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 (28-32 units)

Mathematics (20 units)

MAT 21A Calculus
MAT 21B Calculus
MAT 21C Calculus
MAT 21D Vector Analysis
MAT 22A Linear Algebra
- or MAT 67 Modern Linear Algebra
- or MAT/BIS 27A Linear Algebra with Applications to Biology

Computer Science (4 units)

ECS 32A Intro to Programming
- or ECS 36A Programming & Problem Solving
  - Additional coursework in Python is also recommended (eg. ECS 32B).

Statistics (4-8 units)

STA 32 Gateway to Statistical Data Science
- or STA 35A and STA 35B Statistical Data Science
- or STA 100 Applied Statistics for Biological Sciences
- or STA 13 Elementary Statistics (STA 13 NOT recommended)

Depth Subject Matter (52 units)

Core Coursework

Statistics (36 units)

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 (4 units)

MAT 167 Applied Linear Algebra
- or MAT 168 Optimization

Advanced Electives (12 units)

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 116 Databases for Non-Majors
- or ECS 165A Database Systems
ECS 117 Algorithms for Data Science
- or ECS 122A Algorithm Design and Analysis
ECS 119 Data Processing Pipelines
ECS 158 Programming and Parallel Architectures
ECS 163 Information Visualization
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: 80-84

Major GPA Requirements

Minimum 2.0 GPA in UC Davis courses used in the major.
Minimum 2.0 GPA in Upper Division UC Davis courses used in the major.

Statistics-Machine Learning Track Sample Academic Plan

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. Course offerings may also change year to year so please be sure to utilize the Academic Planning Resources provided.

Academic Planning Resources:

First-year	Fall	Winter	Spring
	MAT 21A	MAT 21B	MAT 21C
		ECS 32A or 36A	STA 13 or 32 or 35B* or 100
Second-year	Fall	Winter	Spring
	MAT 21D	STA 108	STA 106
	MAT 22A or 27A or 67	ECS 32B**	STA 141A
Third-year	Fall	Winter	Spring
	STA 131A	STA 131B	STA 131C
	MAT 167 or 168	STA 142A	STA 142B
Fourth-year	Fall	Winter	Spring
	STA/MAT/ECS Advanced Elective	STA/MAT/ECS Advanced Elective	STA 144 or 145***
		STA/MAT/ECS Advanced Elective

* STA 35A must be taken prior to STA 35B.

** Recommended course (not required).

** The quarters in which STA 144 and STA 145 are offered vary year to year so be sure to check the Statistics Course Offering Schedule.

Degree Reminders (BS)

College and University Requirements
In addition to your major requirements, there are college and university requirements that need to be completed to be awarded a degree. You can find a list of the these requirements on the Letters and Science website. You can find a self-check form here.
Area Breadth Requirement (90 Units in Natural Science and Mathematics)
If you receive a Bachelor of Science in the College of Letters and Science you have an area breadth requirement. You are required to take 90 units in Natural Science and Mathematics. You can find out more about this requirement and view a list of approved courses and restrictions on the Letters and Science website. Please consult an advisor in the Letters and Science Dean's Office for more information about this requirement.
Double Major Unit Overlap Rules
In the College of Letters and Science at least 80 percent of the upper division units used to satisfy course and unit requirements in each major selected must be unique and may not be counted toward the upper division unit requirements of any other major undertaken. If the major programs differ in the number of upper division units required, the major program requiring the smaller number of units will be used to compute the minimum number of units that must be unique.
Major-Minor Course Overlap Rules
No more than one course applied to the satisfaction of requirements in the major program shall be accepted in satisfaction of the requirements of a minor.