PhD in Informatics with Emphasis in Geospatial Informatics
Degree Requirements
All students must have at least 72 credit hours at the graduate level, of which 15 credits must be at the 8000-level not including research, problems, lab rotations, or seminar. Transferring credits will be at the recommendation of the student's doctoral committee and the approval of the MUIDSI Education Committee.
| Required Core Courses - Geospatial Informatics Area | ||
| DATA_SCI 7010 | Principles of Data Science and Analytics | 3 |
| DATA_SCI 8520 | Spatial Analytics and Geostatistical Analysis | 3 |
| Required Methods Courses (9 Credit Minimum) | ||
| INFOINST 8810 | Research Methods in Informatics | 3 |
| DATA_SCI 7020 | Statistical and Mathematical Foundations for Data Analytics | 3 |
| or STAT 7510 | Applied Statistical Models I | |
| Student must choose one additional 3-credit methods course with doctoral committee approval. | ||
| Lab Rotations and Seminar | ||
| INFOINST 8087 | Seminar in Informatics | 0.5-1 |
| INFOINST 8088 | Lab Rotations in Informatics | 1-3 |
| Research | ||
| INFOINST 8090 | Dissertation (pre-candidacy) Research in Informatics | 1-99 |
| INFOINST 9090 | Dissertation (post-candidacy) Research in Informatics | 1-99 |
| Emphasis Area Course Requirements (Must select at least 9 credits from the following list) | ||
| DATA_SCI 8510 | Geospatial Data Engineering and Geodatabase Development | 3 |
| DATA_SCI 8530 | Geospatial AI and Image Analysis | 3 |
| GEOG 7710 | Spatial Analysis in Geography | 3 |
| GEOG 7740 | Location Analysis and Site Selection | 3 |
| GEOG 7810 | Landscape Ecology and GIS Analysis I | 3 |
| GEOG 7840 | Geographic Information Systems I | 3 |
| GEOG 7940 | Advanced Geographic Information Systems (GIS II) | 3 |
| GEOG 7860 | Advanced Remote Sensing | 3 |
| GEOG 8840 | Seminar: Applied Remote Sensing | 3 |
| Area Course Electives (Must select at least 6 credits from the following list) | ||
| AN_SCI 8443 | Functional Genomics of Mammals | 4 |
| AN_SCI 8633 | Molecular and Network Evolution | 3 |
| BIOL_EN 7560 | Observing the Earth from Space | 3 |
| CMP_SC 7380 | Database Management Systems I | 3 |
| CMP_SC 7740 | Interdisciplinary Introduction to Natural Language Processing | 3 |
| CMP_SC 8370 | Data Mining and Knowledge Discovery | 3 |
| CMP_SC 8630 | Data Visualization | 3 |
| CMP_SC 8725 | Supervised Learning | 3 |
| CMP_SC 8735 | Unsupervised Learning | 3 |
| DATA_SCI 7030 | Applied SQL for Database and Analytics | 3 |
| DATA_SCI 7040 | Big Data Visualization | 3 |
| DATA_SCI 8110 | Genomics Analytics | 3 |
| DATA_SCI 8120 | Multi-Omics Analytics | 3 |
| DATA_SCI 8130 | Data Science for Health Care | 3 |
| DATA_SCI 8140 | Advanced Methods in Health Data Science | 3 |
| DATA_SCI 8150 | Precision Medicine Analytics | 3 |
| DATA_SCI 8160 | Population Health Analytics | 3 |
| DATA_SCI 8230 | Streaming Social Media Data Management and Analytics | 3 |
| DATA_SCI 8410 | Data Mining and Information Retrieval | 3 |
| DATA_SCI 8420 | Cloud Computing for Data Analytics | 3 |
| DATA_SCI 8430 | Parallel Computing for Data Analytics | 3 |
| DATA_SCI 8310 | Advanced Visualization I | 3 |
| DATA_SCI 8320 | Advanced Visualization II | 3 |
| ECE 7270 | Computer Architecture | 4 |
| ECE 7590 | Computational Neuroscience | 3 |
| ECE 8320 | Nonlinear Systems | 3 |
| ECE 8570 | Neural Dynamics and Communication | 3 |
| ECE 8580 | Machine Learning in Neuroscience | 3 |
| BBME 7410 | Introduction to the US Health Care System for Biomedical Informatics | 3 |
| BBME 8435 | Information Security, Evaluation and Policy | 3 |
| BBME 8437 | Data Warehousing and Data/Text Mining for Health Care | 3 |
| BBME 8441 | Biomedical and Health Vocabularies and Ontologies | 3 |
| BBME 8443 | Enterprise Information and Solutions Architecture for Strategic Healthcare Operations | 3 |
| BBME 8571 | Decision Support in Health Care Systems for Biomedical Informatics | 3 |
| BBME 8610 | Consumer Health Informatics | 3 |
| INFOINST 8190 | Computational Systems Biology | 3 |
| INFOINST 8210 | Structural Bioinformatics of Proteins, Complexes, System | 3 |
| INFOINST 8450 | Precision Medicine Informatics | 3 |
| INFOINST 8870 | Knowledge Representation in Biology and Medicine | 3 |
| IS_LT 9410 | Seminar in Information Science and Learning Technology | 1-3 |
| NURSE 9460 | Theories and Interventions in Health Behavior Science | 3 |
| PTH_AS 7450 | Precision Medicine Informatics | 3 |
The following is a brief synopsis of the general degree requirements; please see the Informatics Institute web site for complete details:
- Students must take required and area courses.
- Students must pass a qualifying examination.
- Students must present at least one institutional seminar annually.
- Students are required to complete a comprehensive exam, which includes written and oral elements, within a specified time frame.
- Students must pass a comprehensive examination at least 7 months before their scheduled defense.
- Students must submit and defend a dissertation describing the results of successful and original research in one of the branches of informatics.
- To show research progress, students are expected to be working toward presenting at conferences and publishing in peer-reviewed journals based on their informatics research.
Qualifying Exam Process
Students are expected to take the qualifying exam by the end of their third semester in the program. The exam will be based on their previous coursework, lab rotation experience, and one-page research statement. For more information on qualifying exam procedures, please see the MUIDSI student handbook.
Comprehensive Exam Process
The comprehensive exam consists of two parts - the written portion, comprised of an R01 research proposal, and the oral exam. For more information on the comprehensive exam process, please see the MUIDSII student handbook.
Dissertation Defense Process
The doctoral dissertation defense must be scheduled no sooner than seven months after successful completion of the comprehensive exam. The dissertation must be written on an informatics subject approved by the candidate's doctoral program committee, must embody the results of original and significant investigation, and must be the candidate's own work. Please refer to the MUIDSII student handbook for additional information.
Admission Contact Information
MUIDSI Staff (muiiadmissions@missouri.edu)
22 Heinkel Building
Columbia, MO 65211-2060
Phone: 573-882-0135
FAX: 573-884-8709
Institute for Data Science and Informatics website: https://muidsi.missouri.edu/
Admission Criteria
Fall deadline: The deadline for Fall admission is December 1 to be considered for departmental and Graduate School fellowships and assistantships. Applications received after the deadline will be evaluated on a case-by-case basis.
- Preferred GPA: 3.3 out of 4.0
- Preferred GRE scores (optional): 307
- Preferred TOEFL score: 4.5
- Preferred IELTS score: 6.5
All Required Documents
Students are required to send ALL required application materials through the Graduate School's on-line application system. To begin your application, please see the Graduate School website.
- Curriculum Vitae
- Statement of Purpose, which should include a summary of why the applicant is interested in pursuing an advanced informatics degree, a brief description of your previous research experiences, the specific area of informatics you are interested in pursuing, and your future career goals and plans in the informatics field.
- TOEFL/IELTS scores for international applicants, if required.
- Three letters of recommendation from faculty or supervisors who can evaluate the applicant’s credentials and potential to become successful in the area of informatics.
- Scanned copies of transcripts from each college and university attended. If accepted, applicants will be required to have official copies of their transcripts sent directly from the institution to the Graduate School.
Optional Documents
Applicants are encouraged to submit representative publications in informatics, if available.
GRE scores.