Applied Data Science Ph.D.Minor

The Ph.D. minor in Applied Data Science offers doctoral students in other disciplines the opportunity to learn data science approaches and apply them to problems in their major. Data science is now applied to every field. Data science concepts and approaches are required of researchers who collect, curate, manage, analyze, and visualize the big data found in massive, complex, diverse, and distributed repositories.

Our nation’s research enterprise is positioned for gains from the accelerated growth in the generation of digitally born data, and advances in security, privacy, and ethics will be paramount to realizing these gains. The gains are furthered by the potential to link scholarly publications with the data on which the research is based and uniquely identify each dataset for discovery through a web of linked data. The economic potential of research can be realized more fully and quickly when the research can be replicated, and when data can be shared broadly and used and reused in science and policymaking.

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 E570 Fundamentals of Statistics and Econometrics (3 cr.)
• HPER T591 Introduction to Statistics in Public Health (3 cr.)
• PBHL B561 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 B512 Scientific and Clinical Data Management (3 cr.)
• INFO B556 Biological Database Management (3 cr.)

Required Courses (9 cr.)

• INFO H515 Statistical Learning (3 cr.)
• INFO H516 Applied Cloud Computing for Data Intensive Sciences(3 cr.)
• INFO H517 Visualization Design, Analysis, and Evaluation(3 cr.)

Specialization Course

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

• ECON E570 Fundamentals of Statistics and Econometrics(3 cr.)
• ECON E574 Applied Econometrics and Forecasting(3 cr.)
• INFO H518 Deep Learning Neural Networks (3 cr.)
• INFO I575 Informatics Research Design(3 cr.)
• LIS S506 Introduction to Research(3 cr.)
• LIS S541 Information Policy(3 cr.)
• PBHL B572 Biostatistics Method II: Categorical Data Analysis (4 cr.)
• PBHL B574 Biostatistics Methods IV: Applied Longitudinal Data Analysis (3 cr.)
• PBHL B582 Introduction to Clinical Trials (3 cr.)
• PBHL B583 Applied Multivariate Analysis for Public Health (3 cr.)
• PBHL B585 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 IU Indianapolis or IU Bloomington 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 the Graduate Program Coordinator at 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.