This dual degree program awards an M.S. in Statistical Science and an M.S. in Data science. It is recommended for students who have a background in statistics and also want to acquire deep computational skills.
You’ll earn two degrees in fewer credit hours and less time than it would take to earn them separately.
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We want to hear from you! The Luddy School is here to answer any questions you may have, and we’re more than happy to guide you down your path to tomorrow.
You need to apply and be admitted by both the data science and statistics admission committees.
Combining the degrees means you can graduate with 52 credit hours, instead of the 61 credit hours it would take to earn the degrees separately.
Academic requirements
Students need to fulfill the following requirements.
Data Mining and Search (3 credits)
CSCI-B 551
CSCI-B 555
CSCI-B 565
CSCI-P 556
ENGR-E 511
ILS-Z 534
INFO-I 606
Data Management and Engineering (3 credits)
CSCI-B 561
ENGR-E 516
INFO-I 535
DSCI-D 532
Data Visualization and Storytelling (3 credits)
ENGR-E 583
ENGR-E 584
INFO-I 590 Data Visualization
Graduate level Luddy courses (12 credits)
No more than 6 credits from STAT courses
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 one the following domains
INFO-I 590
Artificial Life in Virtual Reality
Building Virtual Worlds
Creating Virtual Assets
Introduction to Virtual Reality
INFO-I 520
INFO-I 525
INFO-I 533
INFO-I 538
ECON-M 504
ECON-M 511
ECON-M 514
ECON-M 518
ECON-M 524
INFO-I 507
INFO-I 519
INFO-I 529
CSCI-B 657
ENGR-E 599 - Autonomous Robotics
INFO-I 513
INFO-I 527
INFO-I 540
INFO-I 542
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)
Degree maps
Below are several sample degree paths students could take.
Typical degree map
The typical path is for students who do not require remedial data science coursework and know from the outset that they want to pursue the dual degree.
Example only
Year
Fall
Spring
First
STAT S610 Introduction to Statistical Computing
STAT S631 Applied Linear Models
DS DMS Requirement
STAT S611 Statistical Computing
STAT S632 Applied Linear Models II
DS DME Requirement
Second
STAT S621 Fundamentals of Statistical Methods and Theory I
STAT S690 Statistical Consulting
DS DVS Requirement
STAT S622 Fundamentals of Statistical Methods and Theory II
STAT S692 Internship in Statistical Consulting
DS Domain 1
Third
DS Domain 2
DS Elective
DS Elective
STAT or DS Elective
STAT or DS Elective
Degree map for students with weak data science background
This path is for students who may need bolster their knowledge in data science.
For example only
Year
Fall
Spring
First
STAT S610 Introduction to Statistical Computing
STAT S631 Applied Linear Models
DSCI-D 590 Intro to Python Programming
STAT S611 Statistical Computing
STAT S632 Applied Linear Models II
DSCI-D 590 Introduction to NLP for DS
Second
STAT S621 Fundamentals of Statistical Methods and Theory I
STAT S690 Statistical Consulting
DS DVS Requirement
STAT S622 Fundamentals of Statistical Methods and Theory II
STAT S692 Internship in Statistical Consulting
DS Domain 1
Third
DS Domain 2
DS Elective
DS Elective
STAT or DS Elective
STAT or DS Elective
Adding data science later
This path is for students who are already in the statistical science program and later decide to add on the M.S. in data science.
An example only
Year
Fall
Spring
First
STAT S610 Introduction to Statistical Computing
STAT S631 Applied Linear Models
STAT S621 Fundamentals of Statistical Methods and Theory I
STAT S611 Statistical Computing
STAT S632 Applied Linear Models II
STAT S622 Fundamentals of Statistical Methods and Theory II
Second
STAT S690 Statistical Consulting
DS DMS Requirement
DS Domain 1
STAT S692 Internship in Statistical Consulting
DS DME Requirement
DS Domain 2
Third
DS DVS Requirement
DS Elective
STAT or DS Elective
DS Elective
STAT or DS Elective
Data Science Program resources and social media channels