Embedded Pathways to a Ph.D.
Applicants* who want to be considered for the PhD in Analytics and Data Science without
a Masters degree (but have completed a quantitative Bachelor’s degree) can apply to
the embedded MS in Computer Science or the embedded MS in Applied Statistics and Analytics.
Ph.D. with (MSAS) Embedded
Curriculum for the PhD in Analytics and Data Science with an embedded MS in Applied Statistics (all courses are 3 credit hours unless otherwise specified):
Ph.D. with (MSCS) Embedded
Curriculum for the PhD in Analytics and Data Science with an embedded MS in Computer Science (all courses are 3 credit hours unless otherwise specified):
MSAS Year One (Fall)
- CS 6045 – Advanced Algorithms
- STAT 7100 – Statistical Methods
- CS 8267 – Machine Learning
- STAT 8240 – Data Mining 1
MSAS Year One (Spring)
- STAT 8120 – Experimental Design
- CS 8265 – Big Data Analytics
- STAT 8940 – Applied Statistics Project
- STAT 8250 – Data Mining 2
After completion of Year One, students will take a Data Science Qualifying Exam for consideration to be accepted into the PhD in Analytics and Data Science Program.
MSCS Year One (Fall)
- CS 6045 – Advanced Algorithms
- CS 6041 – Theory of Computation
- CS 8267 – Machine Learning
- STAT 8240 – Data Mining 1
MSCS Year One (Spring)
After completion of Year One, students will take a Data Science Qualifying Exam for
consideration to be accepted into the PhD in Analytics and Data Science Program.
MSAS Year Two (Fall)
- STAT 7020 – Statistical Computing
and Simulation - MATH 8030 – Discrete Optimization
- STAT 8940 – Applied Statistics Project
- STAT Elective (8000 level)
MSAS Year Two (Spring)
- STAT Elective (8000 level)
- MATH 8020 – Graph Theory
- STAT/DS Elective (8000 level)
(6 credit hours)
After completion of Year Two, students are expected to have completed the requirements for the MS in Applied Statistics and Analytics.
MSCS Year Two (Fall)
- 2 CS Electives (6000 or 7000 level)
- MATH 8030 - Discrete Optimization
MSCS Year Two (Spring)
After completion of Year Two, students are expected to have completed the requirements
for the MS in Computer Science.
MSAS Year Four (Fall)
- DS 9700 – Data Science Doctoral Applied Research Lab
- DS 9900 – Data Science Doctoral Dissertation (9 credit hours)
MSAS Year Four (Spring)
- DS 9700 – Data Science Doctoral Applied Research Lab
- DS 9900 – Data Science Doctoral Dissertation (9 credit hours)
After completion of Year Four, students will defend a dissertation proposal.
MSAS Year Five (Fall)
- DS 9900 – Data Science Doctoral Dissertation (9 credit hours)
MSAS Year Five (Spring)
- DS 9900 – Data Science Doctoral Dissertation (6 credit hours)
After completion of Year Five, students will defend a final dissertation.
*Students with no previous CS training will be required to complete the Foundations
in CS (MOOC version of CS5040) for no credit hours in the preceding summer. Students
with no previous STAT degrees will have to complete STAT7010 and STAT8/7210 for credit
hours in the preceding summer.
MSCS Year Five (Fall)
- DS 9900 – Data Science Doctoral Dissertation (9 credit hours)
MSCS Year Five (Spring)
- DS 9900 – Data Science Doctoral Dissertation (6 credit hours)
After completion of Year Five, students will defend a final dissertation.
* Students with no previous CS training will be required to complete a Foundations in Computer Science course (MOOC version of CS5040) for no credit hours in the preceding summer. Students with no previous STAT degrees will have to complete STAT7010 and STAT8/7210 for credit hours in the preceding summer.