College of Arts and Science

Departmental E-mail: statdept [at] indiana [dot] edu

Departmental URL: www.stat.indiana.edu/

(Please note that when conferring University Graduate School degrees, minors, certificates, and sub-plans, The University Graduate School’s staff use those requirements contained only in The University Graduate School Bulletin.)

Curriculum

Curriculum
Courses
Faculty

Degrees Offered

Master of Science in Statistical Science, Dual Master of Science in Statistical Science and Data Science, Doctor of Philosophy in Statistical Science, Master of Science in Applied Statistics,  Dual Master of Science in Applied Statistics and Data Science

Special Departmental Requirements

(See also general University Graduate School requirements.)

Admission Requirements

Statistics is an increasingly interdisciplinary field. Recognizing that fact, the IU Department of Statistics welcomes students from a variety of quantitative backgrounds, not just statistics and mathematics.

To be admitted to the Master of Science in Applied Statistics program a student must be pursuing a Ph.D. in another program at IU.

Students entering our Master of Science or Ph.D. in Statistical Science programs should have a bachelor's or master's degree from an accredited university. Academic preparation should include at least two undergraduate courses in statistics, some background in mathematics that includes courses in multivariate calculus and linear algebra, and some familiarity with computer programming. 

Applicants will be evaluated using a combination of academic transcripts, grade-point averages, GRE scores, TOEFL scores (for international applicants), letters of recommendation, and personal statements. Selection criteria include breadth and depth of preparation, quality of academic performance, and motivation. 

Master of Science in Statistical Science

The M.S. in Statistical Science program trains students to become applied statisticians who collaborate with researchers in various disciplines to design experiments and analyze data.  Each MSSS student will be advised by the Director of Graduate Studies and will also be assigned a faculty mentor.

Course Requirements 

A total of 31 credit hours including STAT S610, S611, S621, S622, S631, S632, and S690. Students must also complete either a one-semester consulting internship, S692 or S693, or a research paper, S799. The remaining 6 credit hours can be from any graduate statistics courses approved by the Director of Graduate Studies.

Dual Master of Science in Statistical Science and Data Science

The Dual M.S. Degree program in Statistical Science and Data Science is for students in the M.S. in Statistical Science program  who also want to acquire deep computational skills. Students in the dual degree program satisfy the requirements of both the Statistical Science and Data Science M.S. programs, and receive both degrees in 3 or fewer years. Students in the M.S. in Statistical Science program must apply separately for admission to the M.S. in Data Science  The dual degree program requires a minimum of 52 credit hours of graduate course work, a “savings” of 9 credit hours compared to completing both programs individually. Students will still complete the existing requirements of each individual degree.

Course Requirements (52 credits)

• 3 credits of Data Mining and Search (CSCI-B 551, CSCI-B 555, CSCI-B 565, CSCI-P 556, ENGR-E 511, ILS-Z 534, or INFO-I 606)
• 3 credits of Data Management and Engineering (CSCI-B 561, ENGR-E 516, INFO-I 535, or DSCI-D 532)
• 3 credits of Data Visualization and Storytelling (ENGR-E 583, ENGR-E 584, or INFO-I 590 Data Visualization)
• 6 credits from one the following domains
  • Augmented and Virtual Reality (INFO-I 590 Artificial Life in Virtual Reality, Building Virtual Worlds, Creating Virtual Assets, Introduction to Virtual Reality)
  • Data Security and Privacy (INFO-I 520, INFO-I 525, INFO-I 533, INFO-I 538)
  • Economic Data Analytics (ECON-M 504, ECON-M 511, ECON-M 514, ECON-M 518, ECON-M 524)
  • Health and Biomedical Data Science (INFO-I 507, INFO-I 519, INFO-I 529)
  • Human Robotic Interaction (CSCI-B 657, ENGR-E 599 Autonomous Robotics, INFO-I 513, INFO-I 527, INFO-I 540, INFO-I 542)
  • Social Data Science (ENGR-E 583 (may be counted only once), ILS-Z 639, INFO-I 513, INFO-I 590 Data Visualization (may be counted only once), INFO-I 606 (may be counted only once))
• 6 credits of DS (CSCI/ENGR/INFO/ILS/DSCI) 500+ courses, including at most 3 credits of DSCI-D 590 Data Science On-Ramp
• 25 credits of S610, S611, S621, S622, S631, S632, S690, and S692
• 6 credits of DS (CSCI/ENGR/INFO/ILS/DSCI) or STAT 500+ courses 

Doctor of Philosophy in Statistical Science

The Ph.D. program trains students as research statisticians who develop new statistical methodology. This program is for graduate students who wish to obtain positions as research statisticians in academia, government, or industry.  Each Ph.D. student will be advised by the Director of Graduate Studies and will also be assigned a faculty mentor until an advisory committee is formed.

Course Requirements 

A total of 90 credit hours, including at least 60 credit hours of coursework; dissertation research to reach 90 credit hours.

Core Courses (9 credit hours): Math-M 413: Introduction to Analysis I, STAT-S 610: Introduction to Statistical Computing, STAT-S 611: Applied Statistical Computing

Data Analysis Courses (10 credit hours): STAT-S 631: Applied Linear Models I, STAT-S 632: Applied Linear Models II, STAT-S 690: Statistical Consulting

Advanced Statistical Theory Courses (12 credit hours): STAT-S 721: Advanced Statistical Theory I, STAT-S 722: Advanced Statistical Theory II plus at least two semesters of STAT-S 785: Seminar on Statistical Theory

Research Project (3 credit hours): STAT-S 799: Research in Statistics

Elective and Minor Courses (26 credit hours): All students must complete a Ph.D. minor in another graduate program. Minor requirements are specified by the awarding department and are described in the University Graduate School Bulletin. All elective courses in this category must be approved by the Director of Graduate Studies.

Qualifying Examinations

Students advance to candidacy by completing required coursework, passing an examination on statistical theory, and completing a 1-semester research project under faculty supervision. The Statistical Theory exam is a written examination based on material covered in STAT-S 721-722 (Advanced Statistical Theory), administered following the conclusion of that sequence. The research project is completed in STAT-S 799 and culminates in an oral presentation and a written paper.  The project may or may not constitute original research; it may emphasize data analysis, statistical methodology, or statistical theory; and it may or may not lead to dissertation research.  The oral presentation usually takes place near the end of the 799 semester and the paper then incorporates suggested revisions. Students who fail either qualifying examination (stat theory and research project) more than once will be dismissed from the program.

Advisory and Research Committees

For each PhD student, a doctoral advisory committee (typically consisting of the DGS, the anticipated 721/722 instructor, and the anticipated research project supervisor) will be formed in the first year of training. After passing both qualifying exams , a student should begin searching for a research supervisor and dissertation topic.  After advancing to candidacy, a student must form their research committee. The student’s committee (advisory or research) will consult with the student at least once per year to help the student determine their program of graduate study, develop a research program, approve the student’s course selections, and review the student’s progress in all areas (for example, completion of required courses, course grades, and research progress). The student’s committee will determine whether or not the student is making adequate progress in all areas. Should the advisory (or research) committee determine that a student is not making adequate progress in any area, this may be grounds for probation, elimination of departmental funding, or dismissal from the program.

Dissertation Proposal and Research

A dissertation is required. The dissertation represents original methodological research by the student. The research should be of sufficient quality to merit publication in peer-reviewed journals.

At least 6 months before defending the dissertation the student should submit a dissertation proposal and have their research committee approved by the Graduate School. The dissertation proposal is an oral exam intended to demonstrate to the statistics faculty that the student is prepared to begin research. The student will make an oral presentation that outlines the proposed research, including summaries of related work and descriptions of the techniques that will be used. The dissertation committee and other statistics faculty will then question the student.

Ph.D. Minor in Statistical Science

Doctoral students obtaining a Ph.D. in another discipline are welcome to choose Statistics as an outside minor. Four graduate courses in statistics are required, at least three of which must be at the 600-level or above taken from the Department of Statistics. The specific minor courses must be approved by the Director of Graduate Studies of the Department of Statistics.

Master of Science in Applied Statistics

The M.S. in Applied Statistics is intended for the student pursuing a Ph.D. degree in another field who wishes to enhance his or her statistical knowledge and credentials by obtaining a graduate degree in Statistics, in addition to a Ph.D. degree in his or her primary field of study.

Course Requirements

A total of 31 credit hours.  The following 19 hours are required: (1) STAT S520 or S620; (2) one of S610, S611, S612; (3) S631, S632, and S690; (4) one additional course from STAT. The remaining 12 credit hours must be taken in an area relevant to the field of Statistics and must be approved by the Director of Graduate Studies.

MSAS students who choose to minor in statistics must complete the minor coursework prior to submitting their candidacy and may use all the minor coursework towards the MSAS. The student will then be permitted to use an additional three courses (9 credits total) from the PhD courses towards the MSAS requirements. In this case, the total number of courses allowed to be used from the MSAS towards the PhD is four (12 credits) and the total courses allowed from the PhD towards the MSAS is seven (21 credits). The student would need three additional courses that are not used in the PhD to complete the MSAS requirements.

MSAS students who do not minor in statistics can use no more than 12 credit hours from the PhD towards the MSAS. 

Dual Master of Science in Applied Statistics and Data Science

The Dual M.S. Degree program in Data Science and Applied Statistics is recommended for students in the M.S. in Data Science degree program  who also want to acquire deep statistical skills. Students in the dual degree program satisfy the requirements of both the Data Science and Applied Statistics M.S. programs, and receive both degrees in 3 or fewer years. Students in the M.S. in Data Science program must apply separately for admission to the M.S. in Applied Statistics. The dual degree program requires a minimum of 52 credit hours of graduate course work, a “savings” of 9 credit hours compared to completing both programs individually. Students will still complete the requirements of each degree. 

Course Requirements (52 credits):

• 3 credits of Data Mining and Search (CSCI-B 551, CSCI-B 555, CSCI-B 565, CSCI-P 556, ENGR-E 511, ILS-Z 534, or INFO-I 606)
• 3 credits of Data Management and Engineering (CSCI-B 561, ENGR-E 516, INFO-I 535, or DSCI-D 532)
• 3 credits of Data Visualization and Storytelling (ENGR-E 583, ENGR-E 584, or INFO-I 590 Data Visualization)
• 6 credits from one the following domains
  • Augmented and Virtual Reality (INFO-I 590 Artificial Life in Virtual Reality, Building Virtual Worlds, Creating Virtual Assets, Introduction to Virtual Reality)
  • Data Security and Privacy (INFO-I 520, INFO-I 525, INFO-I 533, INFO-I 538)
  • Economic Data Analytics (ECON-M 504, ECON-M 511, ECON-M 514, ECON-M 518, ECON-M 524)
  • Health and Biomedical Data Science (INFO-I 507, INFO-I 519, INFO-I 529)
  • Human Robotic Interaction (CSCI-B 657, ENGR-E 599 Autonomous Robotics, INFO-I 513, INFO-I 527, INFO-I 540, INFO-I 542)
  • Social Data Science (ENGR-E 583 (may be counted only once), ILS-Z 639, INFO-I 513, INFO-I 590 Data Visualization (may be counted only once), INFO-I 606 (may be counted only once))
• 6 credits of DS (CSCI/ENGR/INFO/ILS/DSCI) 500+ courses, including at most 3 credits of DSCI-D 590 Data Science On-Ramp
• 3 credits of STAT S520 or a more advanced course on statistical theory approved by the DGS
• 3 credits of STAT S610, S611, or S612
• 10 credits of STAT S631, S632, and S690
• 3 credits of a STAT 500+ course
• 12 credits of DS (CSCI/ENGR/INFO/ILS/DSCI) or STAT 500+ courses