MS Application Area Courses
Business and Social Science*
- CIS 417: Introduction to Business Analytics*
- CIS 418: Advanced Business Modeling and Analytics*
- CIS 432: Predictive Analytics/Python*
- CIS 434: Social Media Analytics*
- CIS 442F: Big Data*
- FIN 418: Quantitative Finance w/ Python*
- MKT 412: Marketing Research*
- MKT 436R: Marketing Analytics using R*
- MKT 440: Pricing Analytics*
- MKT 437: Digital Marketing Strategy*
- MKT 451: Consumer and Brand Research*
- PSCI 404: Probability and Inference (fall)
- PSCI 405: Linear Models (spring)
- PSCI 504: Causal Inference (spring)
- PSCI 505: Maximum Likelihood Estimation (fall)
Computational Methods
- DSCC 401: Tools for Data Science (fall/spring)
- DSCC 402: Data Science at Scale (spring)
- DSCC 475: Time Series Analysis and Forecasting in Data Science (fall)
- CSC 412: Human Computer Interaction (spring)
- CSC 442: Artificial Intelligence (fall)
- CSC 444: Machine Reasoning (fall)
- CSC 445: Deep Learning (fall)
- CSC 446: Machine Learning (fall/spring)
- CSC 447: Natural Language Processing (spring)
- CSC 448: Statistical Speech and Language Processing (not offered in 2023-24)
- CSC 449: Machine Vision (spring)
- CSC 452: Computer Organization (fall/spring)
- CSC 458: Parallel and Distributed Systems (spring)
- CSC 460: Technology and Climate Change (spring)
- CSC 463: Data Management Systems (spring)
- CSC 464: Computer Audition (fall)
- CSC 466: Frontiers in Deep Learning (spring)
- CSC 477: End-To-End Deep Learning (fall)
- CSC 482: Design and Analysis of Efficient Algorithms (fall/spring)
- CSC 486: Computational Complexity (spring)
- CSC 489: Algorithmic Game Theory (spring)
- CSC 576: Advanced Topics in Data Management
- CSC 577: Advanced Topics in Computer Vision
- CSC 592: Mobile Visual Computing
- CSSP 519: General Linear Approaches to Data Analysis II (spring)
- BST 421W/STAT 221W: Sampling Techniques (fall)
- ECE 410: Introduction to Augmented and Virtual Reality (fall)
- ECE 411: Selected Topics in Augmented and Virtual Reality (spring)
- ECE 417: Introduction to Dip Using Python
- ECE 477/CSC 464: Computer Audition (fall)
- EESC 410: Stochastic Inverse Modeling in Geophysics (spring)
- EESC 414: Earth Science Data Analysis (fall)
- EESC 421: Quantitative Environmental Problem Solving
- LING 424: Intro to Computational Liguistics
- LING 450: Data Sciences for Linguistics
- LING 470: Tools for Language Documentation
- LING 481: Statistical and Neural Methods for Computational Linguistics (spring)
- PHYS 573: Physics and Finance (fall)
Genomics
- BST 434: Genomic Data Analysis
- BIOL 453: Computational Biology
- BIOL 457: Applied Genomics
- IND 501: SMD Research Ethics
Health and Biomedical Sciences
- BIOL 453: Computational Biology (spring)
- BIOL 457L: Applied Genomics with Lab (fall)
- BST 432: High Dimensional Data Analysis (fall)
- BST 433: Introduction to Computational Systems Biology
- BST 434: Genomic Data Analysis (spring)
- BST 467: Applied Statistics in the Biomedical Sciences (spring)
- BCSC 547: Introduction to Computational Neurosciences (every other spring)
- BCSC 512: Computational Methods in Cog Sci (every other fall)
- BCSC 513: Introduction to fMRI (not offered 2021)
- CSPS 504/BCSC 510: Data Analysis I
- PM 410: Introduction to Data Management/Analysis (fall/spring)
- PM 416: Epidemiologic Methods (spring)
- PM 421: US Health Care System (fall)
- PM 422: Quality of Care and Risk Adjustment (spring)
Statistical Methodology
- STAT 416: Applied Statistical Methods-I (fall)
- STAT 417: Applied Stat Methods II (spring)
- STAT 418: Categorical Data Analysis (fall)
- STAT 419: Nonparametric Inference (fall)
- STAT 423: Bayesian Inference (spring)
- STAT 476: Statistical Inference in R (spring)
- STAT 477: Introduction to Statistical Software (fall)
- ECE 440: Introduction to Random Processes (fall)
- ECE 441: Detection Estimation Theory
- ECE 442: Network Science Analytics (spring)
- ECE 443: Probabilistic Models for Inference Estimation
- PHYS 403: Data Science I: Modern Statistics and Exploration of Large Data Sets (spring)
- PHYS 525: Data Science II: Complexity and Network Theory (fall)
* Courses in the business and social science application area that are housed in the Simon Business School do not run on the full semester system and are offered at a different credit hour rate than of Arts, Sciences & Engineering courses.