Master's of Information Technology Syllabus

Program Overview

The Masters in Information Technology (MIT) curriculum is designed by closely following the courses practiced in accredited international universities, with the assumption that intake students are mostly from Bachelor's programmes in Information Technology, Computer Science, Computer Engineering, Computer Application, Information Management, or Information Systems.

MIT provides the knowledge, understanding, and research skills to solve real-world problems with cutting-edge technology. The programme develops deep theoretical and practical knowledge in specific areas of information technology, giving graduates the intellectual and conceptual foundation to play leading roles in the IT industry. The curriculum covers standard core and elective IT courses, plus courses supporting both research and development in fast-changing computer technologies. All graduate students must complete at least 59 credit hours and may complete a maximum of 62 credit hours. The programme is run under the Institute of Science and Technology (IOST).

Objective: To deliver comprehensive education in the principles and practices of information technology, providing students with depth knowledge and research skills across theories, programming practices, and application of computers.

Admission Requirement

Applicants must have completed a Bachelor of Information Technology degree offered by TU or its equivalent, and submit a completed admission form as required by the general university rules. Students are selected based on scores in the entrance test conducted by the admitting college. The programme also admits students holding a Bachelor of Computer Science, Bachelor of Computer Science and Information Technology, Bachelor of Information Technology, Bachelor of Engineering in Computer and Electronics, Bachelor of Information Management, Bachelor of Information Systems, or Bachelor of Computer Application, or their equivalent.

Evaluation

All courses, seminars, Literature Review Research, and Project/Thesis carry an internal weightage of 40% and an external weightage of 60%. A student must secure a minimum of 50% overall weightage to pass a course. The final grade point in each course is the sum of the overall weightage across all categories.

The Seminar, Literature Review Research, and Project/Thesis are evaluated by different evaluators. To pass, students must secure at least 50% marks from each evaluator, and the final grade point is the sum of all evaluations. An external examiner is assigned for the evaluation of final presentations.

Grading System

The grade awarded in each course is based on overall performance through internal and external evaluations. Internal evaluation uses continuous assessment criteria; external evaluation is based solely on the examination conducted by the Institute of Science and Technology (IoST).

Letter Grade	Grading Scale	Grade Point	Performance Remarks
A	90 – 100	4	Distinction
A-	80 – less than 90	3.7	Very Good
B+	70 – less than 80	3.3	First Division
B	60 – less than 70	3	Second Division
B-	50 – less than 60	2.7	Pass in individual subject
F	0 – less than 50	0	Fail

The performance of each student in each semester is evaluated in terms of the Semester Grade Point Average (SGPA) — the grade point average for the semester:

$$\text{SGPA} = \dfrac{\text{Total grade points earned in a semester}}{\text{Total number of credits earned in the semester}}$$

The Cumulative Grade Point Average (CGPA) is the grade point average across all completed semesters:

$$\text{CGPA} = \dfrac{\text{Total grade points earned}}{\text{Total number of credits completed}}$$

Final Examination

The Institute of Science and Technology, Tribhuvan University, conducts the final examination at the end of each semester for every course except Seminar, Literature Review Research, and Project/Thesis. This final examination carries 60% of the overall weightage.

Course Structure

Semester I

Code	Course Title	Credit Hours	Full Marks
MIT501	Object Oriented Analysis and Design	3	75
MIT502	Advanced Database System	3	75
MIT503	Enterprise Applications	3	75
MIT504	Information Security	3	75
MIT505	Programming Language	3	75
	Total	15	375

Semester II

Code	Course Title	Credit Hours	Full Marks
MIT551	Distributed and Cloud Computing	3	75
MIT552	Digital Marketing	3	75
MIT553	IT Project Management	3	75
MIT554	Intelligent Computing	3	75
MIT555	Seminar	1	25
	Elective I	3	75
	Total	16	400

List of Electives (Elective I):

1. Internet of Things (MIT556)
2. Data Structures and Algorithms Analysis (MIT557)
3. E-commerce and E-business (MIT558)
4. Advanced Programming (MIT559)

Semester III

Code	Course Title	Credit Hours	Full Marks
MIT601	Cybersecurity and Digital Forensics	3	75
MIT602	Data Analytics and Visualization	3	75
MIT603	Digital Governance	3	75
MIT604	Term Paper	2	50
	Elective II	3	75
	Total	14	350

List of Electives (Elective II):

1. Digital Economy (MIT605)
2. Mobile Computing (MIT606)
3. Human Computer Interaction (MIT607)
4. Database Administration (MIT608)

Semester IV

Code	Course Title	Credit Hours	Full Marks
MIT651	IT Entrepreneurship and Innovation	3	75
MIT652	Thesis/Project	8	200
	Elective III	3	75
	Extra Elective	3	75
	Total	14	350

List of Electives / Extra Electives:

1. Social Network Analytics (MIT653)
2. Software Testing (MIT654)
3. Network and System Administration (MIT655)
4. Web Technology (MIT656)
5. International Business (MIT657)

MIT501 — Object Oriented Analysis and Design

Semester I · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: This course familiarizes students with the concepts of information systems development using object-oriented concepts, with special focus on the system development life cycle, development methodologies, and the phases of systems development — analysis, design, construction, installation, and operation.

Course Objectives: By the end of this course, students will be able to use different object-oriented concepts of information systems development to develop information systems using different methodologies, tools, and techniques.

Unit 1: Introduction (9 Hrs.)

Introduction; roles and skills of a systems analyst; systems development life cycle; systems development methodologies — structured design, rapid application development, object-oriented systems analysis and design, agile development, DevOps, custom methodologies; unified process; Unified Modeling Language; basic characteristics of object-oriented systems.

Unit 2: Analysis Modeling (18 Hrs.)

Requirements Determination: requirements determination and analysis approaches; requirements-gathering techniques; text analysis; requirements definition; system proposal.

Business Process and Functional Modeling: business process modeling with use case diagrams and activity diagrams; business process identification with use cases and use-case diagrams; business process documentation with use-case descriptions; verifying and validating business process and functional models.

Structural Modeling: structural models; object identification; CRC cards; class diagrams; structural models using CRC cards and class diagrams; verifying and validating structural models.

Behavioral Modeling: behavioral models; interaction diagrams; CRUDE analysis; behavioral state machines; verifying and validating behavioral models.

Unit 3: Design Modeling (12 Hrs.)

Moving on to Design: verifying and validating analysis models; evolving analysis models into design models; packages and package diagrams; design criteria and strategies; selecting acquisition strategy.

Class and Method Design: object design activities; constraints and contracts; method specification; verifying and validating class and method design.

Data Management Layer Design: object persistence formats; mapping problem-domain objects to object persistence formats; designing data access and manipulation classes; nonfunctional requirements and data management layer design; verifying and validating the data management layer.

Human Computer Interaction Layer Design: principles for user interface design; user interface design process; navigation design; input and output design; international and cultural issues; nonfunctional requirements and the HCI layer.

Physical Architecture Layer Design: elements of the physical architecture layer; infrastructure design; hardware and system software specifications; nonfunctional requirements; verifying and validating the physical architecture layer.

Unit 4: Construction, Installation, and Operation (6 Hrs.)

Construction: managing programming; developing documentation; designing tests.

Installation and Operation: cultural issues and IT adoption; conversion; change management; post-implementation activities.

Laboratory / Project Work: Students learn to use CASE tools and modeling tools to draw UML and related diagrams, and prepare a project report covering at least the analysis, design, and implementation phases of object-oriented system analysis and design, using any suitable database, programming, and interfacing technologies.

References

• Dennis, A., Wixom, B. H., & Tegarden, D. (2021). Systems analysis and design — An object-oriented approach with UML (6th ed.). Wiley.
• Wazlawick, R. S. (2014). Object-oriented analysis and design for information systems: Modeling with UML, OCL, and IFML. Morgan Kaufmann.
• Bennett, S., McRobb, S., & Farmer, R. (2010). Object-oriented system analysis and design using UML (4th ed.). McGraw-Hill.
• Valacich, J. S., & George, J. F. Modern systems analysis and design (9th ed.). Pearson.

MIT502 — Advanced Database System

Semester I · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: This course introduces advanced database concepts, covering object and object-relational databases, query processing and optimization, distributed databases, NOSQL databases, big data storage, big data technologies, active/temporal/spatial/multimedia/deductive databases, and information retrieval and web search.

Course Objectives: To familiarize students with advanced concepts of database systems so they can apply them to solve problems related to database systems.

Unit 1: Object and Object-Relational Databases (5 Hrs.)

Overview of object-oriented concepts; object database extension to SQL; the ODMG object model and the Object Definition Language (ODL); object database conceptual design; the Object Query Language (OQL).

Unit 2: Query Processing and Optimization (11 Hrs.)

Translating SQL queries into relational algebra and other operators; algorithms for external sorting; algorithms for the SELECT operation; implementing the JOIN operation; algorithms for PROJECT and set operations; implementing aggregate operations and different types of joins; combining operations using pipelining; parallel algorithms for query processing (operator level, intraquery, interquery).

Query trees and heuristics for query optimization; choice of query execution plans; use of selectivities in cost-based optimization (cost components, catalog information, histograms); cost functions for SELECT and JOIN operations; additional issues in query optimization; query optimization in data warehouses.

Unit 3: Distributed Database Concepts (7 Hrs.)

Distributed database concepts; data fragmentation, replication, and allocation techniques for distributed database design; overview of concurrency control and recovery, and transaction management, in distributed databases; query processing and optimization in distributed databases; types of distributed database systems; distributed database architectures; distributed catalog management.

Unit 4: NOSQL Databases and Big Data Storage Systems (6 Hrs.)

Introduction to NOSQL systems; characteristics and categories of NOSQL systems; the CAP theorem; document-based NOSQL systems and MongoDB; NOSQL key-value stores; column-based/wide-column NOSQL systems; NOSQL graph databases and Neo4j.

Unit 5: Big Data Technologies Based on MapReduce and Hadoop (5 Hrs.)

Introduction to big data; introduction to MapReduce and Hadoop; Hadoop Distributed File System (HDFS); MapReduce runtime; joins in MapReduce; Apache Hive; YARN.

Unit 6: Enhanced Data Models — Active, Temporal, Spatial, Multimedia, and Deductive Databases (5 Hrs.)

Active database concepts and triggers; temporal database concepts; spatial database concepts; multimedia database concepts; introduction to deductive databases.

Unit 7: Introduction to Information Retrieval and Web Search (6 Hrs.)

Information retrieval concepts; retrieval models; types of queries in information retrieval systems; text preprocessing; inverted indexing; evaluation measures of search relevance; web search and analysis; trends in information retrieval.

Laboratory Works: implementing the concepts of each unit using appropriate platforms.

References

• Elmasri, R., & Navathe, S. (2017). Fundamentals of database systems (7th ed.). Pearson Education.
• Korth, H., Silberschatz, A., & Sudarshan, S. Database system concepts (7th ed.). McGraw-Hill.
• Ramakrishnan, R., & Gehrke, J. Database management systems. McGraw-Hill.
• Rob, P., & Coronel, C. Database systems: Design, implementation and management. Thomson Learning.
• Date, C. J. Introduction to database systems. Pearson Education.

MIT503 — Enterprise Application

Semester I · Theory + Practical · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: A general introduction to the concept of enterprise architecture — its meaning, place and role in the overall organizational context, key constituting elements and core mechanisms of an EA practice, and the business value and benefits of using enterprise architecture in organizations, with a focus on EA artifacts as the core elements of an EA practice.

Course Objectives: To give an overall and complete understanding of enterprise architecture and its best practices.

Unit 1: Introduction (4 Hrs.)

The role of information technology in modern organizations; benefits and threats of IT in organizations; business value of IT; the business-and-IT alignment problem and solutions.

Unit 2: Enterprise Application Architecture (10 Hrs.)

Concepts of enterprise architecture; roles and practice of enterprise architecture; architecture functions in organizations; historical origin and best modern practices; enterprise architecture practice as city planning; enterprise architecture artifacts; the CSVLOD model.

Unit 3: Process of Enterprise Architecture (10 Hrs.)

The dialog between business and IT; the enterprise architecture uncertainty principle; processes constituting enterprise architecture practice; a high-level process view of enterprise architecture practice.

Unit 4: The CSVLOD Model of Enterprise Architecture (15 Hrs.)

Dimensions for classifying enterprise architecture artifacts; considerations, standards, visions, landscapes, outlines, and designs as general types of enterprise architecture artifacts; the continuous nature of the CSVLOD taxonomy for EA artifacts.

Unit 5: Enterprise Architecture Practices (6 Hrs.)

Architects in enterprise architecture practice; roles and structure of architecture functions in organizations; modeling languages for enterprise architecture; establishing enterprise architecture practices in organizations; maturity of enterprise architecture practice.

Laboratory Works: Students prepare a case study of a recent enterprise application framework used in an organization.

References

• Kotusev, S. (2021). The practice of enterprise architecture: A modern approach to business and IT alignment. SK Publishing.

MIT504 — Information Security

Semester I · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: This course introduces the concepts of information security, covering cryptosystems, message authentication, digital signatures, database and data center security, denial-of-service attacks, intrusion detection, security administration, and digital forensics.

Course Objectives: To familiarize students with information security concepts so they can understand and apply best practices for securing information and computer systems.

Unit 1: Information Systems Security (4 Hrs.)

Information systems security; tenets of information systems security; domains of IT infrastructures; IT security policy framework; data classification standards.

Unit 2: Private and Public Cryptosystem (8 Hrs.)

Block ciphers, AES, IDEA; stream ciphers, RC4, stream ciphers using feedback shift registers; ElGamal; elliptic curve cryptography; format-preserving encryption; overview of homomorphic encryption; lightweight cryptography and post-quantum cryptography.

Unit 3: Message Authentication Codes and Digital Signature (8 Hrs.)

Message authentication code; HMAC; data authentication algorithm; cipher-based MAC; digital signature; ElGamal digital signature scheme; Schnorr digital signature scheme; digital signature algorithm; elliptic curve digital signature algorithm; RSA-PSS digital signature algorithm.

Unit 4: Database and Data Center Security (4 Hrs.)

Database security; SQL injection attacks; database access control; inference; database encryption; data center security.

Unit 5: Denial of Service Attacks (6 Hrs.)

Denial-of-service attacks; flooding attacks; distributed denial-of-service attacks; application-based bandwidth attacks; reflector and amplifier attacks; defending against and responding to denial-of-service attacks.

Unit 6: Intrusion Detection and Prevention (6 Hrs.)

Intruders; intrusion detection; intrusion detection analysis approaches; host-based and network-based intrusion detection; hybrid intrusion detection; intrusion detection exchange format; honeypots; intrusion prevention systems.

Unit 7: Security Operations and Administrations (5 Hrs.)

Security administration; compliance; professional ethics; infrastructure for IT security policy; data classification standards; configuration management; change management process; application software security; software development and security.

Unit 8: Digital Forensics (4 Hrs.)

Digital forensics; computer crime; forensic methods and lab; collecting, seizing, and protecting evidence; recovering data; operating system forensics; mobile forensics.

Laboratory Works: implementing and simulating the concepts of each unit using appropriate platforms and tools.

References

• Kim, D., & Solomon, M. G. Fundamentals of information systems security (4th ed.). Jones & Bartlett Learning.
• Stallings, W. Cryptography and network security: Principles and practice (8th ed.). Pearson.
• Stallings, W., & Brown, L. Computer security: Principles and practice (4th ed.). Pearson.

MIT505 — Programming Language

Semester I · Theory + Practical · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: This course covers the procedural and object-oriented aspects of the Python programming language, along with a detailed discussion of various libraries and their applications in data processing tasks.

Course Objectives: To provide knowledge of procedural and object-oriented programming using Python and apply it to data processing tasks.

Unit 1: Procedural Python (12 Hrs.)

Tokens, reserved words, identifiers, data types, variables and constants, literals, operators, operator precedence, escape sequences, numbers, comments; control flow — conditional statements, ternary operator, loops, jump statements; functions — defining and calling functions, passing arguments, returning values, global and local variables, recursive functions, anonymous functions, lambda expressions; strings — functions, concatenation, operations, slicing, formatters; lists, tuples, sets, and dictionaries — functions, methods, and operations of each data structure.

Unit 2: Object Oriented Python (10 Hrs.)

Class, object, constructors, access modifiers, static methods, method overloading, operator overloading, inheritance, method overriding, abstract classes; enumerations, exception handling, file handling, regular expressions.

Unit 3: Libraries (10 Hrs.)

NumPy: basics, array and vectorized processing, operations between arrays and scalars, slicing and indexing, multi-dimensional arrays, data processing with arrays, array objects and functions, file input/output with arrays, linear algebra with arrays, random number generation.

Pandas: data structures, essential functionalities, summarizing and computing descriptive statistics, handling missing data, hierarchical indexing.

Matplotlib: introduction, plotting functions in pandas, plotting maps, the Python visualization tool ecosystem.

Unit 4: Data Processing (13 Hrs.)

Data loading, storage, and file formats: reading and writing data in text and binary formats; interacting with HTML and web APIs; interacting with databases.

Data wrangling: combining and merging data sets; reshaping and pivoting; data transformation.

Data aggregation and group operations: GroupBy mechanics; data aggregation; group-wise operations and transformations; pivot tables and cross-tabulation.

Laboratory Works: Students write Python programs using procedural and object-oriented approaches, and use the libraries discussed in class to solve data processing problems.

References

• AMZ Press. (2022). Python programming for beginners: The ultimate guide for beginners to learn Python programming.
• Singh, A. (2022). Master Python programming: Learn Python like never before.
• McKinney, W. (2017). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython (2nd ed.). O'Reilly.
• Zingaro, D. (2021). Learn to code by solving problems: A Python programming primer. No Starch Press.
• Codeone Publishing. (2022). Python programming for beginners: The #1 Python programming crash course to learn Python coding well and fast.

MIT551 — Distributed and Cloud Computing

Semester II · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Introduces the concepts of distributed and cloud computing, including cloud computing, cloud service models, parallel and distributed computing, cloud networks, cloud resource management and scheduling, concurrency in the cloud, and emerging concepts in cloud computing.

Course Objectives: To familiarize students with distributed and cloud computing concepts so they can use and develop distributed and cloud computing models.

Unit 1: Introduction (4 Hrs.)

Cloud computing; impact of cloud computing; ethical issues in cloud computing; factors affecting cloud computing service availability; network-centric computing and network-centric content; virtualization and cloud computing; types of virtualization.

Unit 2: Cloud Ecosystem (6 Hrs.)

Cloud computing delivery models and services; AWS, Google Cloud, Azure, IBM Cloud; cloud storage diversity and vendor lock-in; cloud interoperability; service and compliance level agreements; user challenges and experience; challenges in cloud computing.

Unit 3: Parallel and Distributed Computing (5 Hrs.)

Introduction to parallel and distributed computing; elements of parallel computing; elements of distributed computing; technologies for distributed computing.

Unit 4: Cloud Access and Cloud Interconnection Networks (9 Hrs.)

Packet-switched networks and the internet; TCP congestion control; content-centric networks; software-defined networks; interconnection networks for computer clouds; multistage interconnection networks; storage area networks and Fiber Channel; scalable data center communication architectures; network resource management algorithms (fair queuing, class-based queuing); content delivery networks; vehicular ad hoc networks.

Unit 5: Cloud Resource Management and Scheduling (10 Hrs.)

Policies and mechanisms for resource management; scheduling algorithms for computer clouds; delay scheduling; data-aware scheduling; Apache Capacity Scheduler; start-time fair queuing; borrowed virtual time; cloud scheduling subject to deadlines; resource bundling and combinatorial auctions for cloud resources; resource management and dynamic application scaling; control theory and optimal resource management; two-level resource allocation architecture; feedback control on dynamic thresholds; autonomic performance managers; utility model for cloud-based web services.

Unit 6: Concurrency and Cloud Computing (8 Hrs.)

Concurrency; communication and concurrency; computational models; communicating sequential processes; bulk synchronous parallel model; model for multicore computing; modeling concurrency with Petri nets; process state; communication protocols and process coordination; logical clocks and message delivery rules; runs and cuts; threads and activity coordination; critical sections, locks, deadlocks, atomic actions; consensus protocol; load balancing.

Unit 7: Emerging Clouds (3 Hrs.)

Machine learning on clouds; quantum computing on clouds; vehicular clouds.

Laboratory Works: implementation and simulation of the concepts of each unit using appropriate platforms and tools.

References

• Marinescu, D. C. (2022). Cloud computing: Theory and practice (3rd ed.). Morgan Kaufmann.
• Buyya, R. K., Vecchiola, C., & Selvi, S. T. Mastering cloud computing: Foundations and applications programming. Morgan Kaufmann.

MIT552 — Digital Marketing

Semester II · Theory + Practical · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Introduces the concepts of digital marketing — its types, tools, and analytical aspects, and various digital marketing metrics.

Course Objectives: Upon completion, students should be able to understand digital marketing and differentiate it from traditional marketing; plan and execute digital marketing strategies in real-world scenarios; use various digital marketing methods effectively; and understand lead generation and the lead funnel.

Unit 1: Introduction to Digital Marketing (2 Hrs.)

Defining digital marketing; traditional marketing vs. digital marketing; relevance of digital marketing; the 3i principle.

Unit 2: Search Engine Marketing and Optimization (12 Hrs.)

Techniques to generate website traffic; techniques to increase visibility on the search engine results page (SERP); positioning in SERP; on-page and off-page optimization; pay-per-click; Google AdWords.

Unit 3: Social Media Marketing (10 Hrs.)

Types, forms, and stakeholders of SMM; understanding the right fit; various SMM strategies; analysis of social media.

Unit 4: Web Analytics (6 Hrs.)

Analyzing visitor behavior through reports based on traffic sources, referring sites, page views, and conversion rates; Google Analytics — concept, setup, monitoring, and analysis.

Unit 5: Email Marketing (4 Hrs.)

Email marketing process; design and contents; delivery and discovery of email marketing.

Unit 6: Strategy and Planning (4 Hrs.)

Digital marketing plan and structure; identification of tools and audience; defining objectives and activities; analysis and ROI.

Unit 7: Affiliate Marketing (2 Hrs.)

Concepts and examples of affiliate marketing.

Unit 8: Content Marketing (5 Hrs.)

Definition, history, and need for content marketing; business models and cases of content marketing; epic content marketing.

Laboratory Works: on-page and off-page optimization of a particular website using various tools; hands-on sessions on social media marketing with lead generation and PPC; Google Analytics setup, monitoring, and analysis.

References

• Dodson, I. (2016). The art of digital marketing: The definitive guide to creating strategic, targeted and measurable online campaigns. Wiley.
• Henneberry, R., & Deiss, R. Digital marketing for dummies (2nd ed.). John Wiley & Sons.
• Pulizzi, J. (2014). Epic content marketing: How to tell a different story, break through the clutter, and win more customers by marketing less. McGraw-Hill Education.

MIT553 — IT Project Management

Semester II · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Familiarizes students with managing IT projects systematically using project management concepts, focusing on evaluation, estimation, planning, risk management, managing people, managing contracts, and quality.

Course Objectives: By the end of this course, students will be able to use various project management skills while developing IT projects, and will be able to manage stakeholders and maintain project quality.

Unit 1: Introduction to Software Project Management (4 Hrs.)

Why software project management is important; what is a project; software projects vs. other types of projects; contract management and technical project management; activities covered by software project management; plans, methods, and methodologies; ways of categorizing software projects; stakeholders; setting objectives; project success and failure; what is management; management control; traditional vs. modern project management practices.

Unit 2: Project Evaluation and Program Management (5 Hrs.)

Project portfolio management; evaluation of individual projects; cost-benefit evaluation techniques; risk evaluation; program management; managing the allocation of resources within a program; strategic program management.

Unit 3: An Overview of Project Planning and Modern Project Approaches (3 Hrs.)

Detailed steps of project planning; agile methods; Extreme Programming (XP); Scrum; managing iterative processes.

Unit 4: Software Effort Estimation (5 Hrs.)

Where estimates are done; problems with over- and under-estimates; the basis for software estimating; software effort estimation techniques; bottom-up estimating; top-down approach and parametric models; expert judgement; estimating by analogy; function points Mark II; COSMIC full function points; COCOMO II — a parametric productivity model; cost estimation.

Unit 5: Activity Planning (5 Hrs.)

Planning objectives and timing; project schedules; projects and activities; sequencing and scheduling activities; network planning models; formulating a network model; adding the time dimension; the forward and backward pass; identifying the critical path; activity float; shortening the project duration; identifying critical activities; activity-on-arrow networks.

Unit 6: Risk Management (5 Hrs.)

Categories of risk; a framework for dealing with risk; risk identification; risk assessment; risk planning; risk management; evaluating risks to the schedule; applying the PERT technique; Monte Carlo simulation; critical chain concepts.

Unit 7: Resource Allocation (4 Hrs.)

Identifying and scheduling resources; publishing the resource schedule; the scheduling sequence.

Unit 8: Monitoring and Control (4 Hrs.)

Creating the framework; collecting the data; visualizing progress; earned value analysis; change control; software configuration management.

Unit 9: Managing Contracts (3 Hrs.)

Types of contract; stages in contract placement; typical terms of a contract; contract management.

Unit 10: Managing People in Software Environments and Working in Teams (4 Hrs.)

The Oldham-Hackman job characteristics model; becoming a team; decision making; organization and team structures; coordination dependencies; dispersed and virtual teams; communication genres; communication plans; leadership.

Unit 11: Software Quality (3 Hrs.)

The place and importance of software quality; product and process metrics with respect to project management; quality management systems; process capability models.

Laboratory / Project Work: Students use CASE tools for project management focusing on work breakdown structure, resource breakdown structure, estimation, and software configuration management, and prepare a case-study report on cost-benefit analysis, earned value analysis, and risk analysis.

References

• Hughes, B., Cotterell, M., & Mall, R. Software project management (5th ed.). Tata McGraw Hill Education.
• Ince, D., Sharp, I., & Woodman, M. Introduction to software project management & quality assurance. Tata McGraw Hill.
• Royce, W. Software project management: A unified framework. Addison-Wesley.

MIT554 — Intelligent Computing

Semester II · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Covers concepts related to computational intelligence applicable to machine learning, focusing on artificial neural networks, genetic algorithms, swarm optimization, and fuzzy systems.

Course Objectives: To provide fundamental knowledge of the concept of computational intelligence.

Unit 1: Introduction to Computational Intelligence (2 Hrs.)

Intelligent systems; computational intelligence; computational intelligence paradigms (ANN, evolutionary computation, swarm intelligence, artificial immune systems, fuzzy systems).

Unit 2: Neural Network (5 Hrs.)

Single-layer neural network: introduction to neural networks, Rosenblatt's neuron, perceptron training algorithm, perceptron convergence algorithm, activation functions.

Multi-layer neural network and backpropagation: universal approximation theory, backpropagation training algorithm, batch vs. online learning, cross-validation and generalization.

Radial basis function networks: definitions and examples, the interpolation problem, training algorithm, RBF network variations, kernel regression.

Unit 3: Fuzzy Logic (10 Hrs.)

Basic fuzzy set theory: natural language and formal models, fuzzy sets, interpretation and representation of fuzzy sets, fuzzy logic, operations on fuzzy sets, alpha cuts, the decomposition theorem, the extension principle, compensatory operators.

Fuzzy relations: fuzzy relations and propositions, crisp relations, fuzzy logic inference, fuzzy logic for real-valued inputs.

Fuzzy data analysis: fuzzy methods in data analysis, fuzzy clustering (fuzzy c-means), fuzzy classifiers (fuzzy k-nearest neighbors).

Fuzzy measures and fuzzy integrals: fuzzy measures, fuzzy integrals, training the fuzzy integrals.

Unit 4: Evolutionary Computation (8 Hrs.)

Introduction to evolutionary computation: generic evolutionary algorithms, biological and simulated evolution, representation (the chromosome), initial population, fitness function, selection (selective pressure, random/proportional/tournament/rank-based/Boltzmann selection, elitism, hall of fame), reproduction operators, stopping conditions.

Evolutionary optimization: global numerical optimization, combinatorial optimization, constraint-handling approaches, multi-objective optimization, dynamic and noisy environments, niching, strategy parameters and self-adaptation.

Elements of evolutionary algorithms: encoding of solution candidates (Hamming cliffs, epistasis, closedness of the search space), genetic operators.

Unit 5: Computational Swarm Intelligence (9 Hrs.)

Particle swarm optimization: basic principles of computational swarm intelligence; PSO (influence of parameters, turbulence factor, boundary handling, global-best and local-best PSO); social network structures; basic variations (velocity clamping, inertia weight, construction coefficient, synchronous vs. asynchronous updates, velocity models).

Single-solution particle swarm optimization: guaranteed convergence PSO, social-based PSO, hybrid algorithm, sub-swarm-based PSO, multi-start PSO algorithm, repelling models, binary PSO, multi-objective PSO.

Unit 6: Deep Learning (11 Hrs.)

Basic idea: introduction to deep learning; common architectural principles of deep networks (parameters, layers, activation functions, loss functions, optimization algorithm, hyperparameters).

Major architectures of deep networks: Generative Adversarial Networks; Convolutional Neural Networks (architecture, input layers, convolution layers, pooling layers, fully connected layers); Recurrent Neural Networks (architecture, modeling the time dimension, LSTM networks); Transformers — encoder-decoder architecture, issues with RNN encoder-decoders, attention mechanism.

Laboratory Works: Students implement different computational intelligence models studied in each unit and submit a small model at the end of the course.

References

• Engelbrecht, A. P. (2007). Computational intelligence: An introduction (2nd ed.). Wiley.
• Keller, J. M., Liu, D., & Fogel, D. B. (2016). Fundamentals of computational intelligence. Wiley.
• Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., & Steinbrecher, M. (2016). Computational intelligence: A methodological introduction (2nd ed.). Springer.
• Patterson, J., & Gibson, A. (2017). Deep learning: A practitioner's approach. O'Reilly.

MIT556 — Internet of Things

Semester II Elective I · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Covers the state of the art in communication, networking, and data collection technologies for the IoT, focusing on the design of IoT-based solutions for multi-discipline challenges and the fundamental building blocks and protocols in IoT.

Course Objectives: To provide students with the advanced competitive skills required to contribute to the development of the IoT.

Unit 1: Introduction to Internet of Things (8 Hrs.)

Definition and characteristics of IoT; physical design of IoT (things in IoT, IoT protocols); logical design of IoT (IoT functional blocks, communication models, communication APIs); IoT-enabling technologies; IoT levels and deployment templates.

Unit 2: IoT Design Methodology (8 Hrs.)

Purpose and requirement specification; process specification; domain model specification; information model specification; service specification; IoT level specification; functional view specification; operational view specification; device and system integration; application development.

Unit 3: IoT Sensing and Actuation (9 Hrs.)

Sensors, characteristics of sensors, sensor deviations, sensing types (scalar, multimedia, hybrid, virtual); actuators, actuator types (hydraulic, pneumatic, electric, thermal, mechanical, soft), actuator characteristics.

Unit 4: IoT Processing Topologies and Types (7 Hrs.)

Data format; processing topologies; IoT device design and selection considerations; processing offloading (offload location, offload decision making, offloading considerations); IoT connectivity technologies.

Unit 5: IoT Communication Technologies (6 Hrs.)

Infrastructure protocols; discovery protocols; data protocols; identification protocol; device management; semantics protocols.

Unit 6: IoT Analytics (7 Hrs.)

KNN, decision tree, random forest, K-means, agglomerative clustering, DBSCAN; performance metrics.

References

• Bahga, A., & Madisetti, V. (2014). Internet of things: A hands-on approach.
• Mishra, S., Mukherjee, A., & Roy, A. (2021). Introduction to IoT. Cambridge University Press.

MIT557 — Data Structures and Algorithms Analysis

Semester II Elective I · Theory + Lab · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Covers the basic foundations of data structures and algorithms with analysis, including stacks, queues, lists, trees, and graphs, plus algorithm analysis and optimization paradigms and parallel algorithms.

Course Objectives: To give a basic concept of data structures, depth in optimization paradigms, advanced algorithm design techniques, and a moderate understanding of computational complexity theory.

Unit 1: Data Structure (4 Hrs.)

Elementary data structures — abstract data type, basic operations on stack, queue, and linked list; height-balanced trees — AVL trees, red-black trees.

Unit 2: Foundation of Algorithm Analysis (5 Hrs.)

Algorithms and their properties — RAM model, time and space complexity, detailed analysis of algorithms, concept of aggregate analysis; asymptotic notations — Big-O, Big-Ω, and Big-Θ, geometrical interpretation and examples; recurrences — recursive algorithms and recurrence relations, solving recurrences (recursion tree method, substitution method, Master theorem).

Unit 3: Iterative Algorithms (3 Hrs.)

Searching algorithms — sequential search and its analysis; sorting algorithms — bubble, selection, and insertion sort and their analysis.

Unit 4: Divide and Conquer Algorithms (6 Hrs.)

Searching algorithms — binary search, min-max finding and their analysis; sorting algorithms — merge sort, quick sort (best/worst/average case), heap sort (heapify, build heap, heap sort), randomized quick sort; multiplication of large integers.

Unit 5: Greedy Paradigm (7 Hrs.)

Optimization problems and optimal solutions; elements of greedy strategy; fractional knapsack; job sequencing with deadlines; Kruskal's algorithm, Prim's algorithm, Dijkstra's algorithm and their analysis; Huffman coding — purpose, prefix codes, algorithm, and analysis.

Unit 6: Dynamic Paradigm (7 Hrs.)

Greedy algorithms vs. dynamic programming; DP algorithms — computing binomial coefficients, matrix chain multiplication, string editing, zero-one knapsack problem, optimal binary search tree, Floyd-Warshall algorithm, traveling salesman problem and their analysis.

Unit 7: Backtracking (2 Hrs.)

Concept of backtracking; recursion vs. backtracking; backtracking algorithms — subset-sum problem, zero-one knapsack problem, N-queen problem and their analysis.

Unit 8: Branch and Bound (2 Hrs.)

Assignment problem, knapsack problem, traveling salesman problem.

Unit 9: Introduction to Parallel Algorithms (8 Hrs.)

Parallel processing paradigms — semantics of concurrent programming (axiomatic, denotational, operational); PRAM models — PRAM algorithms (computing prefix sums, parallel sorting); parallel graph algorithms — tree graph algorithms (post-order numbering, computing the number of descendants, level computation, lowest common ancestor computation).

Unit 10: NP Completeness (2 Hrs.)

Complexity classes (P, NP, NP-Complete, NP-Hard); problem reducibility; approximation algorithms — vertex cover algorithm.

Laboratory Works: implementing the algorithms and analyzing their behavior with respect to time and memory.

References

• Langsam, Y., Augenstein, M. J., & Tenenbaum, A. M. Data structures using C and C++ (2nd ed.). PHI.
• Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. Introduction to algorithms (3rd ed.). PHI.

MIT558 — E-Commerce and E-Business

Semester II Elective I · Theory + Practical · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Introduces the fundamentals and infrastructure of e-Commerce and e-Business, including Internet business models, e-commerce infrastructure, e-payment systems, e-SCM, e-CRM, and e-procurement.

Course Objectives: Students are expected to understand recent developments in e-Business, apply e-Business strategically to enhance business processes, and apply skills in planning, designing, and restructuring business using digital tools.

Unit 1: Introduction to Digital Business and E-Commerce (6 Hrs.)

The impact of electronic communications on traditional businesses; difference between digital business and e-commerce; digital business opportunities; risks and barriers to digital business adoption; barriers to consumer Internet adoption.

Unit 2: Marketplace Analysis for E-Commerce (5 Hrs.)

Business and revenue models for e-commerce; online marketplace analysis; location of trading in the marketplace; business models for e-commerce; online start-up companies.

Unit 3: Managing Digital Business Infrastructure (6 Hrs.)

Digital business infrastructure components; introduction to Internet technology; management issues in creating a new customer-facing digital service; web services, SaaS, cloud computing, and service-oriented architecture (SOA); managing internal digital communications through intranets and extranets; web presentation and data exchange standards; Internet governance.

Unit 4: E-Environment (4 Hrs.)

Social and legal factors; environmental and green issues related to Internet usage; e-government; technological innovation and technology assessment.

Unit 5: Digital Business Strategy (6 Hrs.)

Digital business strategy; strategic analysis; strategic objectives; strategy definition; strategy implementation; aligning and impacting digital business strategies.

Unit 6: Supply Chain Management (6 Hrs.)

Supply chain management; value chain; options for restructuring the supply chain; using digital business to restructure the supply chain; supply chain management implementation; goal-setting and performance management for e-SCM.

Unit 7: E-Procurement (6 Hrs.)

E-procurement; drivers of e-procurement; estimating e-procurement costs; barriers and risks of e-procurement adoption; implementing e-procurement; B2B marketplaces; the future of e-procurement.

Unit 8: Customer Relationship Management (6 Hrs.)

e-CRM; conversion marketing; online buying process; customer acquisition management; marketing communications for customer acquisition (search engine marketing, online PR, online partnerships, interactive advertising, email marketing, social media marketing); social media and social CRM strategy; customer retention management; excelling in e-commerce service quality; customer extension; technology solutions for CRM.

Laboratory Works: designing and constructing e-Commerce, e-Business, and mobile applications using tools of the student's choice.

References

• Chaffey, D., Hemphill, T., & Edmundson-Bird, D. (2019). Digital business and e-commerce management (7th ed.). Pearson.
• Laudon, K., & Laudon, J. (2019). E-Commerce 2019: Business, technology and society (15th ed.). Pearson Education.
• Schneider, G. (2016). Electronic commerce (12th ed.). Cengage Learning.
• Strauss, J., & Frost, R. D. (2018). E-Marketing (8th ed.). Routledge.
• Turban, E., Outland, J., King, D., Lee, J. K., Liang, T. P., & Turban, D. C. (2018). Electronic commerce 2018: A managerial and social networks perspective (9th ed.). Springer.

MIT559 — Advanced Programming

Semester II Elective I · Theory + Practical · Credit Hours 3 · Full Marks 45+30 · Pass Marks 22.5+15

Course Description: Covers data processing, inferential statistics, data visualization, machine learning, text mining, and big data processing with Python.

Course Objectives: To enable students to write Python programs related to data science and data visualization.

Unit 1: Data Processing and Inferential Statistics (12 Hrs.)

Review of NumPy arrays; data analysis with Pandas; data cleansing; data operations; forms of distribution; z-score, p-value; one-tailed and two-tailed tests; Type I and Type II errors; confidence interval; correlation; Z-test vs. T-test; F distribution; chi-square distribution — goodness of fit and test of independence; ANOVA.

Unit 2: Data Mining and Data Visualization (10 Hrs.)

Data mining; presenting an analysis (studying the Titanic dataset); controlling line properties of a chart; creating multiple plots; styling plots; box plots; heatmaps; scatter plots with histograms; scatter plot matrix; area plots; bubble charts; hexagon bin plots; trellis plots; 3D surface plots.

Unit 3: Python for Intelligence (14 Hrs.)

Types of machine learning; decision trees; linear regression; logistic regression; naive Bayes classifier; k-means clustering; hierarchical clustering; performing predictions with linear regression; estimating the likelihood of events with logistic regression; generating recommendations with collaborative filtering; making predictions with ensemble methods; applying segmentation with k-means clustering.

Unit 4: Python for Text Mining and Big Data (9 Hrs.)

Preprocessing data; creating a word cloud; word and sentence tokenization; parts-of-speech tagging; stemming and lemmatization; the Stanford Named Entity Recognizer; performing sentiment analysis on world leaders using Twitter; Hadoop, Python MapReduce, file handling with Hadoopy, Pig, Python with Apache Spark.

Laboratory Works: Students write Python programs illustrating inferential statistics, data visualization, machine learning algorithms, text mining, and big data processing.

References

• Madhavan, S. (2015). Mastering Python for data science.
• Raschka, S., & Mirjalili, V. (2017). Python machine learning: Machine learning and deep learning with Python (2nd ed.).
• Artasanchez, A., & Joshi, P. (2020). Artificial intelligence with Python (2nd ed.). Packt Publishing.

MIT555 — Seminar

Semester II · Seminar · Credit Hours 1 · Full Marks 25 · Pass Marks 12.5

Course Description: A student pursuing the seminar prepares a seminar report and presents the seminar in the department; once accepted, the student submits the final copy of the report.

Introduction: Each student writes a comprehensive report (5–10 pages) on a topic of their choice within their relevant subject area. Research-oriented topics are recommended over purely informative ones.

Arrangement of Contents:

1. Cover page & title page
2. Abstract
3. Chapters — introduction; previous works, discussions and findings; conclusion
4. References

Format of References:

References are listed alphabetically by the author's last name and numbered sequentially, e.g.:

• Journal: Stalling, W. RSA and its computational aspects. Infoworld, 12, 28 (Jul. 2012), 42-49.
• Book: Bishop, M., & Boneh, D. Elements of computer security. Pearson Education, 2009.
• Proceedings: Shamir, A. Controlling attacks on public key cryptography. Proceedings of OOPSLA 86 (Sept. 1986, Portland), 405-416.
• Electronically published article: Smith, J. An algorithm for the traveling salesman problem. Journal of the ACM, 54 (June 2011), 234-245.

Citations in the text refer to the numbered entry in square brackets (e.g. [3]), with multiple citations combined as [4, 8, 11].

Text formatting: Prepared using a word processor or LaTeX (LaTeX recommended); one-inch margins; 1.5 line spacing; 12-point Times New Roman font.