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.