BS Data Analytics is a four-year undergraduate degree program designed to develop graduates with integrated capability in mathematics, statistics, computing, communication, and responsible use of artificial intelligence and analytics technologies. The curriculum emphasizes analytical reasoning, evidence-based decision-making, and applied problem-solving across industrial, scientific, public-sector, and emerging interdisciplinary contexts.
The School of Science intends to produce BS Data Analytics graduates who apply foundational knowledge to practical problems of national, industrial, and societal relevance; work effectively in multidisciplinary settings; use data analytics, AI, and quantitative methods to generate actionable insights; and continue professional growth through research, innovation, entrepreneurship, and lifelong learning.
| Code | Course Title | Credit Hours | Status |
|---|---|---|---|
| MF-0001 | Mathematics Foundation-I | 3(3,0) | Deficiency / non-credit |
| MF-0002 | Mathematics Foundation-II | 3(3,0) | Deficiency / non-credit |
| PEO No. | PEO Description |
|---|---|
| PEO-1 | Apply foundational knowledge and analytical reasoning to solve problems of national and industrial importance |
| PEO-2 | Practice effectively through communication, teamwork, ethics, and professional responsibility. |
| PEO-3 | Contribute through innovation, research, and data-informed improvement for society and industry. |
| PEO-4 | Pursue leadership, entrepreneurship, and lifelong learning. |
| PLO No. | PEO No. | PLO Title | PLO Description |
|---|---|---|---|
| PLO-1 | PEO-1 | Knowledge Integration | Ability to integrate mathematics, statistics, computing, and domain knowledge in support of analytics tasks. |
| PLO-2 | PEO-1 | Problem Analysis | Ability to identify, formulate, analyze, and structure complex problems using analytical reasoning and evidence. |
| PLO-3 | PEO-1, PEO-3 | Solution Design and Development | Ability to design, develop, and refine appropriate analytics solutions, workflows, and decision-support artifacts. |
| PLO-4 | PEO-1, PEO-3 | Investigation and Experimentation | Ability to plan and conduct investigations, experiments, and evaluations, and interpret results responsibly. |
| PLO-5 | PEO-1, PEO-3 | Modern Tool Usage | Ability to select and use modern analytical, computational, visualization, and data-management tools effectively. |
| PLO-6 | PEO-2 | Individual and Team Work | Ability to function effectively as an individual and as a member or leader in diverse teams. |
| PLO-7 | PEO-2 | Communication | Ability to communicate ideas, findings, and recommendations effectively in oral, written, visual, and technical forms. |
| PLO-8 | PEO-2 | Ethics and Professional Responsibility | Ability to recognize and act upon ethical, professional, legal, and societal responsibilities in data-driven work. |
| PLO-9 | PEO-4 | Project Management and Leadership | Ability to apply planning, coordination, and leadership principles in academic, industrial, and entrepreneurial settings. |
| PLO-10 | PEO-4 | Lifelong Learning | Ability to recognize the need for, and engage in, independent and continuous learning. |
1st Semester |
||||
| Code | Course Title | Th | Lab | Total Cr |
|
CSC-1071 |
Programming Fundamentals |
2 |
1 |
3 |
|
CS-1075 |
Introduction to ICT |
2 |
1 |
3 |
|
ENG-1091 |
Functional English |
3 |
0 |
3 |
|
MA-1101 |
Calculus and Analytical Geometery |
3 |
0 |
3 |
|
DAC-1002 |
Introduction to Data Analytics and Artificial Intelligence |
3 |
0 |
3 |
|
FQ-1001 |
Fehm-e-Quran-I |
0 |
1 |
1 |
|
Total Cr. Hrs. |
13 |
3 |
16 |
|
2nd Semester |
||||
|
DAC-1003 |
Python Programming for Artificial Intelligence |
2 |
1 |
3 |
|
CS-2078 |
Database Systems |
2 |
1 |
3 |
|
MATH-1002 |
Discrete Structures for Data Analytics |
3 |
0 |
3 |
|
STAT-1001 |
Fundamentals of Statistics for Data Analytics |
3 |
0 |
3 |
|
STAT-1002 |
Statistical Computing and Software Packages for Data Analytics |
3 |
0 |
3 |
|
FQ-1002 |
Fehm-e-Quran-II |
0 |
1 |
1 |
|
Total Cr. Hrs. |
13 |
3 |
16 |
|
3rd Semester |
||||
|
MA-2024 |
Linear Algebra |
3 |
0 |
3 |
|
ENG-2029 |
Expository Writing |
3 |
0 |
3 |
|
HU-1091 |
Islamic Studies |
3 |
0 |
3 |
|
SS-1093 |
Introduction to Psychology |
3 |
0 |
3 |
|
ELC-2001 |
Department-Approved Application Elective-I |
2 |
1 |
3 |
|
STAT-2001 |
Probability Distributions for Data Analytics |
3 |
0 |
3 |
|
Total Cr. Hrs. |
17 |
1 |
18 |
|
4th Semester |
||||
|
DAC-2001 |
Artificial Intelligence Methods for Data Analytics |
2 |
1 |
3 |
|
AIC-3072 |
Machine Learning |
2 |
1 |
3 |
|
MATH-2002 |
Multivariate and Matrix Calculus for Machine Learning |
3 |
0 |
3 |
|
MATH-2003 |
Optimization Methods for Data Analytics |
3 |
0 |
3 |
|
STAT-2002 |
Regression Analysis for Data Analytics |
3 |
0 |
3 |
|
STAT-2003 |
Statistical Inference for Data Analytics |
3 |
0 |
3 |
|
Total Cr. Hrs. |
16 |
2 |
18 |
|
5th Semester |
||||
|
DAC-3001 |
Data Engineering and Warehousing |
2 |
1 |
3 |
|
ACCT-1085 |
Principles of Accounting |
3 |
0 |
3 |
|
ELC-3001 |
Department-Approved Application Elective-II |
2 |
1 |
3 |
|
MATH-3001 |
Numerical Computing for Data Analytics |
3 |
0 |
3 |
|
STAT-3001 |
Generalized Linear Models |
3 |
0 |
3 |
|
STAT-3002 |
Experimental Design and A/B Testing for Data Analytics |
2 |
1 |
3 |
|
Total Cr. Hrs. |
15 |
3 |
18 |
|
6th Semester |
||||
|
DAC-3002 |
Deep Learning for Industry |
2 |
1 |
3 |
|
GEN-3001 |
Everyday Science |
3 |
0 |
3 |
|
DAC-3003 |
Research Methodology and Scientific Writing |
3 |
0 |
3 |
|
DAC-3004 |
Big Data Analysis and Visualization Tools |
2 |
1 |
3 |
|
STAT-3003 |
Time Series and Forecasting |
3 |
0 |
3 |
|
Total Cr. Hrs. |
13 |
2 |
15 |
|
7th Semester |
||||
|
DAC-4001 |
MLOps and Model Deployment |
2 |
1 |
3 |
|
CP-4001 |
Capstone Project-I (Data Analytics) |
0 |
3 |
3 |
|
MGT-4103 |
Entrepreneurship |
3 |
0 |
3 |
|
STAT-4001 |
Statistical Learning and Model Selection |
3 |
0 |
3 |
|
ELC-4001 |
Department-Approved Application Elective-IV |
2 |
1 |
3 |
|
INT-4001 |
Internship |
0 |
3 |
3 |
|
Total Cr. Hrs |
10 |
8 |
18 |
|
8th Semester |
||||
|
CP-4002 |
Capstone Project-II (Data Analytics) |
0 |
3 |
3 |
|
HU-2110 |
Ideology and Constitution of Pakistan |
2 |
0 |
3 |
|
SS-4072 |
Professional Practices |
3 |
0 |
3 |
|
GEN-4004 |
Foreign Language |
3 |
0 |
3 |
|
ELC-4002 |
Department-Approved Application Elective-V |
2 |
1 |
3 |
|
STAT-4002 |
Fundamentals of Stochastic Processes for Artificial Intelligence and Data Analytics |
3 |
0 |
3 |
|
Total Cr. Hrs. |
13 |
4 |
17 |
|
|
Grand Total |
110 |
26 |
136 |
|
At least 50% marks in the following:
Intermediate or equivalent (Must have Mathematics as elective subject) or (Pre-Medical as discipline)*
* All such students must pass deficiency courses of Mathematics of 6 credit hours within one year of their regular studies.
Merit Criteria:
Admissions to the Computer Science Programs of the University are decided on the basis of candidates’ marks in:
Test Required = NTU Test / NTS Test / HEC-ETC Test
Seat:
Note: Applications are entertained on Intermediate Part-I basis and merit would be finalized accordingly. However, the admitted students must meet the basic eligibility criteria of the relevant degree program on the announcement of Intermediate Part-II result failing which admission will be automatically cancelled.
| Fee Heads | 1st Semester Fee (Rs) |
|---|---|
| Admission Fee (Once at admission) | 25,000 |
| Certificate Verification Fee (Once at admission) | 2,000 |
| Red Crescent Donation (Once at admission) | 50 |
| University Card Fee (Once at admission) | 300 |
| University Security (Refundable) | 5,000 |
| Tuition Fee (First Semester) | 87,670 |
| Library Fee (Per Semester) | 3,000 |
| Examination Fee (Per Semester) | 3,000 |
| Medical Fee (Per Semester) | 2,000 |
| Student Activity Fund (Per Semester) | 2,000 |
| Endowment Fund (Per Semester) | 1,000 |
| TOTAL | 131,020 |
| Degree Fee (Once in the Last Semester) | 5,000 |
| Fee Heads | 1st Semester Fee (Rs) |
|---|---|
| Regular Fee of 1st Semester | 131,020 |
| Self Finance Fee Installments (Per Semester) | 50,000 |
| TOTAL | 181,020 |
| Particulars | Rupees |
|---|---|
| Hostel Charges (Per Semester) | 25,000 |
| Hostel Security (Refundable) | 5,000 |
| TOTAL | 30,000 |
| Note: Limited seats are available. |
|
| Particulars | Rupees |
|---|---|
| Transport Charges (Per Semester) | 12,000 |
| Note: Transport charges are only for those students who will avail transport facility. |
|
Application for a refund of Fee can be made by completing the appropriate form and submitting the proof of payment.
Tuition Fee:
The Refund of Tuition Fee is Subject to the following schedule:
| Timeline | Percentage of Fee |
|---|---|
| Upto 10th day of commencement of classes | 100% fee refund |
| Upto 15th day of commencement of classes | 80% fee refund |
| Upto 20th day of commencement of classes | 60% fee refund |
| Upto 30th day of commencement of classes | 50% fee refund |
| 31st day onwards of commencement of classes | No Refund |
Note: The timelines for refund of tuition fee are inclusive of the weekends.
Library Fee:
The library/digital library fee is completely refundable.
Transport Fee:
The transport fee is 100% refundable minus (-) the days a student has availed of that facility(es).
Security/Deposit Fee:
This fee is refundable after deductions are made for the damage/loss (if any) caused by the student.
Examination Fee:
The fee submitted as an examination fee is completely refundable.
Fee for Curricular/Co-Curricular Activities:
The Curricular/co-curricular, extra-curricular, fee is 100% refundable minus (-) the days a student has availed of that activity(es).
Admission Fee and Registration/Application Fee are non-refundable.
Certificate Verification Fee, Card Fee, Red Crescent Fund, Medical Examination Fee, Exhibition Fee and Endowment Fund are also non-refundable.
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