Vision of the Computer Science Department
To become a center of excellence in computer science education, research and globalized technologies
Mission of the BS Computer Science Programme
To produce graduates having good problem-solving skills and knowledge to use computers creatively and effectively along with team building and professional skills
PEO 1: Apply principle and practices of information technology and computing knowledge to solve challenging problems in relevant profession
PEO 2: Demonstrate the ability to use modern tools learnt during degree Programme to design and develop effective solutions
PEO 3: Exhibit managerial capabilities with ethical and moral values Programme Educational Objectives (PEOs)
PLO1 Academic Education: To prepare graduates as computing professionals.
PLO2 Knowledge for Solving Computing Problems: Apply knowledge of computing fundamentals, knowledge of a computing specialization, and mathematics, science, and domain knowledge appropriate for the computing specialization to the 16 abstraction and conceptualization of computing models from defined problems and requirements.
PLO3 Problem Analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.
PLO4 Design/ Development of Solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
PLO5 Modern Tool Usage: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.
PLO6 Individual and Teamwork: Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings.
PLO7 Communication: Communicate effectively with the computing community and with society at large about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions.
PLO8 Computing Professionalism and Society: Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice.
PLO9 Ethics: Understand and commit to professional ethics, responsibilities, and norms of professional computing practice.
PLO10 Life-long Learning: Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional.
| No. | Programme Learning Outcomes (PLOs) | PEOs | ||
| PEO-1 | PEO-2 | PEO-3 | ||
| 1 | Academic Education | ✔ | ✔ | |
| 2 | Knowledge for solving Computing Problems | ✔ | ||
| 3 | Problem Analysis | ✔ | ||
| 4 | Design/ Development of Solutions | ✔ | ✔ | |
| 5 | Modern Tool Usage | ✔ | ✔ | |
| 6 | Individual and Teamwork | ✔ | ✔ | |
| 7 | Communication | ✔ | ||
| 8 | Computing Professionalism and Society | ✔ | ||
| 9 | Ethics | ✔ | ||
| 10 | Life-long Learning | ✔ | ✔ |
CURRICULUM MODEL FOR BS IN INFORMATION TECHNOLOGY
The generic structure for computing degree Programme given before is mapped with the BSIT Programme in the following tables.
Generic Structure for Computing Disciplines:
| Areas | Credit Hours | Courses |
| Computing Core | 48 | 14 |
| Domain Core | 19 | 6 |
| Domain Elective | 21 | 7 |
| Mathematics & Supporting Courses | 12 | 4 |
| Elective Supporting Courses | 3 | 1 |
| General Education Requirement | 30 | 12 |
| Total | 133 | 44 |
BS (Information Technology) Roadmap
| SEMESTER 1 | ||||||
| Prerequisite | Course Code | Course Title | Theory | Lab | CR | CR/Sem |
| None | GSC 114 | Applied Physics | 2 | 0 | 2 |
16 |
| None | GSL 114 | Applied Physics Lab | 0 | 1 | 1 | |
| None | CSC 114 | Introduction to Information &
Communication Technology |
2 | 0 | 2 | |
| None | CSL 114 | Introduction to Information &
Communication Technology Lab |
0 | 1 | 1 | |
| None | ENG 101 | Functional English | 3 | 0 | 3 | |
| None | CSC 113 | Computer Programming | 3 | 0 | 3 | |
| None | CSL 113 | Computer Programming Lab | 0 | 1 | 1 | |
| None | GSC 221 | Discrete Mathematics | 3 | 0 | 3 | |
| SEMESTER 2 | ||||||
| Prerequisite | Course Code | Course Title | Theory | Lab | CR | CR/Sem |
| None | CSC 220 | Database Management Systems | 3 | 0 | 3 |
17 |
| None | CSL 220 | Database Management Systems Lab | 0 | 1 | 1 | |
| CSC 113 | CSC 210 | Object Oriented Programming | 3 | 0 | 3 | |
| CSC 113 | CSL 210 | Object Oriented Programming Lab | 0 | 1 | 1 | |
| GSC 114 | CEN 122 | Digital Design | 2 | 0 | 2 | |
| GSC 114 | CEL 122 | Digital Design Lab | 0 | 1 | 1 | |
| None | GSC 110 | Applied Calculus and Analytical | 3 | 0 | 3 | |
| Geometry | ||||||
| None | GSC 121 | Linear Algebra | 3 | 0 | 3 | |
| SEMESTER 3 | ||||||
| Prerequisite | Course Code | Course Title | Theory | Lab | CR | CR/Sem |
| None | GSC 122 | Probability and Statistics | 3 | 0 | 3 |
17 |
| GSC 110 | GSC 211 | Multivariable Calculus | 3 | 0 | 3 | |
| None | CEN 223 | Computer Communication & Networks | 3 | 0 | 3 | |
| None | CEL 223 | Computer Communication &
Networks Lab |
0 | 1 | 1 | |
| CSC 113 | CSC 221 | Data Structure & Algorithm | 3 | 0 | 3 | |
| CSC 113 | CSL 221 | Data Structure & Algorithm Lab | 0 | 1 | 1 | |
| None | SEN 220 | Software Engineering | 3 | 0 | 3 | |
| SEMESTER 4 | ||||||
| Prerequisite | Course Code | Course Title | Theory | Lab | CR | CR/Sem |
| CSC 210 | ITC 226 | Web Systems & Technologies | 2 | 0 | 2 |
17 |
| CSC 210 | ITL 226 | Web Systems & Technologies Lab | 0 | 1 | 1 | |
| CEN 122 | CEN 323 | Computer Organization and
Assembly Language |
2 | 0 | 2 | |
| CEN 122 | CEL 323 | Computer Organization & Assembly
Language Lab |
0 | 1 | 1 | |
| CSC 210 | CSC 411 | Artificial Intelligence | 2 | 0 | 2 | |
| CSC 210 | CSL 411 | Artificial Intelligence Lab | 0 | 1 | 1 | |
| CEN 223 | CSC 407 | Information Security | 3 | 0 | 3 | |
| CSC 220 | ITC 327 | Database Administration &
Management |
2 | 0 | 2 | |
| CSC 220 | ITL 327 | Database Administration & Management Lab | 0 | 1 | 1 | |
| None | ENG 134 | Communication Skills | 2 | 0 | 2 | |
| SEMESTER 5 | ||||||
| Prerequisite | Course Code | Course Title | Theory | Lab | CR | CR/Sem |
| CSC 221 | CSC 320 | Operating Systems | 3 | 0 | 3 |
17 |
| CSC 221 | CSL 320 | Operating Systems Lab | 0 | 1 | 1 | |
| CSC 221 | CSC 321 | Design and Analysis of Algorithms | 3 | 0 | 3 | |
| CEN 223 | ITC 411 | Cyber Security | 3 | 0 | 3 | |
| CEN 223 | ITC 312 | System & Network Administration | 3 | 0 | 3 | |
| CEN 223 | ITL 312 | System & Network Administration
Lab |
0 | 1 | 1 | |
| None | Social Sciences Elective | 3 | 0 | 3 | ||
| SEMESTER 6 | ||||||
| Prerequisite | Course Code | Course Title | Theory | Lab | CR | CR/Sem |
| ITC 312 | ITC 324 | Information Technology
Infrastructure |
3 | 0 | 3 |
18 |
| ENG 134 | ENG 320 | Technical Writing and Presentation
Skills |
3 | 0 | 3 | |
| CSC 320 | AIC 302 | Parallel & Distributed Computing | 2 | 0 | 2 | |
| CSC 320 | AIL 302 | Parallel & Distributed Computing
Lab |
0 | 1 | 1 | |
| Domain Elective 1 (2+1) | 2 | 1 | 3 | |||
| Domain Elective 2 (2+1) | 2 | 1 | 3 | |||
| Domain Elective 3 (3+0 or 2+1) | 3/2 | 0/1 | 3 | |||
| SEMESTER 7 | ||||||
| Prerequisite | Course code | Course Title | Theory | Lab | CR | CR/Sem |
| None | FYP 400 | Final Year Project | 0 | 3 | 3 |
17 |
| Elective Supporting Course | 3 | 0 | 3 | |||
| Domain Elective 4 (2+1) | 2 | 1 | 3 | |||
| Domain Elective 5 (2+1) | 2 | 1 | 3 | |||
| Domain Elective 6 (3+0 or 2+1) | 3/2 | 0/1 | 3 | |||
| None | HSS 423 | Entrepreneurship | 2 | 0 | 2 | |
| SEMESTER 8 | ||||||
| Prerequisite | Course code | Course Title | Theory | Lab | CR | CR/Sem |
| None | FYP 400 | Final Year Project | 0 | 3 | 3 |
14 |
| None | PAK 101 | Pakistan Studies | 2 | 0 | 2 | |
| None | CSC 308 | Professional Practices & Ethics | 2 | 0 | 2 | |
| None | HSS 217 | Introduction to Sociology | 2 | 0 | 2 | |
| Domain Elective 7 (3+0 or 2+1) | 3/2 | 0/1 | 3 | |||
| None | ISL 101 | Islamic Studies | 2 | 0 | 2 | |
| Total Credit Hours: | 133 | |||||
List of Courses
Computing Core Courses (48 credit hours)
| Pre-requisite | Course Code | Course Title | Lec | Lab | CR |
| None | CSC 113 | Computer Programming | 3 | 1 | 4 |
| CSC 113 | CSC 210 | Object Oriented Programming | 3 | 1 | 4 |
| None | CSC 220 | Database Management Systems | 3 | 1 | 4 |
| GSC 114 | CEN 122 | Digital Design | 2 | 1 | 3 |
| CSC 113 | CSC 221 | Data Structure & Algorithm | 3 | 1 | 4 |
| CEN 223 | CSC 407 | Information Security | 3 | 0 | 3 |
| CSC 210 | CSC 411 | Artificial Intelligence | 2 | 1 | 3 |
| None | CEN 223 | Computer Communication &
Networks |
3 | 1 | 4 |
| None | SEN 220 | Software Engineering | 3 | 0 | 3 |
| CEN 122 | CEN 323 | Computer Organization and
Assembly Language |
2 | 1 | 3 |
| CSC 221 | CSC 320 | Operating Systems | 3 | 1 | 4 |
| CSC 221 | CSC 321 | Design and Analysis of Algorithms | 3 | 0 | 3 |
| None | FYP 400 | Final Year Project | 0 | 6 | 6 |
Information Technology Domain Core Courses (19 credit hours)
| Pre-requisite | Course Code | Course Title | Lec | Lab | CR |
| ITC 312 | ITC 324 | Information Technology Infrastructure | 3 | 0 | 3 |
| CEN 223 | ITC 312 | System & Network Administration | 3 | 1 | 4 |
| CEN 223 | ITC 411 | Cyber Security | 3 | 0 | 3 |
| CSC 210 | CSC 411 | Artificial Intelligence | 2 | 1 | 3 |
| CSC 210 | ITC 226 | Web Systems & Technologies | 2 | 1 | 3 |
| CSC 320 | AIC 302 | Parallel & Distributed Computing | 2 | 1 | 3 |
List of Information Technology Domain Elective Courses (21 credit hours)
| Prerequisite | Course Code | Course Title | Lec | Lab | CR |
| ITC 226 | ITB 471 | E Commerce | 3 | 0 | 3 |
| None | ITC 425 | Business Processing Re-engineering | 3 | 0 | 3 |
| None | ITC 457 | Knowledge Management System &
Technologies |
3 | 0 | 3 |
| CSC 220 | CSC 452 | Data Mining | 3 | 0 | 3 |
| CSC 220 | CSC 454 | Data Warehousing | 3 | 0 | 3 |
| ITC 226 | SEN 421 | Semantic Web | 3 | 0 | 3 |
| SEN 220 | SEN 411 | Software Testing | 3 | 0 | 3 |
| SEN 220 | SEN 456 | Usability Engineering | 3 | 0 | 3 |
| CSC 220 | CSC 426 | Business Intelligence and Analytic | 3 | 0 | 3 |
| None | SEN 427 | Information Systems Auditing and
Assurance |
3 | 0 | 3 |
| SEN 220 | SEN 428 | Service Oriented Architecture | 3 | 0 | 3 |
| SEN 220 | SEN 420 | Software Quality Assurance | 3 | 0 | 3 |
| CEN 223 | EET 455 | Wireless Communication | 3 | 0 | 3 |
| None | SEN 320 | Human Computer Interaction | 3 | 0 | 3 |
| CSC 221 | CSC 404 | Blockchain Technologies | 3 | 0 | 3 |
| CSC 221 | CSC 448 | Introduction to Bioinformatics | 3 | 0 | 3 |
| CEN 223 | CSC 450 | Internet of Things | 3 | 0 | 3 |
| CEN 223 | SEN 459 | Software Defined Network | 3 | 0 | 3 |
| SEN 220 | CSC 489 | Ubiquitous Computing | 3 | 0 | 3 |
| GSC 221 | GSC 445 | Operation Research | 3 | 0 | 3 |
| None | CSC 458 | Management Information System | 3 | 0 | 3 |
| SEN 220 | SEN 410 | Software Project Management | 3 | 0 | 3 |
| Prerequisite | Course
Code |
Course Title | Lec | Lab | CR |
| CSC 210 | CSC 313 | Visual Programming | 2 | 0 | 2 |
| CSC 210 | CSL 313 | Visual Programming Lab | 0 | 1 | 1 |
| CSC 220 | CSC 487 | Introduction to Data Science | 2 | 0 | 2 |
| CSC 220 | CSL 487 | Introduction to Data Science Lab | 0 | 1 | 1 |
| CSC 210 | CEN 444 | Digital Image Processing | 2 | 0 | 2 |
| CSC 210 | CEL 444 | Digital Image Processing Lab | 0 | 1 | 1 |
| CSC 210 | CSC 444 | Computer Graphics | 2 | 0 | 2 |
| CSC 210 | CSL 444 | Computer Graphics Lab | 0 | 1 | 1 |
| CSC 220 | CSC 468 | Advanced Databases | 2 | 0 | 2 |
| CSC 220 | CSL 468 | Advanced Databases Lab | 0 | 1 | 1 |
| CSC 210 | CSC 341 | Mobile Application Development | 2 | 0 | 2 |
| CSC 210 | CSL 341 | Mobile Application Development Lab | 1 | 0 | 1 |
| None | SEN 493 | Multimedia Systems | 2 | 0 | 2 |
| None | SEL 493 | Multimedia Systems Lab | 0 | 1 | 1 |
| SEN 220 | SEN 457 | Software Design and Architecture | 2 | 0 | 2 |
| SEN 220 | SEL 457 | Software Design and Architecture Lab | 0 | 1 | 1 |
| CSC 113 | SEN 310 | Web Engineering | 2 | 0 | 2 |
| CSC 113 | SEL 310 | Web Engineering Lab | 0 | 1 | 1 |
| CSC 220 | CSC 488 | Big Data Analytics | 2 | 0 | 2 |
| CSC 220 | CSL 488 | Big Data Analytics Lab | 0 | 1 | 1 |
| None | CSC 484 | Content Management | 2 | 0 | 2 |
| None | CSL 484 | Content Management Lab | 0 | 1 | 1 |
| CSC 411 | CSC 413 | Introduction to Machine Learning | 2 | 0 | 2 |
| CSC 411 | CSC 413 | Introduction to Machine Learning | 0 | 1 | 1 |
Mathematics & Supporting Courses (12 credit hours)
| Prerequisite | Course Code | Course Title | Lec | Lab | CR |
| GSC 110 | GSC 211 | Multivariable Calculus | 3 | 0 | 3 |
| None | GSC 121 | Linear Algebra | 3 | 0 | 3 |
| None | GSC 122 | Probability & Statistics | 3 | 0 | 3 |
| ENG 134 | ENG 320 | Technical Writing and Presentation
Skills |
3 | 0 | 3 |
Elective Supporting Courses (3 credit hours)
| Prerequisite | Course Code | Course Title | Lec | Lab | CR |
| None | MKT 110 | Principles of Marketing | 3 | 0 | 3 |
| None | FIN 201 | Fundamentals of Finance | 3 | 0 | 3 |
| None | MGT 111 | Principles of Management | 3 | 0 | 3 |
| None | MGT 242 | Organizational Theory and Behavior | 3 | 0 | 3 |
General Education Courses (30 credit hours)
| Prerequisite | Course Code | Course Title | Lec | Lab | CR |
| None | CSC 114 | Introduction to Information &
Communication Technology |
2 | 1 | 3 |
| None | ENG 101 | Functional English | 3 | 0 | 3 |
| ENG 105 | ENG 134 | Communication Skills | 2 | 0 | 2 |
| None | GSC 221 | Discrete Mathematics | 3 | 0 | 3 |
| None | GSC 110 | Applied Calculus & Analytical
Geometry |
3 | 0 | 3 |
| None | ISL 101 | Islamic Studies | 2 | 0 | 2 |
| None | PAK 101 | Pakistan Studies | 2 | 0 | 2 |
| None | GSC 114 | Applied Physics | 2 | 1 | 3 |
| None | CSC 308 | Professional Practices and Ethics | 2 | 0 | 2 |
| None | HSS 217 | Introduction to Sociology | 2 | 0 | 2 |
| None | HSS 423 | Entrepreneurship | 2 | 0 | 2 |
| Social Sciences Electives | |||||
| None | HSS 107 | Introduction to Psychology | 3 | 0 | 3 |
| None | HSS 115 | Introduction to Media studies | 3 | 0 | 3 |
| None | BES 103 | Critical Thinking | 3 | 0 | 3 |
Course Title: Blockchain Technologies
Course Code: CSC 404
Credit Hours: 3
Pre-requisite (if any): CSC 221
Course Description
This is a beginners-level course that focuses on the foundational technologies behind blockchain. We will cover the concepts of distributed ledger, consensus mechanisms, authentication techniques, and relevant protocols. The course will provide case studies of blockchain applications such as cryptocurrencies, supply chain management, and B2B/B2C/C2C scenarios. The course will also provide hands-on experience with building and deploying smart contracts.
Course Objectives
The course aims to introduce basic blockchain concepts. You will learn about the decentralized peer-to-peer network, an immutable distributed ledger and the trust model that defines a blockchain. This course enables you to explain basic components of a blockchain (transaction, block, block header, and the chain) its operations (verification, validation, and consensus model) underlying algorithms, and essentials of trust (hard fork and soft fork). Content includes the hashing and cryptography foundations indispensable to blockchain Programming, which is the focus of two subsequent specialization courses, Smart Contracts and Decentralized Applications (Dapps). You will work on a virtual machine image, specifically created for this course, to build an Ethereum test chain and operate on the chain. This hands-on activity will help you understand the workings of a blockchain, its transactions, blocks and mining.
Course Learning Outcomes
CLO: 1. Acquire the basic concepts and uses of blockchain with different applications/Systems [C1 Knowledge]
CLO: 2. Describe and apply different stages of blockchain development using different algorithms [C3 – Application]
CLO: 3. Identify the problems and apply blockchain solutions. [C2 – Comprehension]
CLO: 4. Build blockchain environment using tools. [P3 – Comprehension]
Recommended Text Books/Reference Books (latest edition)
Antony Lewis The Basics of Bitcoins and Blockchains: An Introduction to Cryptocurrencies and the Technology that Powers Them (5th Edition)
Web Resources/Other Course Materials
Slides, and reference material, whitepapers, online resources
Course Title: Introduction to Bioinformatics
Course Code: CSC 448
Credit Hours: 03
Pre-requisite: CSC 221
Course Description
This course is designed to give students both a theoretical background and a working knowledge of the techniques employed in bioinformatics. Emphasis will be placed on biological sequence (DNA, RNA, protein) analysis and its applications.
Course Objectives
A basic understanding of the biological data and biological processes Familiarity with the biological databases and their use
Understanding of the biological problems that can be solved using computational techniques Understanding and implementation of the algorithms used to solve biological problems Analyses of the biological data and biological techniques
Course Learning Outcomes
Students will become familiar with different biological databases
Students will be able to extract and analyze the data from the different biological resources Students will become familiar with the different biological techniques used to solve biological problems (such as structure prediction)
Students will be able to compare and analyze sequential data (DNA, RNA, and Proteins)
Students will be able to solve the biological problems (by applying computational techniques) using different types of information extracted from the biological data
Course Contents:
Introduction to Bioinformatics
DNA Replication, Transcription, and Translation
Introduction to biological databases and retrieval of information from these databases Sequence Alignment (Local and Global)
Introduction to structure prediction of the proteins
Advance problems in Bioinformatics that can be solved by computational techniques Use of protein sequence and structure to solve biological problems
Recommended Text Books/Reference Books (latest edition)
Introduction to Bioinformatics by Arthur M. Lesk (2019
An Introduction to Bioinformatics Algorithms by NC Jones, and PA Pevzner (2004)
Web Resources/Other Course Materials
Different Databases and Tools such as Uniprot, PDB, SwissProt, etc. Research Articles
Course Title: Introduction to Machine Learning
Course Code: CSC 413
Credit Hours: 3 (Theory) Pre-requisite (if any): CSC 411
Course Description
Machine learning is a subset of AI. This field is incredibly pervasive, with applications spanning from business intelligence to security, from analyzing biochemical interactions to structural monitoring
of aging bridges, etc. It uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. This course will familiarize students with a broad cross-section of models and algorithms for machine learning. Students will also implement algorithms using python machine learning libraries. Students should be familiar with basic mathematics concepts such as linear algebra, calculus, and statistics to get maximum benefit of this course.
Course Objectives
To provide the knowledge of supervised and unsupervised machine learning paradigm that how it can help to do the tasks that humans can do out of instinct.
To provide understanding of machine learning process cycle from data acquisition to model training, and performance evaluation.
To enable student to implement different machine learning algorithms for classification and regression problems.
Course Learning Outcomes
Understand and describe a wide variety of learning algorithms
Apply the machine learning algorithms to a case study data/real-world problem Evaluate and optimize the trained models to tune up the performance
Course Contents:
Introduction: what is ML; Problems, Applications
Categories (Supervised Learning, Un-supervised Learning, Reinforcement Learning)
An introduction to Prominent Supervised learning (Classification and Regression Algorithms) Introduction to Python (Language, Tools and IDE’s Tour, Scikit learn ML Libraries)
ML Process cycle (Data Acquisition, Model Training Process, Trained Model Evaluation)
Data Acquisition (Categorical & numerical data, Data Pre-processing and Preparation, Data Wrangling)
Algorithms (concept, mathematical model, implementation using python) Linear regression
Naïve Bayes Algorithm Decision Trees (ID3) Random Forest
Support vector machines K-near neighbors
K-mean clustering Logistic Regression Neural Network
Reinforcement learning (Agent, reward, feedback policy, different RL Models)
Model Training process (Train test split, hyper parameter, tuning, cross validation, best fit model selection)
Training & Testing conventions (K-fold cross validation, Overfitting, Underfitting) Model Performance Evaluation
Bias, Variance trade-off Loss Functions
Optimization, Gradient Descent Regularization (L1 and L2)
Reporting predictive performance by Evaluation metrics (Accuracy, Precision, Recall, F1, ROC and AUC)
Tuning model complexity, Model Design Issues Trained Model Deployment
Recommended Text Books/Reference Books (latest edition)
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. ” O’Reilly Media, Inc.
Burkov, A. (2019). The hundred-page machine learning book (Vol. 1, p. 32). Quebec City, QC, Canada: Andriy Burkov.
Sebastian Raschka (2017). Python Machine Learning (2nd ed.). ISBN: 978-1-78712-593
Web Resources/Other Course Materials
Wang, W., & Siau, K. (2019). Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. Journal of Database Management (JDM), 30(1), 61-79.
https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Course Title: Business Processing Re-engineering
Course Code: ITC 425
Credit Hours: 3
Pre-requisite (if any): None
Course Description
This course focuses on the application of industry ‘best practice’ strategies, tools and techniques in business process management to re-engineer organizations’ business processes. Students will learn about key business process management concepts, and how to apply a proven five (5) phase methodology to re-engineer business processes in ‘real world’ organizational situations. Upon successful completion of this course, students would be equipped to carry out business process reengineering (BPR) initiatives within their own organizations, to produce better performing business processes.