# Statistics, Bachelor of Science College of Letters & Science

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

Code | Title | Units |
---|---|---|

Preparatory Subject Matter | ||

Mathematics | ||

Choose a series: | 9-12 | |

Short Calculus and Short Calculus and Short Calculus | ||

Calculus for Biology & Medicine and Calculus for Biology & Medicine and Calculus for Biology & Medicine | ||

Calculus for Data-Driven Applications and Calculus for Data-Driven Applications and Calculus for Data-Driven Applications | ||

Calculus and Calculus and Calculus | ||

MAT 021 series preferred. | ||

MAT 022A | Linear Algebra | 3 |

Computer Science Engineering | ||

ECS 032A | Introduction to Programming | 4 |

or ECS 036A | Programming & Problem Solving | |

Statistics | ||

Choose one: | 4 | |

Elementary Statistics | ||

or STA 013Y | Elementary Statistics | |

Gateway to Statistical Data Science | ||

Applied Statistics for Biological Sciences | ||

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 Subtotal | 27-31 | |

Depth Subject Matter | ||

Core Coursework | ||

Statistics | 24 | |

Applied Statistical Methods: Analysis of Variance | ||

Applied Statistical Methods: Regression Analysis | ||

Mathematical Statistics: Brief Course | ||

Mathematical Statistics: Brief Course | ||

Analysis of Categorical Data | ||

Fundamentals of Statistical Data Science | ||

Restricted Electives | ||

Choose three: | 12 | |

Applied Statistical Methods: Nonparametric Statistics | ||

Multivariate Data Analysis | ||

Applied Time Series Analysis | ||

Data & Web Technologies for Data Analysis | ||

Big Data & High Performance Statistical Computing | ||

Sampling Theory of Surveys | ||

Bayesian Statistical Inference | ||

Practice in Statistical Data Science | ||

Optimization | ||

Special Studies for Honors Students | ||

Special Studies for Honors Students | ||

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. | ||

Depth Subject Matter Subtotal | 48-52 | |

Total Units | 75-83 |

## Computational Statistics Track

Code | Title | Units |
---|---|---|

Preparatory Subject Matter | ||

Mathematics | ||

MAT 021A | Calculus | 4 |

MAT 021B | Calculus | 4 |

MAT 021C | Calculus | 4 |

MAT 021D | Vector Analysis | 4 |

MAT 022A | Linear Algebra | 3 |

Computer Science Engineering | ||

Choose one: | 4-5 | |

Software Development in UNIX & C++ | ||

Data Structures, Algorithms, & Programming | ||

Or the equivalent. | ||

Statistics | ||

Choose one: | 4 | |

Elementary Statistics | ||

or STA 013Y | Elementary Statistics | |

Gateway to Statistical Data Science | ||

Applied Statistics for Biological Sciences | ||

Preparatory Subject Matter Subtotal | 27-28 | |

Depth Subject Matter | ||

Statistics | ||

STA 106 | Applied Statistical Methods: Analysis of Variance | 4 |

STA 108 | Applied Statistical Methods: Regression Analysis | 4 |

STA 131A | Introduction to Probability Theory | 4 |

STA 131B | Introduction to Mathematical Statistics | 4 |

STA 141A | Fundamentals of Statistical Data Science | 4 |

Choose two: | 8 | |

Applied Statistical Methods: Nonparametric Statistics | ||

Multivariate Data Analysis | ||

Applied Time Series Analysis | ||

Analysis of Categorical Data | ||

Statistical Learning I | ||

Statistical Learning II | ||

Sampling Theory of Surveys | ||

Bayesian Statistical Inference | ||

Practice in Statistical Data Science | ||

Special Studies for Honors Students | ||

Special Studies for Honors Students | ||

Special Study for Advanced Undergraduates | ||

Programming, Data Management & Data Tehnologies | ||

ECS 130 | Scientific Computation | 4 |

or ECS 145 | Scripting Languages & Their Applications | |

ECS 165A | Database Systems | 4 |

Scientific Computational Algorithm & Visualization | ||

Choose two: | 8 | |

Algorithm Design & Analysis | ||

Computational Structural Bioinformatics | ||

Programming Languages | ||

Programming on Parallel Architectures | ||

Information Interfaces | ||

Data & Web Technologies for Data Analysis | ||

Big Data & High Performance Statistical Computing | ||

Mathematics | ||

Choose two: | 8 | |

Mathematical Biology | ||

Numerical Analysis | ||

Numerical Analysis in Solution of Equations | ||

Fourier Analysis | ||

Combinatorics | ||

Discrete Mathematics | ||

Mathematics for Data Analytics & Decision Making | ||

Mathematics & Computers | ||

Applied Linear Algebra | ||

Optimization | ||

Depth Subject Matter Subtotal | 52 | |

Total Units | 79-80 |

## General Statistics Track

Code | Title | Units |
---|---|---|

Preparatory Subject Matter | ||

Mathematics | ||

MAT 021A | Calculus | 4 |

MAT 021B | Calculus | 4 |

MAT 021C | Calculus | 4 |

MAT 021D | Vector Analysis | 4 |

MAT 022A | Linear Algebra | 3-4 |

or MAT 067 | Modern Linear Algebra | |

Computer Science Engineering | ||

ECS 032A | Introduction to Programming | 4 |

or ECS 036A | Programming & Problem Solving | |

Statistics | ||

Choose one: | 4 | |

Elementary Statistics | ||

or STA 013Y | Elementary Statistics | |

Gateway to Statistical Data Science | ||

Applied Statistics for Biological Sciences | ||

Preparatory Subject Matter Subtotal | 27-28 | |

Depth Subject Matter | ||

Core Coursework | ||

Statistics | 24 | |

Applied Statistical Methods: Analysis of Variance | ||

Applied Statistical Methods: Regression Analysis | ||

Introduction to Probability Theory | ||

Introduction to Mathematical Statistics | ||

Introduction to Mathematical Statistics | ||

Analysis of Categorical Data | ||

Mathematics | 16 | |

Introduction to Abstract Mathematics | ||

or MAT 127C | Real Analysis | |

Real Analysis | ||

Real Analysis | ||

Applied Linear Algebra | ||

Restricted Electives | ||

Choose three: | 12 | |

Applied Statistical Methods: Nonparametric Statistics | ||

Multivariate Data Analysis | ||

Applied Time Series Analysis | ||

Fundamentals of Statistical Data Science | ||

Data & Web Technologies for Data Analysis | ||

Big Data & High Performance Statistical Computing | ||

Statistical Learning I | ||

Statistical Learning II | ||

Sampling Theory of Surveys | ||

Bayesian Statistical Inference | ||

Practice in Statistical Data Science | ||

Optimization | ||

Special Studies for Honors Students | ||

Special Studies for Honors Students | ||

Special Study for Advanced Undergraduates | ||

Related Elective Course | 3-4 | |

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. | ||

Depth Subject Matter Subtotal | 55-56 | |

Total Units | 82-84 |

## Machine Learning Track

Code | Title | Units |
---|---|---|

Preparatory Subject Matter | ||

Mathematics | ||

MAT 021A | Calculus | 4 |

MAT 021B | Calculus | 4 |

MAT 021C | Calculus | 4 |

MAT 021D | Vector Analysis | 4 |

MAT 022A | Linear Algebra | 3 |

Computer Science Engineering | ||

ECS 032A | Introduction to Programming | 4 |

or ECS 036A | Programming & Problem Solving | |

Note: Additional coursework in Python is strongly recommended; e.g., ECS 032B. | ||

Statistics | ||

Choose one: | 4 | |

Elementary Statistics | ||

or STA 013Y | Elementary Statistics | |

Gateway to Statistical Data Science | ||

Applied Statistics for Biological Sciences | ||

Preparatory Subject Matter Subtotal | 27 | |

Depth Subject Matter | ||

Core Coursework | ||

Statistics | 36 | |

Applied Statistical Methods: Analysis of Variance | ||

Applied Statistical Methods: Regression Analysis | ||

Introduction to Probability Theory | ||

Introduction to Mathematical Statistics | ||

Introduction to Mathematical Statistics | ||

Fundamentals of Statistical Data Science | ||

Statistical Learning I | ||

Statistical Learning II | ||

Sampling Theory of Surveys | ||

or STA 145 | Bayesian Statistical Inference | |

Mathematics | 4 | |

Applied Linear Algebra | ||

or MAT 168 | Optimization | |

Restricted Electives | ||

Choose three: | 12 | |

Applied Statistical Methods: Nonparametric Statistics | ||

Multivariate Data Analysis | ||

Applied Time Series Analysis | ||

Analysis of Categorical Data | ||

Data & Web Technologies for Data Analysis | ||

Big Data & High Performance Statistical Computing | ||

Sampling Theory of Surveys | ||

Bayesian Statistical Inference | ||

Real Analysis | ||

Numerical Analysis | ||

Mathematics for Data Analytics & Decision Making | ||

Algorithm Design & Analysis | ||

Programming on Parallel Architectures | ||

Information Interfaces | ||

Database Systems | ||

Introduction to Artificial Intelligence | ||

Computer Vision | ||

Special Studies for Honors Students | ||

Special Studies for Honors Students | ||

Special Study for Advanced Undergraduates | ||

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

Depth Subject Matter Subtotal | 52 | |

Total Units | 79 |

## Statistical Data Science Track

Code | Title | Units |
---|---|---|

Preparatory Subject Matter | ||

Mathematics | ||

MAT 021A | Calculus | 4 |

MAT 021B | Calculus | 4 |

MAT 021C | Calculus | 4 |

MAT 021D | Vector Analysis | 4 |

MAT 022A | Linear Algebra | 3 |

Computer Science Engineering | ||

ECS 032A | Introduction to Programming | 4 |

or ECS 036A | Programming & Problem Solving | |

Note: Additional coursework in Python is strongly recommended; e.g., ECS 032B. | ||

Statistics | ||

Choose one: | 4 | |

Elementary Statistics | ||

or STA 013Y | Elementary Statistics | |

Gateway to Statistical Data Science | ||

Applied Statistics for Biological Sciences | ||

Preparatory Subject Matter Subtotal | 27 | |

Depth Subject Matter | ||

Core Coursework | ||

Statistics | 36 | |

Applied Statistical Methods: Analysis of Variance | ||

Applied Statistical Methods: Regression Analysis | ||

Introduction to Probability Theory | ||

or STA 130A | Mathematical Statistics: Brief Course | |

Introduction to Mathematical Statistics | ||

or STA 130B | Mathematical Statistics: Brief Course | |

Multivariate Data Analysis | ||

Fundamentals of Statistical Data Science | ||

Data & Web Technologies for Data Analysis | ||

Big Data & High Performance Statistical Computing | ||

Practice in Statistical Data Science | ||

Machine Learning | 4 | |

Statistical Learning I | ||

or ECS 171 | Machine Learning | |

Mathematics | 4 | |

Applied Linear Algebra | ||

or MAT 168 | Optimization | |

Restricted Electives | ||

Choose two: | 8 | |

Applied Statistical Methods: Nonparametric Statistics | ||

Applied Time Series Analysis | ||

Analysis of Categorical Data | ||

Statistical Learning I | ||

Statistical Learning II | ||

Sampling Theory of Surveys | ||

Bayesian Statistical Inference | ||

Numerical Analysis | ||

Mathematics for Data Analytics & Decision Making | ||

Algorithm Design & Analysis | ||

Programming on Parallel Architectures | ||

Information Interfaces | ||

Database Systems | ||

Special Studies for Honors Students | ||

Special Studies for Honors Students | ||

Special Study for Advanced Undergraduates | ||

Note: A course used to fulfill a core requirement cannot be used as a restricted elective. | ||

Depth Subject Matter Subtotal | 52 | |

Total Units | 79 |