Data science and analytics: Online master’s degree
Master of science in data science and analytics (MS)
Delivery of this program is blended; two campus visits are required.
In the first year of the program, you will come to the MU campus for a weeklong intensive application of the core curriculum and to execute a project from beginning to end.
In the second year, you will visit campus again to showcase your capstone design and to participate in a weeklong, intensive collaboration with industry partners.
Successful completion requires a minimum of 34 credit hours.
Courses are 3 credit hours each, unless otherwise indicated. The University reserves the right to change or cancel courses as needed.
Core courses (19 credit hours)
|DATA_SCI 7010 Introduction to Data Analytics|
|DATA_SCI 7020 Statistical and Mathematical Foundations for Data Analytics|
|DATA_SCI 7030 Database and Analytics|
|DATA_SCI 8010 Data Analytics from Applied Machine Learning|
|DATA_SCI 8020 Big Data Security|
|DATA_SCI 7040 Big Data Visualization|
|DATA_SCI 8000 Data and Information Ethics|
1 credit hour
Concentration area courses (9 credit hours)
|DATA_SCI 7637 Streaming Social Media Data Management and Analysis|
|DATA_SCI 8220 Communication Networks/Analytics|
|DATA_SCI 8612 Spatial and Geostatistical Analysis|
|DATA_SCI 8410 Data Mining and Information Retrieval|
|DATA_SCI 8635 Cloud Computing for Data Analytics|
|DATA_SCI 8310 Advanced Visualization I|
|DATA_SCI 8320 Advanced Visualization II|
|DATA_SCI 7810 Geospatial Data Engineering|
|DATA_SCI 8530 Remote Sensing Data Analytics|
|DATA_SCI 8750 Parallel Computing for Data Analytics|
|DATA_SCI 8110 Genomics Analytics|
|DATA_SCI 8130 Data Science for Healthcare|
Advanced courses (6 credit hours)
|DATA_SCI 8080 Big Data Analysis Case Study|
|DATA_SCI 8090 Big Data Capstone|
Note Students with a limited programming and statistics background are encouraged to take select foundational courses. Please contact DSAMasters@missouri.edu for more information.
The University of Missouri is accredited by the Higher Learning Commission, one of six regional institutional accreditors in the United States.
Prior to acceptance into the program, you may enroll as a post-baccalaureate (formerly nondegree-seeking) student in up to 12 credit hours. Enrollment does not guarantee acceptance. Learn more about nondegree-seeking university student admissions.
How to apply
Fulfill the admission requirements:
- Completed baccalaureate or advanced degree from a regionally accredited institution.
- Minimum GPA of 3.0 in the last 60 hours of undergraduate education.
- If English is not your native language, please submit a TOEFL score of at least 80 (internet-based test), an IELTS score of at least 6.5, a Pearson Test of English (PTE) score of at least 59 or a Cambridge C1 Advanced score of at least 180.
- GRE scores are not required.
Mark your calendar
Submit your application in time.
|To start classes in||Apply by||Classes begin|
Gather your papers
Applying will be easier and faster if you gather these required documents beforehand:
- Transcripts of all previous college or university education. Upload unofficial transcripts in your online application. If you are accepted, you will be asked to provide official transcripts.
- Letter of interest describing why you wish to pursue a master's degree in data science and analytics, how data science will advance your career/life and any other information you feel might support your application.
- Résumé or curriculum vitae.
- TOEFL, IELTS, PTE or C1 Advanced score reports for applicants whose native language is not English. For information on sending test scores, visit the Grad School website.
Complete the online application
Carefully follow the instructions on the application site.
- Upload the above documents in the online application
- Degree/Delivery: Masters-Distance (Online/Remote Site/etc.)
- Academic Program (choose one):
- Data Science and Analytics/BioHealth Analytics-Distance (MS)
- Data Science and Analytics/Geospatial Concentration-Distance (MS)
- Data Science and Analytics/High Performance Computing-Distance (MS)
- Data Science and Analytics/Human Centered Science Design-Distance (MS)
- Data Science and Analytics/Strategic Communications and Data Journalism-Distance (MS)