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# Courses

#### Core Courses for Major and Minor:

### Computer Science Courses

### Information Systems Courses

### Mathematics Courses

### Statistics Courses

**CS 065: ****INTRODUCTION TO COMPUTER SCIENCE I**, 3 credit hrs.

Algorithms, programming, program structures and computing systems. Debugging and verification of programs, data presentation. Computer solution of problems using a high- level language.

Prereq.: Four years of high school mathematics or MATH 020.

**CS 066: INTRODUCTION TO COMPUTER SCIENCE II**

Continuance of CS 65 using a block-structured language and emphasizing data abstraction. More general data structures and alternative implementations of them are used in programs, Sorting, searching and tree traversal algorithms are used and analyzed. Provides preparation for further study in computer science.

Prereq: CS 065 or equivalent

**CS 083: ****COMPUTER ETHICS**, 3 credit hrs.

This course increases understanding of issues related to ethics, professional conduct and social responsibility as they arise in Computer Science and applications of Information Technology. Additionally, the course serves to develop 1) the ability to think clearly; 2) habits of professional responsibility and behavior; and 3) effective writing and presentation skills. Students are exposed to the history of the discipline from a social point of view, and to various frameworks from which ethical and professional decisions must be made within the discipline. Sophomore, junior, or senior standing required.

**CS/BIO 116: BIOINFORMATICS, ***3 credit hrs.*

The analysis of biological systems through the use of computational methods. Analyzing these systems often involves creating electronic databases of biological structures (protein sequences, genomes, DNA, etc.) and developing algorithms to analyze the data.

Prereq.: CS 065 or consent of instructor.

**CS 137: ****DATA STRUCTURES AND ALGORITHM ANALYSIS,** 3 credit hrs.

Formal and informal methods for analyzing the correctness and efficiency of algorithms. Implementation and analysis of advanced algorithms and data structures such as AVL trees, B-trees, hash-tables, heaps, and graph algorithms. Introduction to complexity theory and NP-Completeness.

Prereq.: CS 66 and (MATH 054 or MATH 101).

**CS 143: ****ARTIFICIAL INTELLIGENCE,** 3 credit hrs.

Introduction to the theory, tools and methods of artificial intelligence. Topics include knowledge representation, predicate calculus, basic data structures, and problem solving strategies. A symbol manipulation language is used. Computer science aspects of artificial intelligence are emphasized. Applications from areas such as natural language understanding, vision or expert systems are examined.

Prereq.: CS 066.

**CS/MATH 165: INTRODUCTION TO NUMERICAL ANALYSIS,**

A practical introduction to numerical computing. The primary focus is the concepts and tools involved in modeling real continuous mathematical or engineering problems on the digital computer. The effects of using floating point arithmetic, error analysis, iterative methods for solving equations, and numerical integration and differentiation will be studied.

Prereq.: CS 065, MATH 080 and 100.

**CS 167: MACHINE LEARNING, **

This course introduces approaches to developing computer programs that learn from data. Both foundational and contemporary machine learning algorithms will be covered in the context of a variety of data and problem types. Specific topics will vary but may include artificial neural networks, decision trees, instance-based learning, Bayesian learning, support vector machines, hidden Markov models, reinforcement learning, and natural language processing. Students will develop their own implementations of the algorithms as well as utilize modern machine learning software and programming libraries.

Pre-requisite: CS 065 and (CS 066 or STAT 040).

**CS 178: CLOUD COMPUTING AND DATABASE SYSTEMS**

Data sets have become so large and complex that a new set of software tools must be developed in order to facilitate questions that can lead to impactful insights. This course will provide an in-depth study of tools and techniques used to process 'big data' stored on multiple computers. Topics include virtualization, python programming, the Hadoop ecosystem, MapReduce programming, Amazon Web Services, database querying including SQL and NoSQL programming.

**CS 190: CASE STUDIES IN DATA ANALYTICS**, 3 credit hrs.

In this course, students will apply descriptive, predictive, and prescriptive data analysis methods learned in previous courses to new cases. Students will learn to effectively manage long-term data analysis projects within diverse teams through a complete data analytics project lifecycle and compellingly communicate outcomes through writing and oral presentations which include appropriate use of data visualizations.

Pre-requisites: (1) CS 066, (2) STAT/MATH 130 or ACTS/MATH 131, and (3) two of STAT 170, STAT 172, CS 167, CS 178.

**IS 044: Microsoft Office Tools for Business Analysis ,**

**IS 160: DATABASE MANAGEMENT,**

A study of database concepts and technologies used in managing and using data within modern organizations: defining data needs; using modern database tools; understanding database design; and creating applications. Prereq.: IS 044 or CS 065.

**MATH 028: ****BUSINESS CALCULUS**, 3 credit hrs.

Brief algebra review, data analysis, limits, derivatives, integration, applications to business. Prereq.: MATH 020 or equivalent.

**MATH 050: CALCULUS I**

Functions; continuity; limits; differentiation; applications of derivatives; definite integrals; Prereq: Math 20 or equivalent.

**MATH 070: ****CALCULUS II**, 4 credit hrs.

Definite integrals; techniques of integration; applications of definite integrals, infinite series and sequences; power series; Taylor series. Prereq: Math 50.

**MATH 080: ****LINEAR ALGEBRA**, 3 credit hrs.

Systems of linear equations; vectors, linear independence, linear transformations; matrix operations, inverse of a matrix, determinants; null and column space of a matrix, rank; general vector spaces, basis of a vector space, dimension; eigenvalues and eigenvectors, diagonalization, orthogonality; applications. Prereq.: MATH 050.

* MATH 100: CALCULUS III, 4 credit hrs.*Plane curves; vectors; limits, continuity and differentiation for functions of several variables; multiple integrals. Prereq.: Math 70

* MATH 120: APPLIED DIFFERENTIAL EQUATIONS I, 3 credit hrs.*Ordinary differential equations; systems of differential equations. Fourier series, integrals and harmonic analysis, partial differential equations, orthogonal functions. Bessel functions. Legendre functions. Prereq.: MATH 080, 100.

**MATH 121: ****APPLIED DIFFERENTIAL EQUATIONS II**, 3 credit hrs.

Continuance of MATH 120. Prereq.: MATH 120.

**MATH 125: MATHEMATICAL MODELING ,**

The construction, analysis and interpretation of mathematical models. Examples are drawn from a variety of areas. Student projects are required. Prereq.: MATH 070, 080.

**MATH 127: ****INTRODUCTION TO GAME THEORY**, 3 credit hrs.

Game Theory is the logical analysis of situations of conflict and cooperation. Topics will include zero-sum games and non-zero-sum two-person games, n person games, applications to economics, politics and nature. Prereq.: MATH 028 or MATH 050 or consent of instructor.

**STAT/MATH 130: PROBABILITY FOR ANALYTICS, **

An introduction to the concepts of probability that form the foundation for analytics practice. Descriptive statistics, data visualization, univariate discrete and continuous probability distributions, confidence intervals and one-sample hypotheses testing. Applies R and/or SAS skills. Prereq.: MATH 070 and STAT 040.

**MATH/CS 165: INTRODUCTION TO NUMERICAL ANALYSIS**

Error analysis, iterative methods for solving nonlinear equations, direct and iterative methods for solving linear systems, approximation of functions, derivatives, integrals. Prereq.: CS 065, MATH 080 and 100.

**MATH 176:**** ADVANCED LINEAR ALGEBRA**, 3 credit hrs.

Hermitian, unitary, normal, positive definite and nonnegative matrices; LU, QR and Choleski factorizations; equivalence, similarity, congruence and their respective canonical forms; norms; Schur triangular form, Jordan canonical form; applications. Prereq.: MATH 101.

**STAT 040: R and SAS***, 3 credit hrs.*

This course will cover how to access, structure, format, manipulate and archive data using R and SAS. It will include topics in data inputting, merging files, cleaning data, data summary, descriptive statistics, running procedure statements, graphical presentation of data, loops, if/then statments, and creating your own scripts and functions that extend the language. Prereq.: MATH 020 or equivalent college algebra course, knowledge of basic software tools including word processing, email, Internet browsers, and presentation software. Course is for the Data Analytics major or minor, or the Actuarial Science major.

**STAT 071: ****STATISTICS I**, 3 credit hrs.

An introduction to descriptive and inferential statistics; frequency distributions; measures of central tendency and spread; confidence intervals; large and small sample tests of significance; probability; and binomial and normal distributions. Prereq.: MATH 020 or MATH 028 or equivalent.

**STAT 072: ****STATISTICS II**, 3 credit hrs.

Continuance of STAT 071 with further tests of significance; analysis of variance; correlation and regression; and contingency table analysis. Prereq.: STAT 071, STAT 130, or ACTS 131, and also IS 044.

**STAT/MATH 130: PROBABILITY FOR ANALYTICS, **

An introduction to the concepts of probability that form the foundation for analytics practice. Descriptive statistics, data visualization, univariate discrete and continuous probability distributions, confidence intervals and one-sample hypotheses testing. Applies R and/or SAS skills. Prereq.: MATH 070 and STAT 040.

**STAT 170: ****STATISTICAL MODELING AND DATA ANALYSIS II***,* *3 credit hrs.*

Regression and time analysis. Specific topics include simple and multiple regressionl multicollinearity; heteroscedasticity; diagnostics; forecasting with the regression model; binary and multiple-choice models; autocorrelation; random walks; ARIMA models; minimum mean-square-error forecasts and confidence intervals. Prereq.: STAT 040 and one of (STAT 072, STAT 130, ACTS 135 or ACTS 141).

**STAT 172: GENERALIZED LINEAR MODELS AND DATA MINING, **

Data Mining and Generalized Linear Modeling - The emphasis will be on data analysis, statistical assumptions, and diagnostics. Topics include: Linear Regression, Logistic and Probit Regression, CART, Neural Networks, Association Rules, Clustering, Generalized Linear Models, Models for Continuous Data, Models for Binary Data, Models for Polytomous data, Log-Linear Models, Conditional Likelihoods, and Gamma Regression. Prereq.: STAT/MATH 130 or ACTS/MATH 131; STAT 040; MATH 070; and STAT 170.

**STAT 190:**** **3 credit hrs.

In this course, students will apply description, predictive, and prescriptive data analysis methods learned in previous cases to new cases. Students will learn to effectively manage long-term data analysis projects within diverse teams through a complete data analytics project lifecycle and compellingly communicate outcomes through writing and oral presentations which include appropriate use of data visualizations. Prereq.: CS 167 AND STAT 172.

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