Data Science Minor
Data science is an interdisciplinary field that combines computer science, statistics, and business intelligence. A Data Science minor gives students data handling, modeling, and interpretation skills.
Data science partners with fields such as Accounting, Banking and Finance, Biology, Computer Science, Criminology, Economics, Health Care, Government, Management, Manufacturing, Mathematical Sciences, Media, Advertising, and Marketing, Physics and Astronomy, Psychology, Retail, Sports, and Tech Startups.
Required Courses (16 hours)
MATH 1530 – Applied Statistics
3 credit hours
Prerequisites: Two years of high school algebra and a Math Enhanced ACT 19 or greater or equivalent. Descriptive statistics, probability, and statistical inference. The inference unit covers means, proportions, and variances for one and two samples, and topics from one-way ANOVA, regression and correlation analysis, chi-square analysis, and nonparametrics. TBR Common Course: MATH 1530
TBC: Quantitative Literacy
BIA 2610 – Statistical Methods
3 credit hours
The application of collecting, summarizing, and analyzing data to make business decisions. Topics include measures of central tendency, variation, probability theory, point and interval estimation, correlation and regression. Computer applications emphasized.
MATH 2050 – Probability and Statistics
3 credit hours
Prerequisite: MATH 1810 or MATH 1910. Data analysis, probability, and statistical inference. The inference material covers means, proportions, and variances for one and two samples, one-way ANOVA, regression and correlation, and chi-square analysis. TBR Common Course: MATH 2050
CSCI 1170 – Computer Science I
4 credit hours
Prerequisite: MATH 1730 or MATH 1810 with a grade of C or better or Math ACT of 26 or better or Calculus placement test score of 73 or better. The first of a two-semester sequence using a high-level language; language constructs and simple data structures such as arrays and strings. Emphasis on problem solving using the language and principles of structured software development. Three lecture hours and two laboratory hour.
DATA 1500 – Introduction to Data Science
3 credit hours
(Same as BIA 1500.) Introduces basic principles and tools as well as its general mindset in data science. Concepts on how to solve a problem with data include business and data understanding, data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.
DATA 3500 – Data Cleansing and Feature Engineering
3 credit hours
Prerequisite: CSCI 1170. Techniques and applications used to collect and integrate data, inspect the data for errors, visualize and summarize the data, clean the data, and prepare the data for modeling for various data types.
DATA 3550 – Applied Predictive Modeling
3 credit hours
(Same as STAT 3550.) Prerequisite: CSCI 1170. An overview of the modeling process used in data science. Covers the ethics involved in data science, data preprocessing, regression models, classification models, and presenting the model.
