B.S. in Statistics: Statistical Data Science

This track emphasizes data handling skills and statistical computation. It is recommended for students interested in statistical learning methodology, advanced data handling techniques and computational aspects of statistical analysis.

Notes:  

These requirements were put 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)

Computer Science (4 units)

  • ECS 32A Intro to Programming
    • or ECS 36A Programming & Problem Solving
      • Former courses ECS 10 or 30 or 40 may also be used.
      • 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 
    • or STA 130A Mathematical Statistics: Brief Course
  • STA 131B Intro to Mathematical Statistics 
    • or STA 130B Mathematical Statistics: Brief Course
  • STA 141A Fundamentals of Statistical Data Science
  • STA 141B Data and Web Technologies for Data Analysis
  • STA 141C Big Data and High Performance Statistical Computing
  • STA 135 Multivariate Data Analysis
  • STA 160 Practice in Statistical Data Science

Machine Learning (4 units)

  • STA 142A Statistical Learning I 
    • or STA 142B Statistical Learning II 
    • or ECS 111 Applied Machine Learning for Non-Majors 
    • or ECS 171 Machine Learning

Mathematics (4 units)

Advanced Electives (8 units)

Choose two: 

  • STA 104 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 and Decision Making
  • ECS 116 Databases for Non-Majors
  • 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
  • One approved course of 4 units from STA 199, 194HA, or 194HB may be used. 

Note: A course used to fulfill a core requirement cannot be used as a restricted 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-Statistical Data Science 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-yearFallWinterSpring
 MAT 21AMAT 21BMAT 21C
  ECS 32A or 36ASTA 13 or 32 or 35B* or 100
Second-yearFallWinterSpring
 MAT 21DSTA 108STA 106
 MAT 22A or 27A or 67ECS 32B**STA 141A
Third-yearFallWinterSpring
 STA 131A or 130ASTA 131B or 130BSTA 141C
 MAT 167 or 168STA 141BSTA 135
Fourth-yearFallWinterSpring
 STA/MAT/ECS Advanced ElectiveSTA 142A or STA 142B or ECS 111 or ECS 171STA 160
  STA/MAT/ECS Advanced Elective 

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

** Recommended course (not required).