Statistics - Minor in Data Analytics

Return To: College of Arts & Sciences: Minor Programs

This minor is for students who are interested in learning from data but may have little or no data analysis background. It requires no prior experience with statistics, computing, or data analysis. Students completing this minor will gain significant experience with working with data to prepare for analysis, drawing conclusions from data, and communicating conclusions.

Requirements for Minor in Data Analytics

The minor in Data Analytics requires four core courses and one restricted elective course. In total the minor in Data Analytics requires 15 credit hours. A grade of C- or higher is required for all minor courses.

Prerequisites

Students must have completed all prerequisite courses to declare the minor. Students may use AP credit to meet the prerequisite requirements.

Introduction to data analysis and computing

Complete one of the following:
STAT 1601 - Introduction to Data Science with R Credits: 3
STAT 1602 - Introduction to Data Science with Python Credits: 3
OR

Complete one of the following:
STAT 1100 - Chance: An Introduction to Statistics Credits: 3
STAT 1120 - Introduction to Statistics Credits: 3
STAT 2020 - Statistics for Biologists Credits: 4
STAT 2120 - Introduction to Statistical Analysis Credits: 4
APMA 3110 - Applied Statistics and Probability Credits: 3
APMA 3120 - Statistics Credits: 3
AND

Complete one of the following:
CS 1110 - Introduction to Programming Credits: 3
CS 1111 - Introduction to Programming Credits: 3
CS 1112 - Introduction to Programming Credits: 3
CS 1113 - Introduction to Programming Credits: 3

Core Courses

Introduction to data analysis and computing

Complete one of the following:
STAT 1601 - Introduction to Data Science with R Credits: 3
STAT 1602 - Introduction to Data Science with Python Credits: 3
OR

Complete one of the following:
STAT 1100 - Chance: An Introduction to Statistics Credits: 3
STAT 1120 - Introduction to Statistics Credits: 3
STAT 2020 - Statistics for Biologists Credits: 4
STAT 2120 - Introduction to Statistical Analysis Credits: 4
APMA 3110 - Applied Statistics and Probability Credits: 3
APMA 3120 - Statistics Credits: 3
AND

Complete one of the following:
CS 1110 - Introduction to Programming Credits: 3
CS 1111 - Introduction to Programming Credits: 3
CS 1112 - Introduction to Programming Credits: 3
CS 1113 - Introduction to Programming Credits: 3
Statistical linear models:

Complete one of the following:
STAT 3220 - Introduction to Regression Analysis Credits: 3
ECON 3720 - Introduction to Econometrics Credits: 4
Data visualization and management

Complete one of the following:
STAT 3280 - Data Visualization and Management Credits: 3
Data Analytics

Complete one of the following:
STAT 4220 - Applied Analytics for Business Credits: 3
STAT 4630 - Statistical Machine Learning Credits: 3

Complete One Restricted Elective:

STAT 3080 - From Data to Knowledge Credits: 3
STAT 3120 - Introduction to Mathematical Statistics Credits: 3
STAT 3130 - Design and Analysis of Sample Surveys Credits: 3
STAT 3250 - Data Analysis with Python Credits: 3
STAT 3480 - Nonparametric and Rank-Based Statistics Credits: 3
STAT 4120 - Applied Linear Models Credits: 3
STAT 4130 - Applied Multivariate Statistics Credits: 3
STAT 4160 - Experimental Design Credits: 3
STAT 4170 - Financial Time Series and Forecasting Credits: 3
STAT 4220 - Applied Analytics for Business Credits: 3
STAT 4630 - Statistical Machine Learning Credits: 3
STAT 4800 - Advanced Sports Analytics I Credits: 3
ASTR 4140 - Research Methods in Astrophysics Credits: 3
ECON 4720 - Econometric Methods Credits: 3