DATS200 Functional Methods and Coding (3 semester hours)

This course provides the student with the basic knowledge and skills to handle and analyze data using a variety of methods, as well as a variety of programming languages and tools. Students are introduced to current industry standard data analysis packages and tools such as those in R/RStudio, Matlab/Octave, SAS or SPSS. Depending on current industry standards, the student will be provided with the opportunity to develop knowledge and skills in programming environments such as R, Octave, and Python. In addition, students are introduced to data analysis packages and tools such as those in various scripting languages, SQL, Java/NetBeans, JavaScript or Julia. (Prerequisites: MATH302 and MATH220)

DATS201 Analytical Methods I (3 semester hours)

This course provides students with the basic toolkit of statistical methods and models that practitioners use for regression, analysis of variance, and linear models. This toolkit could be based on Python or on R. Topics include descriptive statistics/data summaries, inference in simple and multiple linear regression, residual analysis, estimation and testing of hypothesis, transformations, polynomial regression, model building with real data, nonlinear regression and linear models. The course is not mathematically advanced but covers a large volume of material. (Prerequisites: MATH 302 and MATH 220)

DATS211 Introduction to Data Science (3 semester hours)

This course provides an overview of data science including a foundation in research methodology. Data science is a data-driven process that provides descriptive, predictive, and prescriptive insight. Whether reporting on historical information or making predictions about future events, the goal of data science is to add value through analysis that informs. To meet this goal this course introduces a range of tools and methods including supervised and unsupervised techniques. These include techniques such as classification, rule-based association techniques, support vector machines, K-nearest neighbor, regression, and clustering techniques such as K-Means. (Prerequisite: DATS201)

DATS301 Analytical Methods II (3 semester hours)

Whereas Analytical Methods I primarily deals with continuous data, this course deals with methods and tools used to analyze categorical (discrete) data. For example, researchers analyze categorical data, e.g. using logistic regression, to determine the results of tests such as learning if a patient’s tumor cancerous or not, or whether a consumer will purchase a particular product or not. Specific attention will be paid to surveys and survey data. In addition, this course introduces generalized linear modeling. (Prerequisite: DATS211)

DATS311 Intermediate Data Science (3 semester hours)

This course continues to expand the knowledge, skills and abilities of students by two paths; first through the design of experiments required to acquire specified data, and second using carefully designed experiments to establish causal effects. Students will take a deep dive into the differences between correlation and causality. They will learn the critical thinking skills required to assert the reliability of data acquired. (Prerequisite: DATS301)

DATS331 Machine Learning I (3 semester hours)

This course introduces students to machine learning. It provides students with a broad overview of machine learning topics for both supervised and unsupervised methods. The topics typically include classification, decision trees, association rule-based classification, support vector machines, regression (linear, logistic and Bayesian), clustering, k-Nearest Neighbor, principal component analysis (PCA), Feature Selection, Linear Discriminant Analysis (LDA) and Factor Analysis. Additional topics can include ensemble methods such as stacking, bagging and boosting. (Prerequisite: DATS311)

DATS411 Advanced Data Science (3 semester hours)

This course completes the three-course sequence in Data Science. This advanced course takes students through the application of more advanced methods in regression and time series models. It includes discussions about causal inference, and a wide-range of time series models. This course emphasizes tools and methods used to capture key patterns and generate insight from data. (Prerequisite: DATS311)