Statistics & Computer Science, BSLAS

for the degree of Bachelor of Science in Liberal Arts & Sciences Major in Statistics & Computer Science

computer science email:undergrad@cs.illinois.edu or

This major is sponsored jointly by the Departments of Statistics and Computer Science. The Statistics and Computer Science major is designed for students who would like a strong foundation in computer science, coupled with significant advanced coursework in statistics.   The major prepares students for professional or graduate work in statistics and computer science, and for applications of computing in which knowledge of statistics is particularly important, such as data mining and machine learning.

for the degree of Bachelor of Science in Liberal Arts & Sciences Major in Statistics & Computer Science

Departmental distinction: To graduate with distinction requires a specified minimum grade point average in all Computer Science, Statistics, and Mathematics courses listed below. A GPA of 3.25 is required for Distinction, 3.5 for High Distinction, and 3.75 for Highest Distinction.

Minimum hours required for graduation: 120 hours

CS 100Freshman Orientation (recommended)0-1
Calculus through MATH 241 - Calculus III 11-12
MATH 415Applied Linear Algebra3
Required Computer Science Foudation:32
Intro to Computer Science
Discrete Structures
Software Design Studio
Data Structures
Computer Architecture
System Programming
Numerical Methods I
Introduction to Algorithms & Models of Computation
Programming Languages & Compilers
Required Probability and Statistics Foundation:10
Statistics and Probability I 1
Statistics and Probability II
Statistical Computing
At least four other statistics, computer science, or mathematics courses, with at least one chosen from each of the following groups:12
Group I: Statistical Methods
Statistical Analysis
Biostatistics
Probability & Statistics for Computer Science
Group II: Mathematical Analysis and Modeling
Fundamental Mathematics
Differential Equations
Elementary Real Analysis
Real Variables
Group III: Computational Application Areas
Statistics Programming Methods
Text Information Systems
Database Systems
Introduction to Data Mining
Machine Learning
Advanced Topics in Stochastic Processes & Applications
Simulation
Group IV: Statistical Analysis and Modeling
Methods of Applied Statistics
Applied Regression and Design
Sampling and Categorical Data