The ACMS Data Sciences and Statistics option is designed with strong Statistics and Modeling components. The track incorporates coursework in Computation, Statistics and Machine Learning, Databases and Data Visualization, as well as topics related to science and society. This option is unique in its double emphasis on Statistics and Modeling & Scientific Computing. Our graduates will have a unique blend of skills to build models for data, use them efficiently, and interpret them statistically.
Students in the Data Science and Statistics option will substitute Math/Stat 394 for Math/Stat 390.
Option Core (30 credits)
- PHYS 121, 122, 123 (5,5,5)
- AMATH 301: (4) Beginning Scientific Computing or
- STAT 302: (3) Statistical Software and Its Applications
- MATH/STAT 395: (3) Probability II
- STAT 391: (4) Quantitative Introductory Statistics for Data Science
- CSE 414: (4) Introduction to Database Systems
Option Electives (18 credits)
At least 6 credits from:
- STAT 403: (4) Intro to Resampling Inference
- STAT 421: (4) Applied Statistics and Experimental Design
- STAT 423: (4) Applied Regression and Analysis of Variance
- STAT 428: (4) Multivariate Analysis for the Social Sciences
- STAT 435: (4) Introduction to Statistical Machine Learning
- *MATH/STAT 396: (3) Probability III
- *BIOST/STAT 425: (3) Introduction to Nonparametric Statistics
- *STAT 427: (4) Introduction to Analysis of Categorical Data
- *STAT 441: (4) Multivariate Statistical Methods
- *MATH/STAT 491: (3) Introduction to Stochastic Processes
*Students who entered the ACMS program prior to Winter 2020 need to email us at: firstname.lastname@example.org, if you choose to take one (or more) of the * courses above, to have it be counted towards your degree requirements in your DARS after you've registered for the course.
At least 6 credits from:
- AMATH 481: (5) Scientific Computing
- AMATH 482: (5) Computational Methods for Data Analysis
- AMATH 483: (5) High-Performance Scientific Computing
- MATH 464: (3) Numerical Analysis I
- MATH 465: (3) Numerical Analysis II
- MATH 407: (3) Linear Optimization
- MATH 408: (3) Nonlinear Optimization
- MATH 409: (3) Discrete Optimization
- CSE 373: (4) Data Structures and Algorithms
- CSE 415: (3) Introduction to Artificial Intelligence
- CSE 417: (3) Algorithms and Computational Complexity
- CSE 472: (5) Introduction to Computational Linguistics
- HCDE 411: (5) Information Visualization
At least 6 additional credits from approved courses (at the 300 level or higher) in AMATH, CSE, MATH or STAT departments. The courses listed above in Group I are particularly recommended.
Requirements effective Spring Quarter 2016.