How it works 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 : Elements of Artificial Intelligence CSCI-B 555 : Machine Learning CSCI-B 565 : Data Mining CSCI-P 556 : Applied Machine Learning ENGR-E 511 : Machine Learning for Signal Processing ILS-Z 534 : Search INFO-I 606 : Network Science Data Management and Engineering (3 credits)
CSCI-B 561 : Advanced Database Concepts ENGR-E 516 : Engineering Cloud Computing INFO-I 535 : Management, Access, and Use of Big and Complex Data DSCI-D 532 : Applied Database Technologies Data Visualization and Storytelling (3 credits)
ENGR-E 583 : Information Visualization ENGR-E 584 : Scientific Visualization INFO-I 590 : Data Visualization Graduate level Luddy courses (18 credits)
No more than 12 credits from STAT courses At most 3 credits of DSCI-D 590 Data Science On-Ramp All 4 of the following courses (13 credits)
STAT-S 520 : Introduction to Statistics or a more advanced course on statistical theory approved by the DGS STAT-S 631 : Applied Linear Models I STAT-S 632 : Applied Linear Models II STAT-S 690 : Statistical Consulting One of the following courses (3 credits)
STAT-S 610 : Introduction to Statistical Computing STAT-S 611 : Applied Statistical Computing STAT-S 612 : Reproducible Results and the Workflow of Data Analysis Any STAT graduate level course (3 credits)6 credits of one the following domains
Augmented and Virtual Reality INFO-I 590 : Topics in Informatics
Artificial Life in Virtual Reality Building Virtual Worlds Creating Virtual Assets Introduction to Virtual Reality
Data Security and Privacy INFO-I 520 : Security for Networked Systems INFO-I 525 : Organizational Informatics and Economic Security INFO-I 533 : Systems and Protocol Security and Information Assurance INFO-I 538 : Introduction to Cryptography
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 : Introduction to Bioinformatics INFO-I 529 : Machine Learning Bioinformatics
Human Robotic Interation CSCI-B 657 : Computer Vision ENGR-E 599 : Autonomous Robotics INFO-I 513 : Usable Artificial Intelligence INFO-I 527 INFO-I 540 INFO-I 542
Social Data Science ENGR-E 583 : Information Visualization (may be counted only once) ILS-Z 639 : Social Media Mining INFO-I 513 : Usable Artificial Intelligence INFO-I 590 : Data Visualization (may be counted only once) INFO-I 606 : Network Science (may be counted only once) 
