Data Science Minor

Data Science Minor

Data Science

The Data Science minor requires 16 hours.

Required Courses (16 hours)

  • MATH 1530 - Applied Statistics  3 credit hours  
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    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

  • 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.


  • 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.