepted students will be notified promptly.