The department of Information & Communication Technologies (DICT) aims to educate females to excel in cutting edge technologies that are shaping today’s world and having positive impact on everyday life of populace. The department hence offers a program that majors on data science knowledge from a computing point of view. The program has been designed to furnish the students with techniques and algorithms to cater the evolving requirements of latest computer technology applications in healthcare, manufacturing, banking, transport, finance, and e-commerce that demands enormous and efficient use of data. In addition to data science expertise, the department visions to provide basic knowledge of computer science principles as well. Hence, this program is suitable for students who want to learn about ever-increasing and innovative knowledge of computers majoring in data science to become life-long learners
PEO - 1: To produce technologically and academically skilled leaders playing a key role to make or assist in decision making based on data-driven evolution for real-world problems.
PEO - 2: To produce active consultants, innovators, and researchers in the area of data science and data analytics.
PEO - 3: To produce data experts that can leverage full potential of data with good ethical and moral values to work as future industry leaders and ambassadors of technology
To help to achieve the educational objectives, the learning outcomes of BS Data Science have following program learning outcomes.
PLO - 1: Graduates will have advance knowledge and skill in data science analytics to formulate solution for technical problems.
PLO - 2: Graduates will communicate the outcomes of data analytics and visualization to stake holders for better decision making.
PLO - 3: Graduates will continuously manage, digest, and integrate current data science knowledge and analytical skills through the lifelong learning process.
PLO - 4: Graduates will disseminate data analytics information in ethical manner.
PLO - 5: Graduates will demonstrate behavior that portrays social responsibility in conducting project to solve data driven problems in real world.
Scheme of Studies
| First Year | |||
|---|---|---|---|
| SEMESTER – I (YEAR ONE) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | English Composition & Comprehension | Expository Writing | 3 (3-0) |
| 2 | General Psychology | Social Science | 3 (3-0) |
| 3 | Foreign Language | Arts & Humanities | 3 (3-0) |
| 4 | Physics-I | Natural Science | 3 (3-0) |
| 5 | Linear Algebra | Quantitative Reasoning | 3 (3-0) |
| Sub Total Credit Hrs. in First Semester | 15 | ||
| SEMESTER – II (YEAR ONE) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Communication Skills | Expository Writing | 3 (3-0) |
| 2 | Organizational Behaviour | Social Science | 3 (3-0) |
| 3 | Logic and Critical Thinking | Arts & Humanities | 3 (3-0) |
| 4 | Physics-II | Natural Science | 3 (3-0) |
| 5 | Probability and Statistics | Quantitative Reasoning | 3 (3-0) |
| Sub Total Credit Hrs. in Second Semester | 15 | ||
| Second Year | |||
| SEMESTER – III (YEAR TWO) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Technical Writing and Presentation Skills | Expository Writing | 3 (3-0) |
| 2 | Pakistan Studies and Global Perspective | General Education | 3 (3-0) |
| 3 | Islamic Studies & Ethics | General Education | 3 (3-0) |
| 4 | Introduction to Information & Communication Technologies | Major | 3 (2-1) |
| 5 | Programming Fundamentals | Distribution | 3 (3-0) |
| Sub Total Credit Hrs. in Third Semester | 15 | ||
| SEMESTER – IV (YEAR TWO) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Advance Statistics | Core | 3 (3-0) |
| 2 | Operating System | Distribution | 4 (3-1) |
| 3 | Software Engineering | Distribution | 3 (3-0) |
| 4 | Digital Logic Design | Core | 4 (3-1) |
| 5 | Introduction to Data Science | Core | 3 (2-1) |
| Sub Total Credit Hrs. in Fourth Semester | 17 | ||
| Third Year | |||
| SEMESTER – V (YEAR THREE) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Object Oriented Programming | Distribution | 4 (3-1) |
| 2 | Computer Networks: Principles & Practices | Distribution | 4 (3-1) |
| 3 | Elective-I | Elective | 3 (3-0) |
| 4 | Elective-II | Elective | 3 (3-0) |
| 5 | Advance Statistics | Core | 3 (3-0) |
| Sub Total Credit Hrs. in Fifth Semester | 17 | ||
| SEMESTER – VI (YEAR THREE) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Elective -III | Elective | 3 (3-0) |
| 2 | Data Structures & Algorithms | Distribution | 4 (3-1) |
| 3 | Data Mining | Core | 3 (2-1) |
| 4 | Elective-IV | Elective | 3 (3-0) |
| 5 | Elective-V | Elective | 3 (3-0) |
| Sub Total Credit Hrs. in Sixth Semester | 16 | ||
| Final Year | |||
| SEMESTER – VII (YEAR FOUR) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Final Year Project - I | Major | 3 (0-3) |
| 2 | Database Systems | Distribution | 4 (3-1) |
| 3 | Data Visualization | Core | 3 (2-1) |
| 4 | Data Warehousing & Business Intelligence | Core | 3 (2-1) |
| 5 | Elective-VI | Elective | 3 (0-3) |
| Sub Total Credit Hrs. in Seventh Semester | 16 | ||
| SEMESTER – VIII (YEAR FOUR) | |||
| S.No | Course Title | Subject Area | Credit Hours |
| 1 | Final Year Project - II | Major | 3 (0-3) |
| 2 | Elective -VII | Elective | 3 (0-3) |
| 3 | Elective - VIII | Elective | 3 (0-3) |
| 4 | Big Data Analytics | Core | 3(2-1) |
| 5 | Elective - IX | Elective | 3 (0-3) |
| Sub Total Credit Hrs. in Eight Semester | 15 | ||
BS Data Science Electives
| S.No. | Computing Core (Distribution) | ||
|---|---|---|---|
| 1 | Programming Fundamentals (3+1) | ||
| 2 | Discrete Structures (3-0) | ||
| 3 | Object Oriented Programming (3-1) | ||
| 4 | Database Systems (3-1) | ||
| 5 | Data Structures & Algorithms (3-1) | ||
| 6 | Information Security (3-0) | ||
| 7 | Computer Networks (3-1) | ||
| 8 | Operating System (3-1) | ||
| 9 | Software Engineering (3-0) | ||
| 10 | Final Year Project - I (0-2) | ||
| 11 | Final Year Project – II (0+4) | ||
| 12 | Introduction to Information & Communication Technologies (2+1) | ||
| S.No. | Data Science Core | ||
| 1 | Advance Statistics (3-0) | ||
| 2 | Introduction to Data Science (2-1) | ||
| 3 | Data Mining (2-1) | ||
| 4 | Data Visualization (2-1) | ||
| 5 | Data Warehousing & Business Intelligence (2-1) | ||
| 6 | Big Data Analytics (2-1) | ||
| S.No. | Data Science Electives | ||
| 1 | Advance Database Management Systems (3-0) | ||
| 2 | Machine Learning (2-1) | ||
| 3 | Deep Learning (3-0) | ||
| 4 | Theory of Automata & Formal Languages (3-0) | ||
| 5 | Artificial Neural Networks (2-1) | ||
| 6 | Business Process Analysis (3-0) | ||
| 7 | Platform & Architecture for Data Science (3-0) | ||
| 8 | Privacy Preservation (3-0) | ||
| 9 | Speech Processing (3-0) | ||
| 10 | Cloud Computing (3-0) | ||