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Online Computer Science Courses

Curriculum Details

30 total credits required

Our cutting-edge Master of Science in Computer Science program features a curriculum that combines application-oriented projects with industry-informed coursework. As a student, you can gain the skills employers want and apply what you learn in real-time while building relevant skills for the evolving business and technology landscape.

Lebanese American University’s online computer science courses are delivered in a flexible, asynchronous format. The program is designed to be completed in two years. A wide range of electives are built into the curriculum so you can customize your studies and pursue focus areas in artificial intelligence, computer networks, secure computing or general computer science.

This 30-credit program consists of 10 computer science courses that provide an advanced study of topics like software quality assurance, data science, database systems and more. You will take five core courses (18 credits) followed by a range of electives (12 credits).

When you graduate, you’ll have the technical expertise to meet the global demand for skilled computer science and data analysis professionals.

Core

This course addresses both the fundamentals and the research boundaries of algorithm design and analysis. Covered topics include: complexity of algorithms, divide and conquer techniques, greedy methods, dynamic programming, recursive backtracking, amortized analysis, graph algorithms, polynomial-time problem reduction, NP-completeness, approximation algorithms and a selected advanced topic.

This course will cover essential topics in data collection and preprocessing, accelerated data science, scalable and distributed computing, GPU-accelerated machine learning, data visualization and graph analytics. Topics will include introduction to Data Science, data collection and pre-processing, data ethics and bias in data sets, data integration and analytics, data visualization, classification using machine learning, clustering and dimensionality reduction, and neural networks.

This course covers several advanced topics in databases and modern data-intensive systems. Topics include advanced concurrency control techniques, query processing and optimization strategies for relational database systems, advanced indexing methods, parallel and distributed database systems, NoSQL, database-as-a-service (DB clouds), data on the web, data replication, and topics in database security and privacy.

The course covers the capability maturity model; process assessment, modeling, and improvement techniques; maintenance; quality; metrics; theoretical and practical aspects of testing software, including classical and object oriented testing methods at various testing levels.

The course covers the fundamental aspects, functions, and techniques computer networks. Topics include internetworking: Packet-based communications; network routing; TCP/IP networks and addressing; transmission protocols: TCP and UDP; resource management and congestion control; wireless and datacenter networks, software-defined networks, and programmable data-planes; content distribution networks, peer-to-peer networks, video streaming; network security including firewalls and application gateways.

This course entails an independent development, and documentation of substantial software, or computer-based system, using recent or significant techniques and/or tools.

Computer Networks

This course covers theory and practice of network security. Topics include static packet filter, stateful firewall, proxy firewall, IDS, VPN Device, DMZs and screened subnets, networks defence components, internal network security, host hardening, configuration management, audit, human factors, and security policies. The course also covers cryptographic protocols, privacy, anonymity and various case studies.
This course covers methods and tools used for network programming and simulation. Covered topics include operating system support for network protocols, inter-process communication tools (such as pipes, sockets and remote procedure calls), design of client and server sides of network protocols, simulation tools for network design and analysis, in addition to experimental research topics in the area of computer networks.

This course exposes students to the fundamentals of IoT as a paradigm in addition to the foundational problems inherent in this realm. The course will introduce the basic terminology and ecosystem, plus development environments. Topics include IoT hardware and software platforms, data collections and analytics for IoT, security and ethical issues inherent in IoT, and networks programming for IoT.  The course explores problem solving for IoT analytics based on machine learning and deep learning using TensorFlow.  The course will run as a seminar-style readings, discussions, labs, and presentations by the students.   Students will have a semester-long project using a Raspberry PI 3.

This course covers the fundamental principles of pervasive and mobile computing in addition to the design of state-of-the-art wireless technologies and wireless networking protocols. Topics include pervasive and mobile computing fundamentals and challenges; pervasive and mobile computing services and application areas; mobile device technologies; mobile device platforms; mobile device application development challenges; mobile device programming; wireless network architectures; WLAN, WiMAX, GSM, UMTS, and Bluetooth wireless technologies; Mobile IP; Wireless TCP; pervasive computing and wireless networking research trends.

Students must choose CSC637 Pervasive Computing and Wireless Networking or CSC638 Advanced Topics in Computer Networks.

This course covers one or more selected topics in the field of wired and wireless networking. It explores state-of-the-art advances and offers students research and development experience. This course can be repeated for credits more than once.

Students must choose CSC637 Pervasive Computing and Wireless Networking or CSC638 Advanced Topics in Computer Networks.

Artificial Intelligence

Course description details coming soon.

This course provides an overview of popular algorithms in machine learning. Topics include supervised learning, linear and polynomial regression, classification algorithms, gradient descent, unsupervised learning, instance-based learning, neural networks, and genetic algorithms and boosting. The course requires some knowledge of artificial intelligence, and good programming skills. The theoretical aspects of the algorithms will be studied, and assignments will be given to test their applicability.
This course covers the fundamental techniques and applications for mining large data sets. Topics include techniques and algorithms for classification, clustering, association rules, scalable and distributed data mining algorithms, and applications.

The course covers computational approaches for modeling uncertainty and solving decision problems. Topics include search techniques, constraint satisfaction problems, game playing (including alpha-beta pruning), propositional logic, predicate logic, knowledge representation, planning, probabilistic reasoning, Bayesian networks and case-based reasoning. It also covers advanced topics in Artificial Intelligence such as natural language processing and robotics.

Students must choose CSC660 Artificial Intelligence: Principles and Techniques or CSC688 Advanced Topics in Artificial Intelligence.

This course covers selected advanced or emerging topics and state of the art methods in Artificial Intelligence.

Students must choose CSC688 Advanced Topics in Artificial Intelligence or CSC660 Artificial Intelligence: Principles and Techniques.

Secure Computing

Course description details coming soon.

This course covers theory and practice of network security. Topics include static packet filter, stateful firewall, proxy firewall, IDS, VPN Device, DMZs and screened subnets, networks defence components, internal network security, host hardening, configuration management, audit, human factors, and security policies. The course also covers cryptographic protocols, privacy, anonymity and various case studies.
The course provides a basic knowledge of digital forensic examinations, and shows how evidential findings are applied within criminal and civil cases. Topics include and overview of the tools and techniques used, the types of digital storage media likely to be encountered, and clear explanations of the terminology and software commonly found within cases involving computer evidence. The course also highlights the areas of law most relevant to cases involving digital forensic evidence. It reviews the topics covered in relation to an actual case. This part of the study assesses the documentary evidence paper trail, the forensic examination, findings, and the eventual outcome of the case.

This course covers advanced theory, techniques and practices of security modeling and engineering at different layers of computer systems.

Students must choose CSC639 Secure System Modeling and Engineering or CSC688 Advanced Topics in Secure Computing.

This course provides a study of advanced techniques in computer security. It covers rigorous processes and practices for managing secure systems and software operations.

Students must choose CSC688 Advanced Topics in Secure Computing or CSC639 Secure System Modeling and Engineering.

General Pathway

Any 12 credits from the choice of elective modules above.

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