Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla id facilisis arcu.
Praesent fringilla sem non justo dapibus, egestas auctor massa tincidunt. Quisque
pretium, ligula fermentum malesuada feugiat, felis est iaculis nibh, sed iaculis nisi
enim eget est. Nullam id sodales augue, non ullamcorper felis.
Column 2 Content
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla id facilisis arcu.
Praesent fringilla sem non justo dapibus, egestas auctor massa tincidunt. Quisque
pretium, ligula fermentum malesuada feugiat, felis est iaculis nibh, sed iaculis nisi
enim eget est. Nullam id sodales augue, non ullamcorper felis.
Nullam nunc quam, volutpat in bibendum at, molestie eget est.
Miscellaneous
Show/Hide Accordion
Title
Kennesaw State University is the third-largest university in Georgia, offering nearly
150 undergraduate, graduate and doctoral degrees. A member of the University System
of Georgia, Kennesaw State is a comprehensive university with more than 33,000 students
from over 130 countries. In January 2015, Kennesaw State and Southern Polytechnic
State University consolidated to create one of the 50 largest public universities
in the country.
Title
Kennesaw State University is the third-largest university in Georgia, offering nearly
150 undergraduate, graduate and doctoral degrees. A member of the University System
of Georgia, Kennesaw State is a comprehensive university with more than 33,000 students
from over 130 countries. In January 2015, Kennesaw State and Southern Polytechnic
State University consolidated to create one of the 50 largest public universities
in the country.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla id facilisis arcu.
Praesent fringilla sem non justo dapibus, egestas auctor massa tincidunt. Quisque
pretium, ligula fermentum malesuada feugiat, felis est iaculis nibh, sed iaculis nisi
enim eget est. Nullam id sodales augue, non ullamcorper felis.
Christopher Ward joined Kennesaw State University in April 2002. As the Director of
Web Services and Mobile Development, he leads the ESS WebGroup: the campus division
who designs, develops and maintains the top levels of Kennesaw.edu as well as multiple
academic and administrative department Web sites and mobile initiatives. His achievements
at the university include a finalist for the 2014 Administrator of the Year, the Center
for University Learning's Fall 2008 Facilitator of the Quarter, and recognition as
the 2004 Staff Employee of the Year!
Tumblr Feed
Day After Tomorrow...
“Never put
off till tomorrow what may be done day after tomorrow just as well.” ~Mark Twain
Jobs, classes, friends, family, and digital
distractions often lure us into accepting Mark Twain’s invitation to procrastinate.
As the semester winds down, though, deadlines that once seemed so far away now
loom. That “day after tomorrow” is just about here.
When it comes to your writing projects,
procrastination can be downright dangerous. If you don’t start a paper until
the night before it’s due, your grade will likely suffer. Even worse, the most
desperate among us sometimes resort to plagiarism as time runs out. Clearly,
waiting until the last minute can wreak havoc on your academic career.
But is procrastination always bad?
What seems like procrastination, or even
writer’s block, is sometimes just a waiting period of sorts that some writers have to go
through before their ideas can productively take shape on paper or screen.
If this sounds like you, you’re in good
company. E.B. White, Virginia Woolf, and Ernest Hemingway are just a few of the
many great writers who have confessed to their own tendencies to “wait for
writing.”
According to writing expert Donald Murray, five
key elements emerge during what he calls an “essential delay” that is often necessary for writerly success:
1) Information:
Writers need facts and details, not merely abstract ideas.
Writing Center Tip: Do your
research, and when possible (and with your instructor’s permission), draw from personal
experiences to give meaning to your writing.
2) Insight:
Writers develop “hints” or “guesses” about ideas that evolve as they write. In
other words, we often discover what we want to say after we start writing, refining our ideas during revision.
Writing Center Tip: Even if you
don’t know exactly what you want to say, start writing; the answers will arrive.
And give yourself time to revise.
3) Order:
While meaning takes shape during writing, it helps to begin a draft with some
sense of how to organize your ideas. Many writers need a strong introduction to
“set the tone” for what comes next. Others write more productively once they
have a clear idea of how their piece will end, even if they’re not sure how
they will get there.
Writing Center Tip:Prewriting
activities—like cluster diagramming, listing, and outlining— can help you
organize ideas, leading to papers with that “flow” writers strive for.
4) Need:
In this case, need does not refer to deadlines, but rather to the desire of the
writer to share ideas with interested readers.
Writing Center Tip: While an
actual deadline might be your most urgent motivator at this point in the
semester, take ownership of your writing and make meaning with your words—for
yourself and your audience.
5) Voice:
A writer must find the right voice to create authentic, engaging pieces.
Writing Center Tip: When
information, insight, order, and need are in place, your voice emerges more
easily. Finish your first draft leaving time to revise with just the right voice
and audience in mind.
And remember, the KSU Writing Center can help
you with any stage of the writing process, from prewriting to revision. While we
always recommend that you schedule an appointment in time to make meaningful
revisions to your work before the due date, we are also here for those who have
procrastinated beyond a productive delay…even when that means it’s the day after the day after tomorrow.
-June
Source
Murray, Donald. “The Essential Delay: When Writer’s Block Isn’t.” When a Writer Can’t Write: Studies in Writer’s Block and Other Composing Problems. Ed. Mike Rose. New York: Guilford, 1985. 219-226.
The course covers special topics at the intermediate level that are not in the regular course offerings.
Prerequisites: Approval of the instructor, major area committee, and department chair.
Credits: 3
CS 3223 - Computer Architecture
A study of instruction set architectures; basic processor components such as control units, ALU's, and registers; memory; input/output; and performance enhancement using caches and pipelines. Design of the major processor components is discussed in terms of the concepts presented in . Some coverage of assembly language programming is included.
Prerequisites: and CSE 1301
Credits: 3-0-3
CS 3305L - Data Structures Lab
This laboratory course will cover the implementation of data structures concepts in a contemporary programming language.
Prerequisites: (CSE 1322 and CSE 1322L) and MATH 2345
Credits: 1
CS 3305 - Data Structures
This course introduces data structures, specification, application, and implementation. The case studies will illustrate how data structures are used in computing applications. The emphasis of the course is on linear and some nonlinear data structures and object oriented principles. Topics include: abstract data types, stacks, queues, lists, binary search trees, priority queues, recursion, algorithm efficiency, trees, heaps, hash tables, and analysis of search and sort algorithms and their performance for implementation and manipulation. The programming language to be used in this course is any standard high-level object-oriented programming language such as C++, Java, and Ada.
Prerequisites: MATH 2345 and (CSE 1322 and CSE 1322L)
Credits: 3
CS 3410 - Introduction to Database Systems
Introduction to the database management systems, database processing, data modeling, database design, development, and implementation. Contrasts alternative modeling approaches. Includes implementation of current DBMS tools and SQL.
Prerequisites: A grade of B or better in both CSE 1322 and CSE 1322L
Credits: 3
CS 3502 - Operating Systems
This course introduces the fundamental concepts and principles of operating systems. Topics covered include system performance, processes and threads, multiprogramming, scheduling, memory management, synchronization, deadlocks, file systems, Input/output systems. Additional topics: security and protection, network and distributed OS.
Prerequisites: (CS 3503 and CS 3503L) and (CS 3305 and CS 3305L)
Credits: 3
CS 3503L - Computer Organization and Architecture Lab
This course will provide the student the opportunity to access some of the physical components of a computer and generate code to manipulate these components.
Prerequisites: CSE 1322 and CSE 1322L
Credits: 1
CS 3503 - Computer Organization and Architecture
Introduction and overview of basic computer organization. Computer arithmetic: binary, hexadecimal and decimal number conversions, binary number arithmetic and IEEE binary floating point number standard. Basic computer logic: gates, combinational circuits, sequential circuits, adders, ALU, SRAM and DRAM. Basic assembly language programming, basic Instruction Set Architecture (ISA), and the design of single cycle CPU. Hardware security will be introduced.
Prerequisites: CSE 1322 and CSE 1322L
Credits: 3
CS 4242 - Artificial Intelligence
The primary objective of this course is to provide a introduction to the basic principles and applications of Artificial Intelligence. It covers the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, perception and action, and learning -- and their applications. Students will design and implement key components of intelligent agents of modern complexity and evaluate their performance. Students are expected to develop familiarity with current research problems, research methods, and the research literature in AI.
Prerequisites: CS 3305 and CS 3305L
Credits: 3
CS 4265 - Big Data Analytics
This course covers algorithms and tools that are needed to build MapReduce applications with Hadoop or Spark for processing gigabyte, terabyte, or petabyte-sized datasets on clusters of commodity hardware. A wide range of data algorithms will be discussed in this course.
Prerequisites: A grade of C or better in (CS 3305 and CS 3305L) and CS 3410
Credits: 3
CS 4267 - Machine Learning
This course covers the-state-of-the-art machine learning techniques. Focuses will be put on deep learning, kernel methods and ensemble learning. Students will learn applying advanced machine learning techniques to solve challenging problems, especially big data problems.
Prerequisites: A grade of C or better in (CS 3305 and CS 3305L) and CS 3410
Credits: 3
CS 4270 - Intelligent Systems in Bioinformatics
Biological sciences are undergoing a revolution in how they are practiced. In the last decade, a vast amount of biological data has become available, and computational methods are playing a fundamental role in transforming this data into scientific understanding. Bioinformatics involves developing and applying computational methods for managing and analyzing information about the sequence, structure and function of biological molecules and systems. This course covers a wide range of machine learning, data mining, and computational algorithms to solve various bioinformatics research problems.
Prerequisites: A grade of C or better in (CS 3305 and CS 3305L) and CS 3410
Credits: 3
CS 4305 - Software Engineering
This course provides an overview of the software engineering discipline with emphasis on the development life cycle and UML modeling. It introduces students to the fundamental principles and processes of software engineering, including Unified, Personal, and Team process models. This course highlights the need for an engineering approach to software with understanding of the activities performed at each stage in the development cycle. Topics include software process models, requirements analysis and modeling; design concepts and design modeling; architectural design and styles; implementation; and testing strategies and techniques. The course presents software development processes at the various degrees of granularity.
Prerequisites: CS 3410, CSE 3801, COM 1100
Credits: 3
CS 4306 - Algorithm Analysis
Advanced algorithm analysis including the introduction of formal techniques and the underlying mathematical theory. Topics include asymptotic analyses of complexity bounds using big-O, little-o, omega, and theta notations. Fundamental algorithmic strategies (brute-force, greedy, divide-and-conquer, backtracking, branch-and-bound, pattern matching, parallel algorithms, and numerical approximations) are covered. Also included are standard graph and tree algorithms. Additional topics include standard complexity classes, time and space tradeoffs in algorithms, using recurrence relations to analyze recursive algorithms, NP-completeness, the halting problem, and the implications of non-computability.
Prerequisites: CS 3305 and CS 3305L
Credits: 3
CS 4308 - Concepts of Programming Languages
This course covers the fundamental concepts on which programming languages are based and the execution models supporting them. Topics include values, variables, bindings, type systems, control structures, exceptions, concurrency, and modularity. Languages representing different paradigms are introduced.
Prerequisites: (CS 3503 and CS 3503L), and (CS 3305 and CS 3305L)
Credits: 3
CS 4322 - Mobile Software Development
This course primarily focuses on mobile sensor application development and security of smartphones and mobile telecommunication systems. The goals of the course is to provide students with real world relevant mobile sensor app development and improve their knowledge and skills on mobile application development and mobile security.
Prerequisites: (CS 3305 and CS 3305L) and (CS 3410 or CSE 3153) and SWE 3313
Credits: 3
CS 4400 - Directed Studies
This course covers special topics of an advanced nature that are not in the regular course offerings. Up to three hours may be applied to the major area.
Prerequisites: Approval of the instructor, major area committee, and department chair.
Credits: 1-3
CS 4412 - Data Mining
This course covers fundamental data mining concepts and techniques for discovering interesting patterns from data in various applications. Topics include data preprocessing, data warehousing and OLAP, mining frequent patterns, classification, clustering, and tend analysis.
Prerequisites: (CS 3305 and CS 3305L) and CS 3410
Credits: 3
CS 4491 - Advanced Topics in Computer Science
This course provides the current and relevant topics in an advanced Computer Science area of interest to faculty.
Prerequisites: A grade of C or better in any prerequisite course. Prerequisite course(s) vary depending upon the topic.
Credits: 3
CS 4504 - Distributed Computing
A course that introduces students to the fundamental principles common to the design and implementation of programs that run on two or more interconnected computer systems. The subtopics, which are based on these principles, include: distributed operating system and network protocols for process communication, synchronization, scheduling, and exception and deadlock resolution; understanding of client-server, web-based collaborative systems; parallel computing; concurrency issues; and API's for distributed application development. Several distributed computing environments, like MPI, PVM, and Java RMI are discussed and used in developing experimental projects in a cluster of networked computers.
Prerequisites: CS 3502
Credits: 3
CS 4512 - Systems Programming
This course presents an introduction to systems programming in Linux/Unix. Topics include file I/O, process control and communication, threading, and network-aware systems programs.
Prerequisites: (CS 3305 and CS 3305L), and CS 3502
Credits: 3
CS 4514 - Real-Time Systems
This course covers the software-development life cycle as it applies to real-time systems. Alternatives: • Including labs that involve the use of a real-time operating system and an associated development environment, or • Modeling with UML, and object oriented simulation. Introduction to formal specification of real-time systems. A course project is required to be completed by the end of the semester.
Prerequisites: CS 3502
Credits: 3
CS 4522 - HPC & Parallel Programming
This course will introduce parallel programming techniques for shared memory and distributed memory systems. Topics include threading, OpenMP, and MPI.
Prerequisites: (CS 3305 and CS 3305L), and CS 3502
A study of practical parallel algorithms with an emphasis on implementation and performance issues on massively parallel processors. Design and implement high performance computing applications using CUDA running on Graphics Processing Unit (GPU). Topics include heterogeneous parallel programming, hardware threading models, synchronization, parallel blocking algorithms, register allocations, memory performance, and inter-thread communication.
Prerequisites: (CS 3305 and CS 3305L), and CS 3502
Credits: 3
CS 4524 - Cloud Computing
This course discusses the fundamental concepts and techniques of cloud computing. Students will develop an understanding of cloud computing architecture, Infrastructure as a Service (IaaS), Platform-as-a-Service (PaaS), Software as a Service (SaaS), Virtualization, and Application Development on Cloud.
Prerequisites: (CS 3305 and CS 3305L) and CS 3502
Credits: 3
CS 4612 - Secure Software Development
This course covers the design and implementation of secure software. Some of the topics covered are the characteristics of secure software, the role of security in the development lifecycle, designing secure software, and best security programming practices. Security for web and mobile applications will be covered.
Prerequisites: CS 3503 and CS 3503L
Credits: 3
CS 4622 - Computer Networks
This course covers computer networking and includes software application-related, protocol-related and security-related issues involved in the Internet. Topics include basic network structures, mechanisms for application-to-application communications, protocol layering, Internet addressing, unicast and multicast routing, connection establishment and termination, data flow and congestion control, and error handling. A specific protocol suite will be examined in detail. More advanced topics that build on the student's understanding of network protocols are also introduced, such as network security, mobile networks and the future Internet.
Prerequisites: CS 3503 and CS 3503L
Credits: 3
CS 4632 - Modeling and Simulation
This course covers the modeling and simulation of the structure and behavior of real-world systems using object-oriented discrete-event simulation techniques. The course emphasizes the modeling and computer programming perspective of simulation; design and implementation of simulation models. The fundamental concepts of object-oriented simulation are introduced. Model implementation will require programming in an object-oriented simulation language such as OOSimL, or in a general purpose programming language (Java or C++). Students will also be exposed to a commercial integrated simulation software tool: Arena.
Prerequisites: CS 3305 and CS 3305L
Credits: 3
CS 4712 - User Interface Engineering
A comprehensive study of techniques in design and implementation of user interfaces engineering. Topics include the foundation of human-computer interaction and interface related to software lifecycle, building a graphic user interface engineering, interaction devices and technologies, human-computer dialogue, cognitive models, usability, the design and development process, user interface management systems (UIMS), interface style and techniques, user learning, and diversity in interaction styles. Major research and the building of a working graphic user interface are included.
Prerequisites: CSE 1322 and CSE 1322L
Credits: 3
CS 4720 - Internet Programming
This course introduces current technologies for modeling, designing, implementing, and developing Web applications. Topics include developing for the server and the client, programming frameworks, server administration and integration with databases. Practice will involve platforms and language such as Linux, Python, PHP, Ruby and JavaScript.
Prerequisites: (CS 3305 and CS 3305L) and (CSE 3153 or CS 3410)
Credits: 3
CS 4722 - Computer Graphics and Multimedia
The basic principles and practices of interactive computer graphics and multimedia systems are covered in this introductory course. The design and implementation of state-of-the-art computer graphic rendering and visual multimedia systems are the main part of the course. The sub-topics of the course deal with specific input/output hardware devices and their technology, software and hardware standards, programming methods for implementing 3-dimensional graphical applications and interactive multimedia applications, and a study and evaluation of the effectiveness of graphic/multimedia communications. A large component of the class is the building of a large-scale application.
Prerequisites: CS 3305 and CS 3305L
Credits: 3
CS 4732 - Machine Vision
This course introduces concepts and techniques in machine vision. Students successfully completing this course will be able to apply a variety of image processing techniques for the design and analysis of efficient algorithms for real-world applications, such as optical character recognition, face detection and recognition, motion estimation, human tracking, and gesture recognition.
Prerequisites: CS 3305 and CS 3305L
Credits: 3
CS 4850 - Computer Science Senior Project
The course provides a capstone experience for CS majors to promote a successful transition to the work place or further academic study. Students will have the opportunity to practice essential project management skills and work with current software tools and technologies. Student teams will develop a project scope, project plan, document functional specifications, develop a design document, implement specified functions, provide weekly progress reports, give project presentations to the class, conduct final project presentation to the instructor and/or project sponsor, and provide a complete final report that includes documentation of all class activities. Each team will designate a team leader who is responsible for coordinating work tasks, team meetings, communications with the instructor and/or project sponsor, and team effort.
Description
Position:
