BS Data Science
We are currently immersed in an information age, where each day sees the creation of vast amounts of data, measured in zettabytes, worldwide. For this data to be valuable, it must be effectively processed and analyzed to inform decisions. Data science has emerged as a pivotal field that equips us with the necessary tools and techniques to convert raw data into actionable insights. Consequently, there is a growing demand for highly skilled professionals who excel in data processing and analysis.
In response to current and anticipated market demands, the Bachelor of Science (BS) in Data Science program has been designed to provide students with advanced knowledge and skills tailored to the dynamic needs of modern computing technologies. This program aims to prepare graduates for careers in data analysis by integrating core statistical methods with computational techniques from computer science, ensuring they are ready to tackle the complexities of data-driven decision-making
Objectives
- Acquire expertise in tools, techniques, applications, and models for effective data processing and visualization.
- Gain fundamental data science knowledge and skills needed to design and execute diverse solutions.
- Strengthen problem-solving abilities through the development of analytical, logical, and critical thinking skills for addressing data and analysis challenges.
- Master cutting-edge tools and techniques to maintain data security and integrity at both individual and organizational levels.
- Cultivate ethical and social values, along with communication and presentation skills.
- Develop proficiency in data cleaning and preprocessing to ensure high-quality, actionable insights.
- Learn to apply statistical methods and machine learning algorithms to solve complex problems and make data-driven decisions.
- Enhance collaborative skills by working on group projects and understanding team dynamics in a data science context.
- Stay updated with industry trends and emerging technologies to adapt to the evolving field of data science.
Eligibility
(I) for Students 12-Years Education
- 50% in Intermediate (HSSC) or a comparable certification approved by IBCC is required.
- Deficiency courses: Students who did not pass Mathematics at the required (Intermediate) level mentioned above point can apply, but they need to complete two additional 6-credit hour Mathematics courses in the first two semesters.
Course exemption is not allowed.
Degree Duration
This program is a four-year full-time course, divided into eight semesters, each semester has at least 18 weeks for teaching and examinations.
Study Scheme
Semester 1
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS101
|
Introduction to Computing |
Required |
|
3 (Theory:3), Practical:0) |
|
|
ENG101
|
English Comprehension |
Required |
|
3 (Theory:3), Practical:0) |
|
|
MTH101 |
Calculus And Analytical Geometry |
Required |
|
3 (Theory:3), Practical:0) |
|
|
MGT301 |
Principles of Marketing |
Elective |
|
3 (Theory:3), Practical:0) |
|
|
MGT503 |
Principles of Management |
Elective |
|
3 (Theory:3), Practical:0) |
|
|
MTH100 |
General Mathematics |
Deficiency |
|
3 (Theory:3, Practical:0) |
|
|
VU001 |
Introduction to e-Learning |
Required |
|
1 (Theory:1, Practical:0) |
|
|
PAK301 |
Pakistan Studies |
Required |
|
2 (Theory:2, Practical:0) |
|
|
ETH202 |
Ethics (for Non-Muslims) |
Elective |
|
2 (Theory:2, Practical:0) |
|
|
ISL202 |
Islamic Studies |
Elective |
|
2 (Theory:2, Practical:0) |
|
Semester 2
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS201
|
Introduction to Programming |
Required |
|
3 (Theory:3), Practical:0) |
|
|
MTH202
|
Discrete Mathematics |
Required |
|
3 (Theory:3), Practical:0) |
|
|
ENG201 |
Business and Technical English Writing |
Required |
ENG101 |
3 (Theory:3), Practical:0) |
|
|
MTH501 |
Linear Algebra |
Required |
|
3 (Theory:3), Practical:0) |
|
|
ECO401 |
Economics |
Elective |
|
3 (Theory:3), Practical:0) |
|
|
MGT211 |
Introduction To Business |
Elective |
|
3 (Theory:3, Practical:0) |
|
|
MTH104 |
Sets and Logic |
Deficiency |
|
3 (Theory:3, Practical:0) |
|
|
CS201P |
Introduction to Programming (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
Semester 3
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS205
|
Information Security |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS301
|
Data Structures |
Required |
CS201 |
3 (Theory:3), Practical:0) |
|
|
CS302 |
Digital Logic Design |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS304 |
Object Oriented Programming |
Required |
|
3 (Theory:3), Practical:0) |
|
|
STA301 |
Statistics and Probability |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS301P |
Data Structures (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
|
CS302P |
Digital Logic Design (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
|
CS304P |
Object Oriented Programming (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
Semester 4
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS401
|
Computer Architecture and Assembly Language Programming |
Required |
CS201 |
3 (Theory:3), Practical:0) |
|
|
CS403
|
Database Management Systems |
Required |
CS201 |
3 (Theory:3), Practical:0) |
|
|
CS502 |
Fundamentals of Algorithms |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS504 |
Software Engineering – I |
Required |
|
3 (Theory:3), Practical:0) |
|
|
MGT501 |
Human Resource Management |
Elective |
|
3 (Theory:3), Practical:0) |
|
|
MGT602 |
Entrepreneurship |
Elective |
|
3 (Theory:3, Practical:0) |
|
|
CS401P |
Computer Architecture and Assembly Language Programming (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
|
CS403P |
Database Management Systems (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
Semester 5
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS604
|
Operating Systems |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS607
|
Artificial Intelligence |
Required |
|
3 (Theory:3), Practical:0) |
|
|
MCM301 |
Communication skills |
Required |
|
3 (Theory:3), Practical:0) |
|
|
MTH401 |
Differential Equations |
Required |
|
3 (Theory:3), Practical:0) |
|
|
STAT404 |
Regression and Correlation |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS604P |
Operating Systems (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
|
CS607P |
Artificial Intelligence (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
Semester 6
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS402
|
Theory of Automata |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS442
|
Introduction to Data Science |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS610 |
Computer Networks |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS621 |
Parallel and Distributed Computing |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS620 |
Modelling and Simulation |
Elective |
|
3 (Theory:3), Practical:0) |
|
|
CS631 |
Deep Learning |
Elective |
|
3 (Theory:3, Practical:0) |
|
|
CS610P |
Computer Networks (Practical) |
Required |
|
1 (Theory:0, Practical:1) |
|
Semester 7
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS619
|
Final Project – CS619 |
Required |
|
3 |
|
|
CS435
|
Cloud Computing |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS614 |
Data Warehousing |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS626 |
Data Mining Techniques |
Required |
|
3 (Theory:3), Practical:0) |
|
|
CS628 |
Machine Learning |
Required |
|
3 (Theory:3), Practical:0) |
|
Semester 8
| Course Code | Subject Title | Type | Pre Requisite | Credit Hours | Specialization |
|---|---|---|---|---|---|
|
CS619
|
Final Project |
Required |
|
3 |
|
|
CS625
|
Professional Practices |
Required |
|
3 (Theory:3, Practical:0) |
|
|
CS641 |
Big Data Analytics |
Required |
|
3 (Theory:3, Practical:0) |
|
|
CS642 |
Data Visualization |
Required |
|
3 (Theory:3), Practical:0) |
|
|
PSY101 |
Introduction to Psychology |
Elective |
|
3 (Theory:3), Practical:0) |
|
|
SOC101 |
Introduction to Sociology |
Elective |
|
3 (Theory:3, Practical:0) |
|
Assessment of Study Progress
The evaluation of student’s academic progress is based on continuous assessment throughout the semester, including assignments, online quizzes, Graded Discussion Boards (GDBs), mid-term and final term exams. The mid and finalterm exams are conducted at VU’s designated exam centers and typically account for 80-85% of the total marks for a course.
Semester Work | Apply | Graded/Non-Graded | Marks | Count |
Quizzes |
| Graded | 10-20% | 2-4 /course |
GDBs/Viva |
| 1/course | ||
Assignments/Project |
| 2-4 / Course | ||
MDBs |
| Non-Graded |
| 1 / Lecture (Module) |
Lab Work |
| Non-Graded |
| 1/Week |
Teaching Practice |
|
|
|
|
Live Sessions |
| Non-Graded |
| 1 / Week |
Attendance |
|
|
|
|
Mid Term Exam |
| Graded | 20-30% | 1 /Course |
Final Term Exam |
| Graded | 60% | 1/Course |
Any Other (Please specify) |
|
|
|
|
Project
To complete their BS degree from the CS & IT department, all students must pass the “CS619 Final Year Project” course worth 6 credit hours during the 7th semester of their program. If a student is unable to pass CS619 by the end of the 8th semester, they may choose to take two 3-credit hour courses instead to fulfill their degree requirements. This option is only available to students who are facing hardship.
Degree Awarded
- The minimum requirement for eligibility is the completion of the requisite courses, which must add up to at least 134 credit hours.
- A minimum CGPA (Cumulative Grade Point Average) of 2.0 out of 4.0 is required to be considered eligible.
Important notice
In accordance with HEC guidelines, students who are enrolled in a BS program are permitted to switch to an associate degree (AD) program after completing four semesters (2 years) of their BS program. If a student chooses to do so, they must fulfill the following requirements:
- The student must complete and pass a final project worth 3 credit hours.
- The student must have successfully completed all the courses necessary to fulfill the requirements for the corresponding associate degree (AD).
- If there are any missing requirements, the student must fulfill them by passing the remaining courses.
- All regulations regarding changes in academic programs, as established by the university, will be enforced.
If a student wishes to leave their BS program before completing four semesters, they must apply for a study change program. These guidelines will be followed in this case.
- Curriculum or course plan for the corresponding associate degree program
- Extra courses will be considered non-credit courses and will not contribute towards the completion of the program.
Fee Structure
| Degree Program | Admission Fee | Registration Fee | Security Fee | Admission Processing Fee | Semester Enrollment Fee | Per credit hour fee | Fee of Convocation | Fee payable at admission |
|---|---|---|---|---|---|---|---|---|
|
BS |
3,000 |
2,500 |
2,000 |
500 |
1,000 |
935 |
2,000 |
11,905 |
Semester Fee
Semester fee Approximately Rs. 17000/-
Disclaimer
- The courses are regularly reviewed to ensure they are up-to-date with those taught at top universities, taking into account the needs of local industry and the job market.
- The University may change its fee structure from time to time.