Degree Course Descriptions

Required Degree Program Courses

Course Number: DSCS701
Course Title: Programming with Python
Credit Hours: 3
Type: Required
Description: Students will learn the core concepts of Python, and more advanced Python programming with a focus on enterprise development, students will learn more advanced Python programming with a focus on enterprise development. Students will use Python to interact with databases and GUI’s and perform Network Programming. This is a practical, hands-on course, designed to teach students practical programming for the real business application.


Course Number: DSCS702
Course Title: Data Mining
Credit Hours: 3
Type: Required
Description: The Data Mining course provides a practical and technical introduction to knowledge discovery and data mining. The topics that will be covered include problems of data analysis in databases, discovering patterns in the data, and knowledge interpretation, extraction and visualization. Additionally, data mining and machine learning techniques used for descriptive and predictive analysis, such as clustering association rules mining, classification, prediction, will be covered. This course is an absolute necessity for those interested in joining the data science workforce, and for those who need to obtain more experience in data mining.


Course Number: DSCS703
Course Title: Linux and Networking Security
Credit Hours: 3
Type: Required
Description: The Linux and Networking Security course focuses on configuring a secure Linux network using command line and graphical utilities. Emphasis is placed on file sharing technologies such as the Network File System, NetWare’s NCP file sharing, and File Transfer Protocol. Additional topics include making data secure, user security, file security, and network intrusion detection. Students will be required to take on the role of problem solvers and apply the concepts presented to situations that might occur in a work environment.


cCourse Number: DSCS704
Course Title: Deep Learning
Credit Hours: 3
Type: Required
Description: Deep learning is a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. For example, if asked to recognize faces, a deep neural network may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and ears, and, finally, individual face identities. Deep learning is behind many recent advances in AI, including Siri’s speech recognition, Facebook’s tag suggestions and self-driving cars. This course will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains like speech recognition and computer vision.


Course Number: DSCS705
Course Title: Security Management
Credit Hours: 3
Type: Required
Description: In the Security Management course, students will examine information security as a risk management problem where the organization identifies information security risks, evaluates those risks, and makes risk mitigation and acceptance decisions given its resource constraints. Part one of this class covers foundational concepts in risk management and economic valuation and will be introduced to standard risk management approaches for identifying, analyzing, and responding to risk, as well as the tools and methodologies for metrics to monitor risk management activities. Part two of the course expands coverage to more quantitative approaches to risk analysis, risk valuation, and risk metrics using Factor Analysis of Information Risk (FAIR) and an associated analysis software toolset called Risk Lens.


Course Number: DSCS706
Course Title: Big Data Analysis
Credit Hours: 3
Type: Required
Description: The Big Data Analysis course provides the data science students with an understanding of the Big Data and its role in data analysis. It provides the terminology and core concepts behind big data problems, applications, and systems. It affords introduction to one of the most common frameworks, Hadoop and Spark, that have made big data analysis easier and more accessible. Also, it will provide you with the necessary skill in manipulating big data distributed over a cluster using functional concepts and in-memory distributed collections framework written in Scala or Spark. We'll cover Spark's programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential collections. Through hands-on examples in Spark and Scala, students learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance.


Course Number: DSCS799
Course Title: Capstone Project
Credit Hours: 3
Type:  Required
Description: The Capstone Project course will enable students to apply and coordinate their acquired knowledge, then deepen it through a specified theory-based capstone project. The Capstone Project will allow students to showcase their skills and abilities, use scientific methodology, and showcase their academic competence in various ways in preparation to transition from the academic to the industry environment.

Elective Courses

Course Number: DSCS707
Course Title: Secure Programming
Credit Hours: 3
Type: Elective
Description: The Secure Programming course provides the essential and fundamental skills for secure programming. The most prevalent reason behind vulnerabilities and buggy code being exploited by hackers and malicious code is the lack of adoption of secure coding practices. This course will expose students to the inherent security drawbacks in various programming languages or architectures. Further, they will learn strategies to exercise secure programming practices to overcome these inherent drawbacks and preempt bugs from the code.


Course Number: DSCS708
Course Title: Network Security
Credit Hours: 3
Type: Elective
Description: The Network Security course explores the basic components and design principles of advanced broadband networks (wireline and wireless) and how they enable essential services such as mobility, secure data storage, processing and transmission. This course will also introduce the student to emerging issues facing organizations considering implementing cloud computing services and mobility to enable worker productivity. Students will be exposed to the basic pillars of network security and protecting individual privacy.


Course Number: DSCS709
Course Title: Mobile Forensics
Credit Hours: 3
Type: Elective
Description: The Mobile Forensics course focuses on the intricacies of manual acquisition (physical vs. logical) and advanced analysis using reverse engineering to understand how popular Mobile OSs are hardened to defend against common attacks and exploits. Topics include; mobile forensic challenges and process, mobile hardware design and architectures, OS architecture, boot process, and file systems, threats and security, evidence acquisition and analysis, application reverse engineering, and mobile forensics reporting and expert testimony. Through this course, students will be able to extract information from mobile devices, and validate the results of mobile forensics solutions, Demonstrate various mobile enabling technologies, forensics process, methods, techniques and tools.


Course Number: DSCS710
Course Title: Cyber Law
Credit Hours: 3
Type: Elective
Description: The Cyber Law course focuses on cyber-attack prevention, planning, detection, and incident response with the goal of counteracting cybercrime, cyber terrorism, and cyber predators, and holding perpetrators accountable. Topics include fundamentals of computer forensics, forensic duplication and analysis, network surveillance, intrusion detection and response, incident response, anonymity, computer security policies and guidelines, and case studies.


Course Number: DSCS711
Course Title: Artificial Intelligence
Credit Hours: 3
Type: Elective
Description: The Artificial Intelligence course is divided into four parts. The first part covers knowledge representation. The second part introduces heuristic search and constraint satisfaction. The third part is dedicated to advanced topics such as rule-based Expert Systems, case-based reasoning, and model-based reasoning. Finally, the fourth part is dedicated to machine learning techniques and theory. Through this course students will be able to differ between supervised, unsupervised and reinforcement learning, be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.


Course Number: DSCS712
Course Title: Research and Writing for the IT Practitioner
Credit Hours: 3
Type: Elective
Description: The Research and Writing for the IT Practitioner course provides 21st Century IT practitioners with the knowledge and skill to read, understand, implement, and conduct research in the workplace. Emphasis will be placed on data driven analysis, understanding credibility of resources, and determining data validity. Students will design and conduct a research project in the course, write an analysis document, and present their project to other students.