CSIT1100 Principles of Computing - 3 s.h.
An introduction to the fundamentals of computer programming through extensive practice developing software in the Python language. Fundamental terminology and topics such as integrated development environments, variables, data types, control structures, functions, and objects will be covered. ELO4 Global Learning - Innovation

CSIT1200 Data Structures - 3 s.h.
Continuation of CSIT1100 with emphasis on more advanced programming that involve classic data structures such as arrays, dictionaries, linked lists, queues, stacks, and trees. Recursive techniques and efficiency considerations will also be covered. Prerequisite: CSIT1100.

CSIT3300 Database Concepts and SQL - 3 s.h.
A study of the concepts and structures required to implement a database system including the logical design and physical organization of the database. Emphasis is given to the design and development of database systems that includes understanding and applying entity-relationship models. Implementation of a database using SQL on a database system is included. Prerequisite: CSIT1100.

CSIT4200 Machine Learning - 3 s.h.
A study of regression, kernels, support vector machines, clustering, Neural networks. Prerequisites: MATH3340, MATH2510, CSIT1200. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5370 Machine Learning.)

DSCI1500 Beginning Data Science and Data Analytics - 3 s.h.
Introduction to the basic methods of analysis in Data Science and Data Analytics. This course introduces students to the basic statistical methods, coding applications, problem solving, and data integrity issues common to the field.

DSCI4320 Practical Applications of Data Science - 3 s.h.
Exploratory data analysis is introduced along with fundamental considerations for data analysis on real data sets. Classical models and techniques for classification are included. Methods of data visualization are introduced. Prerequisites: CSIT4200 (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5320 Practical Application of Data Science.)

MATH1370 Statistics for Sciences - 3 s.h.
Data analysis and measures of central tendency, dispersion, and correlation. Introduction to probability. Estimation and hypothesis testing. Bivariate regression. ANOVA. Introduction to nonparametric techniques. Prerequisite: 1 year high school algebra. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.

MATH1510 Calculus I - 4 s.h.
Limits, continuity, differentiation, and applications including exponential, logarithmic, trigonometric, and inverse functions. Mean value theorem, curve sketching, Riemann sums, and the fundamental theorem of calculus. Prerequisite: 2 years high school algebra. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.

MATH1520 Calculus II - 4 s.h.
Integration techniques and applications, polar coordinates, improper integrals, sequences and series of real numbers, and power series. Prerequisite: MATH1510. Goal 3A

MATH2350 Discrete Mathematics - 3 s.h.
A survey of topics in discrete mathematics focusing on introductory logic, methods of mathematical proof, set theory, determinants and matrices, combinatorics, and graph theory. Prerequisite: Instructor approval for non-CSIT/MATH majors, 2 years high school algebra or MATH1280. ELO6 Math, GE2B Foundational Skills-Mathematics/Quantitative.

MATH2510 Calculus III - 4 s.h.
Conic sections, vectors in space, functions of several variables, partial differentiation, multiple integration, line integrals, and Green’s Theorem. Prerequisite: MATH1520. Goal 3A

MATH3340 Linear Algebra - 3 s.h.
Matrices, vector spaces, linear transformations. Prerequisite: MATH1510 and MATH2350. Goal 3A.
+This course is only offered every other year.

MATH4350 Probability and Advanced Statistics - 3 s.h.
Introduction to probability, classical probability models and processes, random variables, conditional probability, bivariate distributions and their development, goodness of fit tests, and other nonparametric methods. Prerequisite: MATH1520 and MATH2350. +This course is only offered every other year. (Students participating in the 4+1 Masters program in Data Science and Analytics should sign up for DSCI5340 Probability and Statistical Inference.)