Introduction to the social implications of computing, Social implications of networked communication, Growth of, Control of, and access to the Internet, Gender – Related issues, Cultural issues, International Issues, Accessibility Issues (e.g. underrepresentation of minorities, Women and disabled in the computing profession), Public policy issues (e.g. electronic voting). Making and evaluating ethical arguments, Identifying and evaluating ethical choices, Understanding the social context of design, Identifying assumptions and values. Professional Ethics: Community values and the laws by which we live, The nature of professionalism (Including care, attention and discipline, fiduciary responsibility, and mentoring). Keeping up-to-date as a professional (in terms of knowledge, tools, skills, legal and professional framework as well as the ability to self-assess and computer fluency), Various forms of professional credentialing and the advantages and disadvantages, Codes of ethics, conduct, and practice(IEEE, ACM, SE, AITP, and so forth), Dealing with harassment and discrimination, Historical examples of software risks (such as the Therac-25 case), Implications of software complexity, Risk assessment and Risk Management; Risk removal, risk reduction and risk control. Security Operations: Physical security, Physical access controls, Personnel access controls, Operational security, Security polices for systems/networks, Recovery and Response, Dealing with problems (both technical and human). Foundations of Intellectual Property, Copyrights, patents, and trade secrets, Software Piracy, Software Patents, Transactional issues concerning Intellectual Property. History and examples of computer crime, “Cracking” (“Hacking”) and its effects, Viruses, Worms, and Trojan Horses, Identity Theft, Crime Prevention strategies.
Course Catalogue
Advancement of AI technology has enabled more expansion of its application area. From medical treatment, gaming, manufacturing to daily business processes. Huge amount of money has been poured in AI research due to its exciting discoveries. However, the rapid growth and excitement that the technology offers obscure us from looking at the impact it brings in our society. This course gives an introduction to AI & Machine Learning and discusses various impacts of AI & ML in our lives and society today.
This course will work on building the entrepreneurial mindset and introducing basic entrepreneurship principles, technology innovation and creativity through interactive lectures, workshops, and case studies in contemporary issues to include energy, life sciences, healthcare, social issues to be tackled through technology. This course will help students learn the processes and skills needed to launch and manage new technology ventures, with a focus on business plan development. Topics related to critical legal and business issues entrepreneurs face as they build and launch a new technology venture will be covered. Attention will be placed on new venture formation, intellectual property management, and financing arrangements to establish a tech startup.
Data analysis is an evolving area of studies with focus on various predictive modeling techniques coupled with ample analytical tools that help increase our capacity to handle data. This course helps students to develop a basic understanding of data analysis that they will need to make decisions using data, and to communicate the results effectively. The course is an introduction to the key concepts of statistics for data analysis, essential tools and methodologies for predictive modeling tasks.
Introduction to IT project management concepts and frameworks; the role of the project manager; conceptualising and visualising projects in terms of the overall cycle from initiation through to sign off; project management processes and knowledge areas; project management tools and techniques; interfaces between the project manager and various stakeholders; working in teams; career planning.
This course will introduce you to the world of Augmented, Mixed, and Virtual Reality interfaces. These interfaces enable new kinds of user experiences by superimposing digital content onto the user’s real world view or creating fully immersive virtual world experiences. You will learn about the differences between AR/VR, about the technical and design requirements for creating such user experiences, and how to prototype and develop your first AR/VR interfaces. You will also receive an overview of new and evolving interaction design principles and methods, current AR/VR interface development approaches, and how to assess the usability of AR/VR interfaces.
Designing an Internet utilizing a range of different technologies. Simplifying the creation and updating web content. Expanding Intranet services by adding client-slide and server-side processing. Interfacing Internet to a database. Querying a database using Cold Fusion.
History of Computing, Social context of computing, Methods and tools of analysis, Professional and ethical responsibilities, Risks and liabilities of computer-based systems, Intellectual property, Privacy and civil liberties, Computer crime, Economic issues in computing, Philosophical frameworks.
Final Year Project/Internship is a subject that must be completed by final year student as a requirement to receive a Bachelor of Science (BSc) degree in Computer Science and Engineering. In this subject the student will be given one semester to work on a task that is related to their field of interest. Students are expected to do their work independently most of the time. But their progress will be monitored closely by their supervisors. At the end of the project/internship, students should document their work in a thesis which must be hard bounded and submitted to the department. Students are also required to submit a soft copy of their thesis to the department.
Important Documents:
Detailed Syllabus
Internship Report Outline
Research Project Report Outline
Supervisor Evaluation form
Examiners Evaluation form
Feedback Form – Industry Supervisor
Circuit variables and elements: Voltage, current, power, energy, independent and dependent sources, resistance. Basic laws: Ohm’s law, Kirchhoff’s current and voltage laws. Simple resistive circuits: Series and parallel circuits, voltage and current division, Wye-delta transformation. Technique of circuit analysis: Nodal and Mesh analysis. Network theorems: Source transformation, Thevenin’s, Norton’s and superposition theorems. Maximum power transfer condition and reciprocity theorem. Energy storage elements: Inductors and capacitors, series and parallel combination of inductors and
capacitors. Response of RL, RC, and RLC circuits: transient and steady state responses. Basic magnetic circuits: magnetic quantities and variables: Flux density, magnetomotive force, magnetic field strength, permeability and B-H curve, reluctance. Laws in magnetic circuits: Ohm’s law and Ampere’s circuital law. Magnetic circuits: Composite series magnetic circuit, parallel, and series-parallel circuits. Analogy between electrical and magnetic circuits. Hysteresis loss and magnetic materials.