Complete course information for Mathematics and Computer Information Science
CS 2510: Computer Programming I
Course Description
An introduction to procedural and object-oriented programming methodology using the Java programming language. Topics include variables, data types, operators, expressions, flow of control, classes, objects, methods, i/o operations, debugging, and testing.
Student Learning Outcomes
Problem Solving through iterative design and algorithmic thinking; Problem Formulation through Abstraction; Expressing Solutions Computationally.
Topics to be Covered
Hardware, Operating Systems, Application Software, Computer Networks, Data Representation (Binary, Hexadecimal), Unicode, Programming Basics, Java Application Structure, Expressions and Arithmetic, Object-Oriented Programming, Selection (if/else), switch, Looping (while, for, do/while).
CS 2511: Computer Programming II
Course Description
Continuation of CS 2510. Discusses arrays, class and object, inheritance, polymorphism, GUI design, exception handling, recursion, files and string manipulation. Introduces basic data structures and algorithms.
Student Learning Outcomes
Plan, design, implement, test, and debug object-oriented software; Define recursion; Define inheritance; Define polymorphism; Implement exception-handling.
Topics to be Covered
User-Defined Classes, Instance Variables, Constructors, Accessors/Mutators, Single-Dimensional Arrays, Multidimensional Arrays, ArrayList Class, Inheritance Design, Overriding Methods, Abstract Classes, Interfaces, Exceptions, Reading/Writing Text Files, GUI Basics, Recursion.
CS 2521: Scientific Programming in Python
Course Description
Focuses on developing Python coding skills, covering fundamentals, anatomy of a program, functions, data structures, and debugging. Emphasis on scientific applications, data abstraction, and object-oriented programming concepts using NumPy, Pandas, Scikit-learn, and Matplotlib.
Student Learning Outcomes
Understand data containers and control structures; Access files and data streams; Understand OOP concepts; Use abstract data structures; Understand basic machine learning; Apply scientific algorithms.
Topics to be Covered
Variables, functions, data types, Conditions, Modules, Loops (while, for), Lists, Indexing/Slicing, Scope, Parameters/Arguments, Mutable/Immutable types, Tuples, Dictionaries, Reading/Writing data, Error messages, Classes/Methods, NumPy, Pandas, Matplotlib, Machine Learning basics.
CS 3810: Data Structures and Algorithms
Course Description
Introduction to abstract data structures and their implementations, including lists, stacks, queues, trees, hash tables, heaps, and graphs. Analysis and design of sorting and searching algorithms. Advanced techniques like recursion and dynamic programming. Analysis of runtime and space complexity.
Student Learning Outcomes
Understand features of different data structures; Select proper data structures for problems; Evaluate performance of operations; Abstract operations with pseudo-language; Use OO programming to represent data structures.
Topics to be Covered
Abstract Data Types (ADT), Algorithm Efficiency, Searching (Linear, Binary), Big O Notation, Sorting (Selection, Bubble, Insertion, Merge, Quick, Radix, Bucket), Singly and Doubly Linked Lists, Hash Tables (Chaining, Probing), Binary Search Trees, AVL Trees, Red-black Trees, Heaps, Dynamic Programming, Graphs (BFS, DFS).
CS 3911: C++ in Object Oriented Design
Course Description
Introduction to object-oriented programming and design using C++. Topics include program specification, algorithm development, control structures, string manipulation, vectors, pointers, streams, inheritance, and recursion.
Student Learning Outcomes
Plan, design, and deploy object-oriented solutions in C++; Create user-defined functions; Implement pointers; Apply recursion; Implement inheritance and exception-handling.
Topics to be Covered
C++ Basics, Control Structures, Arrays/Vectors, User-defined Functions, Objects/Classes, Inheritance, Streams, Pointers, Recursion, Exceptions, Templates, Containers, Searching and Sorting Algorithms.
CS 4200: Mobile Programming via Android
Course Description
Creating mobile applications through the Android environment using Kotlin. Covers event-driven multi-tier programming, interactive GUIs, leveraging mobile sensors (GPS, cameras), and database integration. Focus on Model View Controller (MVC) pattern.
Student Learning Outcomes
Create MVC projects in Android; Work with multiple activities; Understand Android interfaces; Self-educate using documentation; Conceptualize and implement an Android course project.
Topics to be Covered
Kotlin Basics (nullable types, lambdas, classes), Android Studio, Layouts, Gradle, GUI Widgets, event-based programming, Coroutines, Permissions, Activity Life Cycle, Persistent Data (SQLite, Room, Firebase), Graphics, Animations, Sensors (GPS, accelerometers).
CS 4550: Database Management Systems
Course Description
Basic concepts of DBMS, relational model (schema, algebra, calculus), database design (ER model, normal forms), SQL, triggers, stored procedures, performance, integrity, security, and transaction processing. Includes a project using MySQL.
Student Learning Outcomes
Design relational databases; Tune database performance; Write efficient SQL; Understand relational basics and fisik plans; Understand concurrency control; Document database systems.
Topics to be Covered
Database vs DBMS, Data Independence, Data Models, Relational Model, Keys/Dependencies, ER Modeling, Extended ER, Normalization (Normal Forms), SQL (DDL, DML), Views, Procedural SQL, Transaction Management, Concurrency Control (Locks, Timestamping), Query Optimization.
CS 5710: Computer Networks
Course Description
Introduction to design and analysis of computer communication networks. Topics: application layer protocols, Internet protocols, network interfaces, local/wide area networks, wireless networks, bridging, routing, and current security topics.
Student Learning Outcomes
Understand OSI and TCP/IP architectures; Understand client/server model; Learn socket programming; Understand reliable data transfer and congestion control; Learn routing principles and IP; Understand error detection.
Topics to be Covered
Internet basics, Protocol layers, Network Edge/Core, Delay/Loss/Throughput, Security, Wireshark Labs, Application Layer (Web, HTTP, Email, DNS, P2P, Video streaming), Transport Layer (UDP, TCP), Network Layer (Forwarding, Routing, IPv4, IPv6, SDN), Link Layer (ARP, Ethernet, VLANs).
MA 1010: Powertrack Math
Course Description
Develops mathematical literacy to prepare students for college-level courses. Covers numerical, algebraic, and graphical problem-solving, real number arithmetic, and introductory algebra.
Student Learning Outcomes
Interpret mathematical models; Represent information symbolically/visually; Employ quantitative methods (arithmetic, algebra, geometry); Estimate results; Recognize limits of math methods.
Topics to be Covered
Numerical problem solving, Algebraic techniques, Graphical reasoning, Real number arithmetic, Introductory Algebra basics.
MA 1020: College Algebra
Course Description
Topics include factoring polynomials, rational and algebraic expressions, exponents and radicals, linear and quadratic equations, complex numbers, inequalities, and functions and their graphs.
Student Learning Outcomes
Understand analytic and graphical solutions; Interpret mathematical models; Represent information symbolically; Employ quantitative methods; Estimate results.
Topics to be Covered
Algebra essentials, Geometry essentials, Polynomials, Factoring, Rational expressions, Exponents, Linear/Quadratic equations, Complex numbers, Radical equations, Inequalities, Absolute value, Graphs (Distance, Midpoint, Lines, Circles), Functions.
MA 2000: Applied Statistics
Course Description
General Education course focused on statistical literacy. Emphasis on organizing/summarizing data, applying appropriate statistics, and interpreting statistical results using real data from various fields.
Student Learning Outcomes
Proficient in organizing and summarizing data; Using statistical tests and interpreting results; Interpret mathematical models; Represent information; Employ quantitative methods; Estimate results.
Topics to be Covered
Statistical thinking, Types of data, Frequency distributions, Histograms, Measures of Center/Variation, Probability basics, Binomial Distributions, Poisson Distribution, Normal Distributions, Central Limit Theorem, Hypothesis Testing, Correlation, Regression.
MA 2010: Foundations of Mathematics I
Course Description
Designed primarily for elementary education majors. Covers fundamental theory, historical context, and logic of math taught in elementary schools. Emphasis on problem solving and communication.
Student Learning Outcomes
Not explicitly detailed, but aligned with NCTM Standards for problem solving and communication in mathematics.
Topics to be Covered
Problem solving basics, Patterns/Algebraic thinking, Logic, Set theory, Numeration systems, Whole number operations, Number Theory (Divisibility, Primes, GCD/LCM), Integers, Rational numbers, Proportional reasoning, Decimals, Percents, Real Numbers, Functions.
MA 2050: Quantitative Decision Making
Course Description
Fulfills liberal education math requirement for non-STEM students. Improves quantitative literacy and daily math skills, including calculating tips, understanding interest, and critical thinking.
Student Learning Outcomes
Improved quantitative literacy and reasoning; Factoring math into daily life; Applying math to real-life problems; Decision tools for society.
Topics to be Covered
Inductive/Deductive reasoning, Estimation, Problem solving, Geometry (Perimeter, Area), Managing Money (Percent, Loans, Simple Interest), Probability (Empirical, Theoretical), Financial Literacy (Psychology/Foundations of Money), Academic Integrity.
MA 2080: Precalculus for Business
Course Description
Study of linear, quadratic, exponential, and logarithmic functions and their graphs; systems of equations and matrices; with applications in business and economics.
Student Learning Outcomes
Understand basic concepts of functions; Develop mathematical maturity for business calculus; Survey analytical techniques used in business/economics.
Topics to be Covered
Linear Equations and Graphs, Regression, Functions (Elementary, Quadratic, Polynomial, Rational, Exponential, Logarithmic), Mathematics of Finance (Simple/Compound interest, Annuities, Amortization), Systems of Linear Equations, Matrices, Linear Programming (Geometric Approach, Simplex Method), Logic, Sets, Counting, Probability.
MA 2090: Precalculus
Course Description
Study of algebraic, logarithmic, exponential, and trigonometric functions and their graphs. Designed for students planning to take MA 2310 Calculus I.
Student Learning Outcomes
Understand basic concepts of functions; Develop mathematical maturity for calculus; Use functions as tools to model and solve practical problems.
Topics to be Covered
Linear and Quadratic Functions, Polynomial and Rational Functions, Exponential and Logarithmic Functions, Composite/Inverse functions, Trigonometric Functions (Unit circle, Graphs), Analytic Trigonometry (Identities, Equations), Applications of Trig (Law of Sines/Cosines, Harmonic Motion).
MA 2300: Calculus for Business & Economics
Course Description
Topics include limits, differentiation, and integration with applications from business, economics, and social sciences.
Student Learning Outcomes
Foundation in differential calculus and introduction to integral calculus; Emphasis on business applications, problem solving, graphing, and optimization; Interpret mathematical models and results.
Topics to be Covered
Limits and the Derivative (Continuity, Marginal Analysis), Additional Derivative Topics (Chain Rule, Related Rates, Elasticity), Graphing and Optimization (First/Second Derivative test, L'Hopital's Rule), Integration (Substitution, Definite/Indefinite integrals, FTC, Area between curves).
MA 2310: Calculus & Analytic Geometry I
Course Description
Covers functions, limits, continuity, derivatives of various functions (polynomial, algebraic, exponential, trig), applications of derivatives, integrals, and the fundamental theorem of calculus.
Student Learning Outcomes
Understand meaning of limits, continuity, and derivatives; Use these concepts to solve various problems; Interpret mathematical models and represent information symbolically.
Topics to be Covered
Limits (Idea, Definitions, Techniques, Infinite), Derivatives (Rules, Product/Quotient, Trig, Rates of Change, Chain Rule, Implicit, Log/Exp, Inverse Trig), Related Rates, Applications (Max/Min, Mean Value Theorem, Graphing, Optimization, Differentials, L'Hopital's), Integration (Approximating Area, Definite Integrals, FTC, Substitution).
MA 2320: Calculus & Analytic Geometry II
Course Description
Topics include indefinite and definite integrals, applications of the definite integral, integration techniques, and infinite sequences and series.
Student Learning Outcomes
Proficient in integration and its applications; Learn about infinite sequences and series; Interpret and draw inferences from models; employ quantitative methods in calculus.
Topics to be Covered
Integration (advanced techniques), Applications of Definite Integrals, Infinite Sequences and Series, Logic of Calculus expansion.
MA 3020: Foundations of Mathematics II
Course Description
Designed primarily for elementary education majors. Covers elementary combinations, probability, statistics, geometry, and measurement. Aligns with National Council of Teachers of Mathematics Standards.
Student Learning Outcomes
Fundamental theory and underlying logic of math for elementary school; Focus on problem solving and communication.
Topics to be Covered
Probability (Multistage experiments, Simulations, Permutations/Combinations), Data Analysis/Statistics (Experiments, Displaying data, Central tendency/Variation), Geometry (Curves, Polygons, 3D), Congruence and Similarity (Constructions, Transformations), Area, Pythagorean Theorem, Volume, Mass, Temperature.
MA 3030: Discrete Mathematics
Course Description
Introduction to discrete mathematical structures. Topics include propositional and predicate logic, set theory, relations, functions, induction, recursion, algorithms, number theory, and graphs/trees.
Student Learning Outcomes
Foundation for upper-level CS and math courses; Learn to think and reason mathematically; Communicate ideas mathematically; Master symbolic logic and set theory; Understand Graph Theory algorithms.
Topics to be Covered
Logic of Compound Statements (Truth tables, Arguments), Logic of Quantified Statements (Predicates), Number Theory (Direct/Indirect proof, Divisibility), Mathematical Induction, Recursion, Set Theory (Definitions, Element method of proof), Functions (One-to-one, Inverse), Relations (Reflexivity, Symmetry, Equivalence), Graphs and Trees (Paths, Matrices, Isomorphisms).
MA 3160: Linear Algebra
Course Description
Discusses main concepts of linear algebra including systems of linear equations, matrices, determinants, vectors in space, Euclidean vector spaces, subspaces, linear independence, eigenvalues/eigenvectors, and linear transformations.
Student Learning Outcomes
Solve systems of linear equations; Carry out basic matrix algebra; Interpret geometric properties of vectors; Define linear transformations and represent by matrices; Determine subspaces, bases, and dimension; Calculate eigenvalues/eigenvectors; Write mathematical proofs.
Topics to be Covered
Gaussian Elimination, Inverses, Elementary Matrices, Matrix Transformations, Determinants (Cofactor expansion, Row reduction, Cramer's Rule), Euclidean Vector Spaces (Norm, Dot Product, Orthogonality), General Vector Spaces, Rank/Nullity, Diagonalization, Inner Product Spaces.
MA 3210: Introduction to Probability and Statistics
Course Description
Foundation material in probability and statistical inference. Topics include sample spaces, conditional probability, Bayes' rule, random variables, discrete/continuous probability distributions, expectation, estimation, and hypothesis testing.
Student Learning Outcomes
Foundation in probability theory and statistical inference; Preparation for advanced courses; Solve applied problems.
Topics to be Covered
Sample Space, Events, Counting Sample Points, Additive Rules, Bayes' Rule, Random Variables, Discrete/Continuous/Joint Probability Distributions, Expectation (Mean, Variance, Covariance), Chebyshev's Theorem, Normal Distribution, Sampling Distributions, Central Limit Theorem, t-Distribution, F-Distribution, Estimation Problems, Hypothesis Testing.
MA 3330: Calculus & Analytic Geometry III
Course Description
Continuation of Calculus covering vector algebra and geometry of 3D space, limits/differentiation for functions of multiple variables, and multiple integration including Green's, Stokes, and Divergence Theorems.
Student Learning Outcomes
Understand algebra of vectors; Meaning of limits, continuity, and derivatives for multivariable functions; Master double/triple integrals and Green's Theorem; Solve a variety of complex problems.
Topics to be Covered
Parametric Equations, Polar Coordinates, Three-Dimensional Coordinate Systems, Vectors, Dot/Cross Product, Lines and Planes, Vector Functions (Arc length, Motion in space), Partial Derivatives (Chain Rule, Gradient Vector, Max/Min, Lagrange Multipliers), Multiple Integrals (Rectangles, General regions, Polar, Surface Area), Vector Calculus (Line integrals, Surface integrals).
MA 3520: Transition to Advanced Mathematics
Course Description
Introduction to concepts in advanced mathematics with focus on writing proofs. Topics: logic, set theory, relations, functions, cardinality, number theory, abstract algebra, and real analysis.
Student Learning Outcomes
Prepare for higher-level math courses; Master problem solving techniques and mathematical reasoning; Ability to read and write mathematical proofs; Mastery of fundamental advanced math topics.
Topics to be Covered
Proof Methods, Mathematical Induction, Relations (Equivalence, Congruence Modulo n), Functions (Bijective, Inverse), Cardinalities of Sets (Numerically equivalent, Denumerable, Uncountable), Number Theory (Divisibility, Euclidean Algorithm, Fundamental Theorem of Arithmetic), Group Theory (Binary Operations, Subgroups, Isomorphisms), Calculus (Limits, Continuity, Differentiability), Topology (Metric Spaces, Open sets).
MA 4360: Differential Equations
Course Description
Covers methods for solving first and higher order differential equations, systems of equations, boundary value problems, and Laplace transforms. Includes applications in social sciences, physics, and engineering.
Student Learning Outcomes
Proficient in solving ordinary differential equations; Study and apply ODEs to real-world scenarios.
Topics to be Covered
First-Order DEs (Separable, Linear, Exact, Substitutions), Modeling with First-Order DEs, Higher-Order DEs (Homogeneous, Undetermined coefficients, Variation of parameters, Cauchy-Euler), Modeling with Higher-Order DEs (Initial/Boundary-Value), Laplace Transform, Systems of Linear First-Order DEs.