Ph.D. in Data Science
Discover novel solutions to data research problems
There’s no choice but to lead when you’re breaking new ground. Guide rapid development in an emerging field when you earn a Ph.D. in Data Science through the IU School of Informatics and Computing at IUPUI.
Graduates of our program—the first of its kind in both Indiana and the Big Ten—develop the skills to make pioneering research contributions to data science theory and practice in academic and the industrial sectors.
Data scientist = #1
Job ranking in America per Glassdoor, with $110,000 median base salary
A dynamic data science environment
From life science to product development, the demand is only growing for professionals who can apply principles of data science to specific areas. Our graduates learn to define and investigate relevant research problems in this interdisciplinary field.
The School of Informatics and Computing at IUPUI offers students a unique opportunity to work with faculty who are international leaders in their fields, and to collaborate with researchers from leading global health and life science research centers on campus and in Indianapolis.
Our students acquire the skills to develop inventive and creative solutions to data research problems—solutions that demonstrate a high degree of intellectual merit and the potential for broader impact. The Ph.D. curriculum also prepares students to make research contributions that advance the theory and practice of data science.
“We provide a rigorous, high-quality, competitive program that prepares intellectual leaders in a rapidly developing field.”
Karl MacDorman, associate dean of academic affairs and director of the data science program
Map the path from big data to knowledge
Graduates learn to develop and evaluate novel approaches to collecting, organizing, managing, and extracting knowledge and insights from massive, complex, distributed, heterogeneous data sets. The program hones students’ ability to:
- Define, create, adapt, and apply rigorous research methods
- Communicate research findings effectively to peers through scholarly, peer-reviewed publications that appear in international venues
- Define, conduct, and manage a research project that involves several people and interdisciplinary expertise
- Contribute to writing research grant proposals aimed at securing external funding to support research activities
- Understand and address ethical and professional issues related to their research, including approval processes and certification for human-subject research
Deep technical skills and the ability to formulate and test hypotheses using massive and heterogeneous data provide the foundation for graduates who can become successful researchers either in academic settings or in industrial research and development laboratories.
Careers in Data Science
This degree leads to positions within academia that include research, research support, and tenure-track positions in major universities.
Positions in industry include:
- Data scientist
- Director of research
- Senior data analyst
- Strategic innovation manager
Plan of Study
Data Science Core (24 cr.)
- INFO I501 Introduction to Informatics (3 cr.)
- LIS S511 Database Design (3 cr.) or CSCI 54100 Database Systems (3 cr.)
- STAT 51100 Statistical Methods I or higher (3 cr. requires approval)
- INFO H515 Data Analytics (3 cr.) or CSCI 57300 Data Mining (3 cr.)
- INFO H516 Applied Cloud Computing for Data Intensive Sciences (3 cr.) or CSCI 59000 Cloud Computing (3 cr.)
- INFO H517 Visualization Design, Analysis, and Evaluation (3 cr.) or CSCI 55200 Data Visualization (3 cr.)
- LIS S541 Information Policy (3 cr.)
- INFO I575 Informatics Research Design (3 cr.)
Methods Courses (18 cr.)
- CSCI 52000 Computational Methods in Analysis (prerequisites: CSCI 23000 Computing II or equivalent and MATH 35100 Elementary Linear Algebra OR MATH 511 Linear Algebra and Applications)
- CSCI 58000 Algorithm Design, Analysis, and Implenmentation
- NURS-L 650 Data Analysis for Clinical and Administrative Decision-Making (3 cr.)
- NURS-R 612 Interpretive Data Analysis (2 cr.)
- PBHL-B 515 Biostatistics Practicum (3 cr.)
- PBHL-B 527 Introduction to Clinical Trials (3 cr.)
- PBHL-B 546 Applied Longitudinal Data Analysis (3 cr.)
- PBHL-B 571 Biostatistics Method I: Linear Models in Public Health (4 cr.)
- PBHL-B 621 Advanced Statistical Computing (3 cr.)
- PBHL-B 636 Advanced Survival Analysis (3 cr.)
- PBHL-B 646 Advanced Generalized Linear Models (3 cr.)
- PSY 60000 Statistical Inference (3 cr.)
- PSY 60100 Experimental Design (3 cr.)
- PSY 60800 Measurement Theory and Interpret Data (3 cr.)
- PSY 64000 Survey of Social Psychology I (3 cr.)
- PSY-I 643 Field Methods & Experimentation (3 cr.)
- SOC-R 551 Quantitative Methods (3 cr.)
- SOC-R 559 Intermediate Soc. Statistics (3 cr.)
- STAT 51100 Statistical Methods 1 (3 cr.)
- STAT 51200 Applied Regression Analysis (3 cr.)
- STAT 51600 Basic Probability Applications (3 cr.)
- STAT 51900 Introduction to Probability (3 cr.)
- STAT 52100 Statistical Computing (3 cr.)
- STAT 52200 Sampling and Survey Techniques (3 cr.)
- STAT 52400 Applied Multivariate Analysis (3 cr.)
- STAT 52500 Generalized Linear Model (3 cr.)
- STAT 52800 Mathematical Statistics I (3 cr.)
- STAT 52900 Applied Decision Theory and Bayesian Statistics (3 cr.)
- STAT 53600 Introduction to Survival Analysis (3 cr.)
- STAT 61900 Probability Theory (3 cr.)
- STAT 62800 Advanced Statistical Inference (3 cr.)
May include up to 6 credit hours of INFO-I 790 Informatics Research Rotation.
Specialization (18 cr.)
- Disciplinary Affinities (0–6 cr.)
- Minor (12–18 cr.)
The student must complete a minor within a domain appropriate to the chosen specialization and/or research area. All courses must be graduate-level and taken outside the Data Science program.
Qualifying Examination, Written and Oral
A student must successfully complete a written and oral qualifying examination before the fifth semester of the program. The written exam has a breadth part and a depth part. The breadth part covers the program’s core courses. The depth part additionally covers material from the student’s research.
The oral exam takes place shortly after the student passes the written exam. The oral exam is based on the student’s response to the written exam and the core courses. The both the written and oral exams are prepared and evaluated by faculty in the school who are familiar with the content of the core courses.
The student must pass both the written exam and the oral exam before advancing to candidacy. The student may retake once either the written exam or oral exam, but not both, if they do not pass that part on the first attempt. For further details, consult with the data science program director.
Dissertation (30 cr.)
A dissertation is a written elaboration of original research that makes creative contributions to the student’s chosen area of specialization. The student will enroll multiple times in INFO I890 Thesis Readings and Research (1-12 cr.) while completing the dissertation. All requirements must be completed within seven years of passing the qualifying exams. The dissertation process includes the following components:
- Proposal: This is an in-depth oral review undertaken by students who have made significant progress in their research. The proposal will be defended at a public colloquium. The student must complete the proposal within one year of passing the qualifying exams.
- Defense: The student must defend his or her dissertation in an open seminar scheduled when doctoral research is almost complete.
Please refer to the IUPUI Graduate School Bulletin for more details on the dissertation process.
Take the next step
Students will demonstrate competency in research:
- Critically evaluate the published scholarly record.
- Critically apply the theories and methodologies of data science to new research in their primary area of study.
- Apply appropriate principles, frameworks, and models to evaluate and interpret the frontiers of knowledge in their primary area of study.
- Demonstrate expository and oral communication skills appropriate to a Ph.D., publishing and presenting work in their field.
- Critique data practices for ethical issues, including discriminatory practices, power imbalances, and invasions of privacy.
- Demonstrate advanced competency in data science tools and techniques, applied statistical analysis, and a domain area relevant to their area of specialization.
- Develop a record of relevant scholarship.
- Demonstrate an ability to conduct independent, original research with a depth of knowledge in the chosen area of specialization.
Students will demonstrate competency in data analytics:
- Design and execute ethical research using quantitative and experimental methods.
- Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
- Apply inferential statistics, predictive analytics, and data mining to informatics-related fields.
- Analyze datasets with supervised learning methods for functional approximation, classification, and forecasting and unsupervised learning methods for dimensionality reduction and clustering.
- Identify, assess, and select appropriately among data analytics methods and models for solving a particular real-world problem, weighing their advantages and disadvantages.
- Write programs to perform data analytics on large, complex datasets.
Students will demonstrate competency in data management and infrastructure:
- Design and implement relational databases using commercial database management systems according to database concepts and theory.
- Diagram a relational database design based on an identified scenario.
- Produce database queries using SQL.
- Perform database administration tasks.
- Describe the data management activities associated with the data lifecycle.
- Overcome difficulties in managing very large datasets, both structured and unstructured, using nonrelational data storage and retrieval (NoSQL), parallel algorithms, and cloud computing.
- Apply the MapReduce programming model to data-driven discovery and scalable data processing for scientific applications.