Skip to content

Department of Electrical Engineering

BS Computer Science

IST is now offering Bachelors program in Computer Science from Fall 2020 at IST main campus Islamabad. Computer Science is the most saught-after degree these days and student's premier preference. IST has done the necessary homework to make it a very competitive program. Related research labs like Cybersecurity Lab, Artificial Intelligence & Computer Vision Lab (iVision) have already been established.

The curriculum of Computer Science is designed to provide in-depth knowledge about the field of computing and related interdisciplinary areas. The students will be able to gain practical knowledge and hands-on experience in the areas like artificial intelligence, image processing, mobile application development, big data analytics, social network analysis, computer architecture and Internet of Things (IoT). The program of computer science will allow the students to involve themselves in the solution development of the real-world problems and open the opportunities for the students as potential candidates for private or public sector organization, freelance developers and entrepreneurs.

  1. Apply computing knowledge and skills to compete in the market
  2. Initiate projects and manage teams through inter-personal skills
  3. Demonstrate high moral and ethical values, life-long learning attitude and societal responsibilities
  1. Academic Education:
    To prepare graduates as computing professionals
  2. 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 abstraction and conceptualization of computing models from defined problems and requirements
  3. 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
  4. 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
  5. 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
  6. Individual and Team Work:
    Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings
  7. 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
  8. 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
  9. Ethics:
    Understand and commit to professional ethics, responsibilities, and norms of professional computing practice
  10. Life-long Learning
    Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional
Course Name Course Cred Hr
Semester 1
Programming Fundamentals – Theory 3 (3-0)
Programming Fundamentals – Labs 1 (0-3)
Application of Information and Communication Technologies – Theory 2 (2-0)
Application of Information and Communication Technologies – Lab 1 (0-3)
Quantitative Reasoning – 1 (Discrete Structures) 3 (3-0)
Quantitative Reasoning – 2 (Calculus & Analytical Geometry) 3 (3-0)
Functional English 3 (3-0)
Total 16 (14-6)
Semester 2
Object Oriented Programming – Theory 3 (3-0)
Object Oriented Programming – Labs 1 (0-3)
Database Systems – Theory 3 (3-0)
Database Systems – Labs 1 (0-3)
Digital Logic Design – Theory 2 (2-0)
Digital Logic Design – Labs 1 (0-3)
Multivariable Calculus 3 (3-0)
Linear Algebra 3 (3-0)
Total 17 (14-9)
Semester 3
Data Structures – Theory 3 (3-0)
Data Structures – Lab 1 (0-3)
Information Security – Theory 2 (2-0)
Information Security – Labs 1 (0-3)
Artificial Intelligence – Theory 2 (3-0)
Artificial Intelligence – Labs 1 (0-3)
Computer Networks – Theory 2 (3-0)
Computer Networks – Labs 1 (0-3)
Software Engineering 3 (3-0)
Probability and Statistics 3 (3-0)
Total 19 (15-12)
Semester 4
Computer Organization & Assembly Language – Theory 2 (2-0)
Computer Organization & Assembly Language – Lab 1 (0-3)
Domain Core – 1 (Theory of Automata) 3 (3-0)
Domain Core – 2 (Advanced Database Management Systems – Theory) 2(2-0)
Domain Core – 2 (Advanced Database Management Systems – Lab) 1 (0-3)
Natural Science (Applied Physics – Theory) 2 (2-0)
Natural Science (Applied Physics – Lab) 1 (0-3)
Expository Writing 3 (3-0)
Islamic Studies 2 (2-0)
Total 17 (14-9)
Semester 5
Operating Systems – Theory 2 (2-0)
Operating Systems – Lab 1 (0-3)
Domain Core – 3 (HCI & Computer Graphics – Theory) 2 (2-0)
Domain Core – 3 (HCI & Computer Graphics – Lab) 1 (0-3)
Domain Core – 4 (Computer Architecture – Theory) 2 (2-0)
Domain Core – 4 (Computer Architecture – Lab) 1 (0-3)
Domain Elective – 1 - Theory 2 (2-0)
Domain Elective – 1 – Lab 1 (0-3)
Domain Elective – 2 – Theory 2 (2-0)
Domain Elective – 2  – Lab 1 (0-3)
Social Science (Introduction to Management) 2 (2-0)
Total 17 (12-15)
Semester 6
Domain Core – 5 (Compiler Construction) 3 (3-0)
Domain Core – 6 (Parallel & Distributed Computing – Theory) 2 (2-0)
Domain Core – 6 (Parallel & Distributed Computing – Lab) 1 (0-3)
Domain Elective – 3 – Theory 2 (2-0)
Domain Elective – 3 – Lab 1 (0-3)
Domain Elective – 4 -Theory 2 (2-0)
Domain Elective – 4 - Lab 1 (0-3)
Domain Elective – 5. – Theory 2 (2-0)
Domain Elective – 5 – Lab 1 (0-3)
Domain Elective – 6 - Theory 2 (2-0)
Domain Elective – 6 – Lab 1 (0-3)
Total 18 (12-18)
Semester 7
Final Year Project – I 2 (0-6)
Analysis of Algorithms 3 (3-0)
Domain Elective – 7 – Theory 2 (2-0)
Domain Elective – 7 – Lab 1 (0-3)
Elective Supporting 3 (3-0)
Technical & Business Writing 3 (3-0)
Entrepreneurship 2 (2-0)
Total 16 (13-9)
Semester 8
Final Year Project – II 4 (0-12)
Ideology and Constitution of Pakistan 2 (2-0)
Arts and Humanities (Professional Practices) 2 (2-0)
Civics and Community Engagement 2 (2-0)
Internship 03
Total 13 (6-12)
Electives Courses (Computer Science)
(Computer Science Domain Electives 21 credit hours/ 07 courses are included in the curriculum of BSCS as per HEC 2023 Revised Curriculum.)
Course Title
Web Technologies (WT)
Mobile Application Development – 1 (MAD-1)
Advanced Programming (AP) (Old Name: Visual Programming)
Numerical Analysis (NA)
Web Engineering (WE)
Cyber Security (CS)
Software Testing & Quality Assurance (STQA)
Blockchain
Data Mining
Big Data Analytics
Object Oriented Analysis & Design
Computer Vision
E-Commerce
Wireless Security
Software Design Patterns
Natural Language Processing
Digital Image Processing
Web Design and Development
Semantic Web
Cloud Computing
Database Administration and Management
Software Quality Engineering
Management Information System
Mobile Application Development – 2 (MAD-2)
Electives Supporting Courses
(Elective Supporting 03 credit hours/ 01 course is included in the curriculum of BSCS as per HEC 2023 Revised Curriculum.)
Course Title
Social Science (Example: Introduction to Marketing)
Social Science (Example: Financial Accounting)
Social Science (Example: Organization Behavior)
Social Science (Example: Financial Management)
Social Science (Example: Human Resource Management)

(Mandatory Courses)

Introduction to ICT:

Introduction to IT, Hardware, Computer Software, Internet and Web, Introduction to Data Communication and Computer Networks, Development, Introduction to Software Engineering

Programming Fundamentals:

Introduction to programming languages, Introduction to C++, C/C++ Programming Basics, Loops and Decisions, Structures, Pointers, Functions, Arrays and Strings.

Digital Logic Design

Number systems, Logical Analysis, Combinational Logic, Sequential Logic, Data Processing, manipulation, data storage, logic families and logic design, FPGAs, Hardware description language like VERILOG

Object Oriented Programming

Evolution of Object Oriented (OO) programming, OO concepts and principles, problem solving in OO paradigm, OO program design process, classes, methods, objects and encapsulation, constructors and destructors, operator and function overloading, virtual functions, derived classes, inheritance and polymorphism, I/O and file processing, exception handling

Computer Organization and Assembly Language:

Introduction to microprocessors and computers, System Buses, Internal Memory, External Memory, Input/Output, Interrupts, DMA/IO Channels, Computer Arithmetic, Addressing Modes, CPU Structure and Functions, Control Unit Operations

Data Structure and Algorithms:

Introduction to Data structures and types of data structures, Definition of algorithm, running time of algorithm, examples, role of efficient algorithms, Definition of Recursion, Direct and Indirect Recursion, Examples of Recursive Functions, Linear Queue & Its Features, Linear Queue Implementation, Circular Queue, Linked List & Its Features, Linked List Implementation, Doubly Linked List & its Implementation, Stack & Its Implementation, Postfix Notation Concept, Implementation Of Postfix Notation, Binary Trees, Strictly Binary Tree, Complete Binary Tree, Almost Complete Binary Tree, Binary Tree Applications, Traversing Trees, Pre-Order Traversing In-Order Traversing, Post-Order Traversing, Bubble Sort, Quick Sort, Binary Sort, Merge Sort, Insertion Sort, Heap, Heap Construction, Heap Sort, Heap Sort Implementation. Hashing & its Implementation, Linear and Binary Search, What Are Graphs, Representation Of Directed Graphs, Graph Vocabulary, Graph Operations (Add Vertex, Add Edge), C++ Implementation, Hashing, dictionaries and hash tables, hashing function, hashing implementation using array and linked list.

Discrete Structures:

Mathematical reasoning, propositional and predicate logic, rules of inference, proof by induction, proof by contraposition, proof by contradiction, proof by implication, set theory, relations, equivalence relations and partitions, partial orderings, recurrence relations, functions, mappings, function composition, inverse functions, recursive functions, Number Theory, sequences, series, counting, inclusion and exclusion principle, pigeonhole principle, permutations and combinations, elements of graph theory, planar graphs, graph coloring, euler graph, Hamiltonian path, rooted trees, traversals.

Differential Equations:

First order differential equations, separation of variables, homogeneous, exact and linear first order ODE, higher order differential equations, Cauchy Euler differential equations, the method of variation of parameters, power series method, Laplace transformation, linearity and first shifting theorem dirac delta and gamma functions, differentiation and integral theorems, inverse Laplace transformation, system of linear differential equations, partial differential equations, method of separation of variables, solution of one dimensional heat and wave equations D'Alembert to solve wave equation

Design & Analysis of Algorithms

Proof techniques, Induction, Summations, basic algorithms on numbers, complexity classes, Searching and Sorting, Asymptotic analysis , Divide-and-conquer: merge-sort, closest pair problems, collaborative filtering, Karatsuba algorithm, deterministic Selection, Greedy algorithms: Huffman codes, Minimum Spanning Tree, Interval Scheduling Dynamic programming: Weighted Independent Set in Paths, Weighted Interval Scheduling, Knapsack Problem, Sequence Alignment, Single Source Shortest Paths (Bellman-Ford Algorithm), BFS, DFS, Topological Sort, Shortest paths, The Class P and NP, Decision, Optimization and Search problems, NP Complete Problems, Reduction, P vs NP Question, Ford Fulkerson Algorithm, Max Flow-Min Cut Algorithm, Edmond Karp Algorithm, Applications, Standard Form, Geometry of LP, Simplex Algorithm, Closest pair in linear-time, Universal and uniform hashing, Randomized Selection, Randomized Quicksort, Minimum Cut algorithm, Easy vs. hard problems, Approximation Algorithm with absolute approximation guarantees, Hardness of Approximation, Relative Approximation Algorithm, Max Cut , Set Cover, Vertex Cover, Parallel machine scheduling, PTAS and FPTAS for Knapsack problem, Approximation algorithms for TSPLP- rounding based Approximation Algorithms

Theory of Automata:

Finite State Models: Language definitions preliminaries, Regular expressions/Regular languages, Finite automata (FAs), Transition graphs (TGs), NFAs, Kleene’s theorem, Transducers (automata with output), Pumping lemma and non-regular language Grammars and PDA: CFGs, Derivations, derivation trees and ambiguity, Simplifying CFLs, Normal form grammars and parsing, Decidability, Context sensitive languages, grammars and linear bounded automata (LBA), Chomsky’s hierarchy of grammars Turing Machines Theory: Turing machines, Post machine, Variations on TM, TM encoding, Universal Turing Machine, Defining Computers by TMs.

Database Systems:

Basic database concepts, Database approach vs file based system, database architecture, three level schema architecture, data independence, relational data model, attributes, schemas, tuples, domains, relation instances, keys of relations, integrity constraints, relational algebra, selection, projection, Cartesian product, types of joins, normalization, functional dependencies, normal forms, entity relationship model, entity sets, attributes, relationship, entity-relationship diagrams, Structured Query Language (SQL), Joins and sub-queries in SQL, Grouping and aggregation in SQL, concurrency control, database backup and recovery, indexes, NoSQL systems.

Compiler Construction:

Introduction to interpreter and compiler. Compiler techniques and methodology; Organization of compilers; Lexical and syntax analysis; Parsing techniques. Types of parsers, top-down parsing, bottom-up parsing, Type checking, Semantic analyser, Object code generation and optimization, detection and recovery from errors.

Operating Systems:

Operating systems basics, system calls, process concept and scheduling, inter-process communication, multithreaded programming, multithreading models, threading issues, process scheduling algorithms, thread scheduling, multiple-processor scheduling, synchronization, critical section, synchronization hardware, synchronization problems, deadlocks, detecting and recovering from deadlocks, memory management, swapping, contiguous memory allocation, segmentation & paging, virtual memory management, demand paging, thrashing, memory-mapped files, file systems, file concept, directory and disk structure, directory implementation, free space management, disk structure and scheduling, swap space management, system protection, virtual machines, operating system security

Software Engineering:

Nature of Software, Overview of Software Engineering, Professional software development,Software engineeringpractice, Software process structure, Software process models, Agile software Development, Agile process models, Agile development techniques, Requirements engineering process, Functional and non-functional requirements, Context models, Interaction models, Structural models, behavioral models, model driven engineering, Architectural design, Design and implementation, UML diagrams, Design patterns, Software testing and quality assurance, Software evolution, Project management and project planning, configuration management, Software Process improvement.

Artificial Intelligence:

Introduction (Introduction, basic component of AI, Identifying AI systems, branches of AI, etc.); Reasoning and Knowledge Representation (Introduction to Reasoning and Knowledge Representation, Propositional Logic, First order Logic); Problem Solving by Searching (Informed searching, Uninformed searching, Local searching.); Constraint Satisfaction Problems; Adversarial Search (Min-max algorithm, Alpha beta pruning, Game-playing); Learning (Unsupervised learning, Supervised learning, Reinforcement learning) ;Uncertainty handling (Uncertainty in AI, Fuzzy logic); Recent trends in AI and applications of AI algorithms (trends, Case study of AI systems, Analysis of AI systems).

Computer Networks:

Introduction to Computer Networks, Network Hardware, LAN, MAN, WAN, Inter Networks and the Internet, End Systems, Client and Servers, Connectionless and Connection Oriented Services, The Network Core: Circuit Switching and Packet Switching, Network Access and Physical Media, ISPs and Internet Backbones, Delay and Loss in Packet Switched Networks, Protocol Layering, Design Issues for Layers, Service Models. OSI Reference Model, Principles of Application-Layer Protocols, The Web and HTTP, FTP, DNS, WWW, Electronic Mail, Multimedia, Transport Layer Introduction, Transport Protocols, UDP, TCP/IP, Network Layer Introduction, Routing Algorithms, Congestion and Congestion Control Algorithms, QoS, Tunneling, Inter-network Routing and Addressing, Introduction to MAC and Channel Allocation, MAC Protocols, Bridges, Spanning Tree, Remote Bridges, Repeaters, Hub, Bridges, Switches, Routers, and Gateways, Introduction to Data Link Layer, Framing, Error Control, Flow Control, Error Detection and Correction

Parallel & Distributed Computing:

Introduction, Parallel and Distributed Computing, Parallel and Distrubuted Architectures, Socket programming, Parallel Performance, Shared Memory and Threads, Parallel Algorithms, Parallel Algorithms, OpenMP, Scalable Algorithms, Message Passing, Distributed Systems, MapReduce, Clusters, Distributed Coordination, Security, Distributed File Systems, Security, Distributed Shared Memory, Peer-to-Peer, Cloud Computing

Information Security:

Information security foundations, security design principles; security mechanisms, symmetric and asymmetric cryptography, encryption, hash functions, digital signatures, key management, authentication and access control; software security, vulnerabilities and protections, malware, database security; network security, firewalls, intrusion detection; security policies, policy formation and enforcement, risk assessment, cybercrime, law and ethics in information security, privacy and anonymity of data.

  1. Matric/ Equivalent Certificate
  2. HSSC/ Equivalent with Mathematics with minimum 50% overall marks OR HSSC Pre-Medical/ Equivalent with minimum 50% overall marks*
  3. No Entry Test Required

*Candidates with FSc Pre-Medical / equivalent qualification enrolled in BS Computer Science program will have to clear deficient Mathematics courses of 6 credit hours, within one year

Number of Seats 50