Statistics, Bachelor of Science College of Letters & Science

Office & Contact Information

Statistics enables us to make inferences about entire populations, based on samples extracted from those populations. Statistical methods can be applied to problems from almost every discipline and they are vitally important to researchers in agricultural, biological, environmental, social, engineering, and medical sciences.

The Program

Statistics majors may receive either a Bachelor of Arts or a Bachelor of Science degree. Both the A.B. and the B.S. programs require theoretical and applied course work and underscore the strong interdependence of statistical theory and the applications and computational aspects of statistics. The B.S. degree program has five tracks: Applied Statistics Track, Computational Statistics Track, General Track, Machine Learning Track, and the Statistical Data Science Track.

B.S. in Statistics-Applied Statistics Track emphasizes statistical applications. This track is recommended for students who are interested in applications of statistical techniques to various disciplines including the biological, physical and social sciences.

B.S. in Statistic-Computational Statistics Track emphasizes computing. This track is recommended for students interested in the computational and data management aspects of statistical analysis.

B.S. in Statistics-General Track emphasizes statistical theory and is especially recommended as preparation for graduate study in statistics.

B.S in Statistics-Machine Learning 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.

B.S. in Statistic-Statistical Data Science Track emphasizes data handling skills and statistical computation. This track is recommended for students interested in statistical learning methodology, advanced data handling techniques and computational aspects of statistical analysis.

Major Advisors

For a current list of faculty and staff advisors, see Undergraduate Advising.

Students are encouraged to meet with an advisor to plan a program as early as possible.

Career Alternatives

Probability models, statistical methods, and computational techniques are used in a great many fields, including the biological, physical, social, and health sciences, business, and engineering. The wide applicability of statistics is reflected in the strong demand for graduates with statistical training in both the public and private sectors. Employment opportunities include careers in data & policy analysis in government & industry, financial management, quality control, insurance & healthcare industry, actuarial science, engineering, public health, biological & pharmaceutical research, law, and education. Students with an undergraduate degree in statistics have entered advanced studies in statistics, economics, finance, psychology, medicine, business management & analytics, and other professional school programs.

The major requirements below are in addition to meeting University Degree Requirements & College Degree Requirements; unless otherwise noted. Respective of the Track, the minimum number of units required for the Statistics Bachelor of Science are 75, 79, 82, 79, & 79.

Applied Statistics Track

Preparatory Subject Matter
Mathematics
Choose a series:9-12
MAT 016A
& MAT 016B
& MAT 016C
Short Calculus
and Short Calculus
and Short Calculus
MAT 017A
& MAT 017B
& MAT 017C
Calculus for Biology & Medicine
and Calculus for Biology & Medicine
and Calculus for Biology & Medicine
MAT 019A
& MAT 019B
& MAT 019C
Calculus for Data-Driven Applications
and Calculus for Data-Driven Applications
and Calculus for Data-Driven Applications
MAT 021A
& MAT 021B
& MAT 021C
Calculus
and Calculus
and Calculus
MAT 021 series preferred.
MAT 022ALinear Algebra3
Computer Science Engineering
ECS 032AIntroduction to Programming4
or ECS 032AV Introduction to Programming
or ECS 036A Programming & Problem Solving
Statistics
Choose one:4
STA 013
Elementary Statistics
or STA 013Y
 Elementary Statistics
STA 032
Gateway to Statistical Data Science
STA 100
Applied Statistics for Biological Sciences
STA 032 or STA 100 preferred.
Cluster Elective Prerequisites
Two introductory courses serving as the prerequisites to the chosen Cluster Electives (see Cluster Electives section below). 7-8
Note: Additional coursework beyond this requirement may be needed to fulfill the Cluster Elective prerequisites.
Preparatory Subject Matter Subtotal27-31
Depth Subject Matter
Core Coursework
Statistics24
STA 106
Applied Statistical Methods: Analysis of Variance
STA 108
Applied Statistical Methods: Regression Analysis
STA 130A
Mathematical Statistics: Brief Course
STA 130B
Mathematical Statistics: Brief Course
STA 138
Analysis of Categorical Data
STA 141A
Fundamentals of Statistical Data Science
Restricted Electives
Choose three: 12
STA 104
Applied Statistical Methods: Nonparametric Statistics
STA 135
Multivariate Data Analysis
STA 137
Applied Time Series Analysis
STA 141B
Data & Web Technologies for Data Analysis
Only one of STA 141B or STA 141C can be used as an elective.
STA 141C
Big Data & High Performance Statistical Computing
Only one of STA 141B or STA 141C can be used as an elective.
STA 144
Sampling Theory of Surveys
STA 145
Bayesian Statistical Inference
STA 160
Practice in Statistical Data Science
MAT 168
Optimization
With advisor approval, one of STA 194HA or STA 194HB or STA 199 may be used as an elective. The course must be taken for four units.
STA 194HA
Special Studies for Honors Students
STA 194HB
Special Studies for Honors Students
STA 199
Special Study for Advanced Undergraduates
Cluster Electives
Choose four upper division elective courses outside of statistics:12-16
Cluster electives are chosen with and must be approved by the major advisor. Electives must follow a coherent sequence in one single disciple/cluster where statistical methods and models are applied. At least three of the cluster electives must cover the quantitative aspects of the discipline. A list of pre-approved electives can be found on the Statistics Department website.
Pre-Approved Electives List
Depth Subject Matter Subtotal48-52
Total Units75-83

Computational Statistics Track

Preparatory Subject Matter
Mathematics
MAT 021ACalculus4
MAT 021BCalculus4
MAT 021CCalculus4
MAT 021DVector Analysis4
MAT 022ALinear Algebra3
Computer Science Engineering
Choose one:4-5
ECS 034
Software Development in UNIX & C++
ECS 036C
Data Structures, Algorithms, & Programming
Or the equivalent.
Statistics
Choose one:4
STA 013
Elementary Statistics
or STA 013Y
 Elementary Statistics
STA 032
Gateway to Statistical Data Science
STA 100
Applied Statistics for Biological Sciences
STA 032 or STA 100 preferred.
Preparatory Subject Matter Subtotal27-28
Depth Subject Matter
Statistics
STA 106Applied Statistical Methods: Analysis of Variance4
STA 108Applied Statistical Methods: Regression Analysis4
STA 131AIntroduction to Probability Theory4
STA 131BIntroduction to Mathematical Statistics4
STA 141AFundamentals of Statistical Data Science4
Choose two:8
STA 104
Applied Statistical Methods: Nonparametric Statistics
STA 135
Multivariate Data Analysis
STA 137
Applied Time Series Analysis
STA 138
Analysis of Categorical Data
STA 142A
Statistical Learning I
STA 142B
Statistical Learning II
STA 144
Sampling Theory of Surveys
STA 145
Bayesian Statistical Inference
STA 160
Practice in Statistical Data Science
With advisor approval, one of STA 194HA or STA 194HB or STA 199 may be used as an elective. The course must be taken for four units.
STA 194HA
Special Studies for Honors Students
STA 194HB
Special Studies for Honors Students
STA 199
Special Study for Advanced Undergraduates
Programming, Data Management & Data Tehnologies
ECS 130Scientific Computation4
or ECS 145 Scripting Languages & Their Applications
ECS 165ADatabase Systems4
Scientific Computational Algorithm & Visualization
Choose two:8
ECS 122A
Algorithm Design & Analysis
ECS 129
Computational Structural Bioinformatics
ECS 140A
Programming Languages
ECS 158
Programming on Parallel Architectures
ECS 163
Information Interfaces
STA 141B
Data & Web Technologies for Data Analysis
STA 141C
Big Data & High Performance Statistical Computing
Mathematics
Choose two:8
MAT 124
Mathematical Biology
MAT 128A
Numerical Analysis
MAT 128B
Numerical Analysis in Solution of Equations
MAT 129
Fourier Analysis
MAT 145
Combinatorics
MAT 148
Discrete Mathematics
MAT 170
Mathematics for Data Analytics & Decision Making
MAT 165
Mathematics & Computers
MAT 167
Applied Linear Algebra
MAT 168
Optimization
Depth Subject Matter Subtotal52
Total Units79-80

General Statistics Track

Preparatory Subject Matter
Mathematics
MAT 021ACalculus4
MAT 021BCalculus4
MAT 021CCalculus4
MAT 021DVector Analysis4
MAT 022ALinear Algebra3-4
or MAT 067 Modern Linear Algebra
Computer Science Engineering
ECS 032AIntroduction to Programming4
or ECS 032AV Introduction to Programming
or ECS 036A Programming & Problem Solving
Statistics
Choose one:4
STA 013
Elementary Statistics
or STA 013Y
 Elementary Statistics
STA 032
Gateway to Statistical Data Science
STA 100
Applied Statistics for Biological Sciences
STA 032 or STA 100 preferred.
Preparatory Subject Matter Subtotal27-28
Depth Subject Matter
Core Coursework
Statistics24
STA 106
Applied Statistical Methods: Analysis of Variance
STA 108
Applied Statistical Methods: Regression Analysis
STA 131A
Introduction to Probability Theory
STA 131B
Introduction to Mathematical Statistics
STA 131C
Introduction to Mathematical Statistics
STA 138
Analysis of Categorical Data
Mathematics16
MAT 108
Introduction to Abstract Mathematics
or MAT 127C
 Real Analysis
MAT 127A
Real Analysis
MAT 127B
Real Analysis
MAT 167
Applied Linear Algebra
Restricted Electives
Choose three:12
STA 104
Applied Statistical Methods: Nonparametric Statistics
STA 135
Multivariate Data Analysis
STA 137
Applied Time Series Analysis
STA 141A
Fundamentals of Statistical Data Science
STA 141B
Data & Web Technologies for Data Analysis
Only one of STA 141B or STA 141C can be used as an elective.
STA 141C
Big Data & High Performance Statistical Computing
Only one of STA 141B or STA 141C can be used as an elective.
STA 142A
Statistical Learning I
STA 142B
Statistical Learning II
STA 144
Sampling Theory of Surveys
STA 145
Bayesian Statistical Inference
STA 160
Practice in Statistical Data Science
MAT 168
Optimization
With advisor approval, one of STA 194HA or STA 194HB or STA 199 may be used as an elective. The course must be taken for four units.
STA 194HA
Special Studies for Honors Students
STA 194HB
Special Studies for Honors Students
STA 199
Special Study for Advanced Undergraduates
Related Elective Course3-4
One upper division course outside of Statistics approved by major advisor. The Related Elective should be in mathematics, computer science or cover quantitative aspects of a substantive discipline. A list of pre-approved electives can be found on the Statistics Department website.
Pre-Approved Electives List
Depth Subject Matter Subtotal55-56
Total Units82-84

Machine Learning Track

Preparatory Subject Matter
Mathematics
MAT 021ACalculus4
MAT 021BCalculus4
MAT 021CCalculus4
MAT 021DVector Analysis4
MAT 022ALinear Algebra3
Computer Science Engineering
ECS 032AIntroduction to Programming4
or ECS 032AV Introduction to Programming
or ECS 036A Programming & Problem Solving
Note: Additional coursework in Python is strongly recommended; e.g., ECS 032B.
Statistics
Choose one:4
STA 013
Elementary Statistics
or STA 013Y
 Elementary Statistics
STA 032
Gateway to Statistical Data Science
STA 100
Applied Statistics for Biological Sciences
STA 032 or STA 100 preferred.
Preparatory Subject Matter Subtotal27
Depth Subject Matter
Core Coursework
Statistics36
STA 106
Applied Statistical Methods: Analysis of Variance
STA 108
Applied Statistical Methods: Regression Analysis
STA 131A
Introduction to Probability Theory
STA 131B
Introduction to Mathematical Statistics
STA 131C
Introduction 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
Mathematics4
MAT 167
Applied Linear Algebra
or MAT 168
 Optimization
Restricted Electives
Choose three:12
STA 104
Applied Statistical Methods: Nonparametric Statistics
STA 135
Multivariate Data Analysis
STA 137
Applied Time Series Analysis
STA 138
Analysis of Categorical Data
STA 141B
Data & Web Technologies for Data Analysis
STA 141C
Big Data & 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 & Decision Making
ECS 122A
Algorithm Design & Analysis
ECS 158
Programming on Parallel Architectures
ECS 163
Information Interfaces
ECS 165A
Database Systems
ECS 170
Introduction to Artificial Intelligence
ECS 174
Computer Vision
With advisor approval, one of STA 194HA or STA 194HB or STA 199 may be used as an elective. The course must be taken for four units.
STA 194HA
Special Studies for Honors Students
STA 194HB
Special Studies for Honors Students
STA 199
Special Study for Advanced Undergraduates
Note: A course used to fulfill the core requirement cannot be used as an elective.
Depth Subject Matter Subtotal52
Total Units79

Statistical Data Science Track

Preparatory Subject Matter
Mathematics
MAT 021ACalculus4
MAT 021BCalculus4
MAT 021CCalculus4
MAT 021DVector Analysis4
MAT 022ALinear Algebra3
Computer Science Engineering
ECS 032AIntroduction to Programming4
or ECS 032AV Introduction to Programming
or ECS 036A Programming & Problem Solving
Note: Additional coursework in Python is strongly recommended; e.g., ECS 032B.
Statistics
Choose one:4
STA 013
Elementary Statistics
or STA 013Y
 Elementary Statistics
STA 032
Gateway to Statistical Data Science
STA 100
Applied Statistics for Biological Sciences
STA 032 or STA 100 preferred.
Preparatory Subject Matter Subtotal27
Depth Subject Matter
Core Coursework
Statistics36
STA 106
Applied Statistical Methods: Analysis of Variance
STA 108
Applied Statistical Methods: Regression Analysis
STA 131A
Introduction to Probability Theory
or STA 130A
 Mathematical Statistics: Brief Course
STA 131B
Introduction to Mathematical Statistics
or STA 130B
 Mathematical Statistics: Brief Course
STA 135
Multivariate Data Analysis
STA 141A
Fundamentals of Statistical Data Science
STA 141B
Data & Web Technologies for Data Analysis
STA 141C
Big Data & High Performance Statistical Computing
STA 160
Practice in Statistical Data Science
Machine Learning4
STA 142A
Statistical Learning I
or ECS 171
 Machine Learning
Mathematics4
MAT 167
Applied Linear Algebra
or MAT 168
 Optimization
Restricted Electives
Choose two:8
STA 104
Applied Statistical Methods: Nonparametric Statistics
STA 137
Applied Time Series Analysis
STA 138
Analysis of Categorical Data
STA 142A
Statistical Learning I
STA 142B
Statistical Learning II
STA 144
Sampling Theory of Surveys
STA 145
Bayesian Statistical Inference
MAT 128A
Numerical Analysis
MAT 170
Mathematics for Data Analytics & Decision Making
ECS 122A
Algorithm Design & Analysis
ECS 158
Programming on Parallel Architectures
ECS 163
Information Interfaces
ECS 165A
Database Systems
With advisor approval, one of STA 194HA or STA 194HB or STA 199 may be used as an elective. The course must be taken for four units.
STA 194HA
Special Studies for Honors Students
STA 194HB
Special Studies for Honors Students
STA 199
Special Study for Advanced Undergraduates
Note: A course used to fulfill a core requirement cannot be used as a restricted elective.
Depth Subject Matter Subtotal52
Total Units79