B.S. in Statistics: Machine Learning Track

This track emphasizes algorithmic and theoretical aspects of statistical learning methodologies that are geared towards building predictive and explanatory models for large and complex data. It is recommended for students interested in pursuing graduate programs in statistics, machine learning, or data science, as well as for students interested in learning statistical techniques for industry. 

Notes:  

These requirements went into effect Fall 2020. Requirements from previous years can be found in the General Catalog Archive.  

Preparatory Subject Matter (27 units)

Mathematics

  • MAT 21A Calculus    
  • MAT 21B Calculus    
  • MAT 21C Calculus  
  • MAT 21D Vector Analysis
  • MAT 22A Linear Algebra

Computer Science

  • ECS 32A or 36A Programming
    • Additional coursework in Python is also recommended (eg. ECS 32B).

Statistics

  • STA 13 or 32 or 100 
    • STA 32 or 100 preferred.

Depth Subject Matter (52 units)

Core Coursework

Statistics

  • STA 106 Analysis of Variance
  • STA 108 Regression Analysis
  • STA 131A Intro to Probability Theory
  • STA 131B Intro to Mathematical Statistics
  • STA 131C Intro to Mathematical Statistics
  • STA 141A Fundamentals of Statistical Data Science
  • STA 142A Statistical Learning I
  • STA 142B Statistical Learning II
  • STA 144 Sampling Theory of Surveys or STA 145 Bayesian Statistical Inference

Mathematics

  • MAT 167 Applied Linear Algebra or MAT 168 Optimization

Restricted Electives

Choose three:

  • STA 104 Nonparametric Statistics
  • STA 135 Multivariate Data Analysis
  • STA 137 Applied time Series Analysis
  • STA 138 Analysis of Categorical Data
  • STA 141B Data and Web Technologies for Data Analysis
  • STA 141C Big Data and High Performance Statistical Computing
  • STA 144 Sampling Theory of Surveys
  • STA 145 Bayesian Statistical Inference
  • MAT 127A Real Analysis
  • MAT 128A Numerical Analysis
  • MAT 170 Mathematics for Data Analytics and Decision Making
  • ECS 122A Algorithm Design and Analysis
  • ECS 158 Programming and Parallel Architectures
  • ECS 163 Information Interfaces
  • ECS 165A Database Systems
  • ECS 170 Introduction to Artificial Intelligence
  • ECS 174 Computer Vision
  • One approved course of 4 units from STA 199, 194HA, or 194HB may be used. 

NOTE: A course used to fulfill the core requirement cannot be used as an elective.

Total Units: 79

 

Sample Schedule

This schedule can be used as a guide, but students are recommended to meet with an advisor on a regular basis to make a customized plan that works best for their unique needs and priorities.  

Academic Planning Resources:

FreshmanFallWinter Spring
 MAT 21AMAT 21BMAT 21C
  ECS 32A or 36ASTA 13 or 32 or 100
SophomoreFallWinterSpring
 MAT 21DSTA 108STA 106
 MAT 22AECS 32B*STA 141A
JuniorFallWinterSpring
 STA 131ASTA 131BSTA 131C
 MAT 167 or 168STA 142ASTA 142B
SeniorFallWinterSpring
 STA/MAT/ECS Restricted ElectiveSTA/MAT/ECS Restricted ElectiveSTA 144 or 145
  STA/MAT/ECS Restricted Elective 

* Recommended course (not required).