DATA SCIENCE
DATA SCIENCE
The Bachelor’s program in Data Science at DHA Suffa University builds a strong foundation in key data science principles, including algorithms, data structures, data management, and machine learning. Graduates are well-prepared for careers in data analysis, integrating essential statistical concepts with computational techniques from computer science.
MISSION
To educate and empower students to become ethical and proficient in data science, capable of addressing complex challenges through a comprehensive curriculum, practical experience, and a global perspective.
INTRODUCTION
DHA Suffa university’s BS in Data Science program is designed to help students understand vast volume of data generated as a result of latest modern data revolution. The specific syllabus enables the students to design sophisticated computing techniques in order to process this volume of information by designing expertise in machine learning, big data analysis and algorithmic complexity as well as skill development in mathematics and statistical modeling and algorithm design.
This program is suitable for students interested in either career in industry or to pursue more specialized graduate study. This program helps students to prepare career in data analysis, combining foundational statistical concepts with computational principal from computer science. The major part of this program is final year project, that teaches students about implementation of statistical and computational principal to solve large range, real world data analysis problems.
BS (Data Science) program is a four-year degree program and accredited by NCEAC (National Computing Education Accreditation Council).
Data Science students will learn to
- Define information need for individuals and organizations
- Select and transform data to increase helpfulness for solving practical problems
- Analyze unstructured data to create meaning information
- Create visualizations for data exploration
- Handle large volume of data sources
- Secure data in consistent ways with legal and organizational considerations
Learning outcomes
- Understanding of applying analytic techniques and algorithms on large dataset to extract meaningful information
- Hands on experience with specific software tools, languages, data models and data processing with visualization
- Capability to communicate results of analysis visually and verbally to audience
Career Path
- Data Architect
- Infrastructure Architect
- Data Scientist
- Data Analyst
- Data Engineer
- Machine Learning Engineer
Prospective Firms/Companies
- Real Estate Industry
- Hospital Industry
- Social Media Data Analytics Firms
- Food and Supply Industry
- Banking Sector
- Airline Industry
- Communication & Transportation Industry
- Government & Private Sector
- Insurance Industry
Eligibility Criteria for BS (Data Science)
- Intermediate or an equivalent examination (Pre-Engineering/ Pre-Medical/ General Science) with at least 50% marks; Or
- A-Level with minimum 3 passes in principal subjects
- Applicants with O-Level / A-Level and Technical studies must submit equivalence from IBCC
- At least 50% marks in DSU Entrance Test
REQUIRED DOCUMENTS
Applicants are required to bring a set of copied documents for application processing:
- Matriculation / O-Level / Equivalent Marks Sheet / Statements and Certificates
- Intermediate / Equivalent / A-Level Marks Sheet / Statements and Certificates
- Applicants with O-Level / A-Level and Technical studies must submit equivalence from IBCC
- College Provisional Certificate / Promotion Certificate by the respective Board
- A copy of CNIC and B-Form
- 3 passport-sized photographs with white background
- Admit Card / Statement of Entry is required, if awaiting result
OPTIONAL:
Valid test scores of NEDUET (current year), NTS NAT (IE/ICS) (current year) or SAT II (within two years) if appeared. Applicants Submitting NEDUET, NTS NAT (IE/ICS) or SAT II test scores shall be exempted from taking DSU’s entrance test. However, candidates who do not have good enough NEDUET, NTS NAT (IE/ICS) or SAT II scores will be advised to appear in the DSU Admissions Test for better chances of securing admission.
Program: BS DATA SCIENCE
Semester-1
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-1201 |
Introduction to ICT |
3+1 |
|
|
2 |
DS-1001 |
Programming Fundamentals |
3+1 |
|
|
3 |
DS-1003 |
Discrete Structures |
3+0 |
|
|
4 |
BS-1301 |
Calculus & Analytic Geometry |
3+0 |
|
|
5 |
HU-1002 |
English Composition & Comprehension |
2+0 |
|
Semester-2
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-1002 |
Object Oriented Programming |
4(3-1) |
Programming Fundamentals |
|
2 |
DS-1004 |
Database Systems |
4(3-1) |
|
|
3 |
BS-1302 |
Linear Algebra |
3(3-0) |
Calculus & Analytic Geometry |
|
4 |
BS-1402 |
Probability & Statistics |
3(3-0) |
|
|
5 |
HU-2001 |
Communication & Presentation Skills |
3(3-0) |
English Composition & Comprehension |
Semester-3
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-2005 |
Data Structures & Algorithms |
4(3-1) |
Programming Fundamentals |
|
2 |
DS-2006 |
Information Security |
3(3-0) |
|
|
3 |
DS-2101 |
Artificial Intelligence |
4(3-1) |
Object Oriented Programming |
|
4 |
DS-2102 |
Digital Logic Design |
4(3-1) |
|
|
5 |
BS-2302 |
Differential Equations |
3(3-0) |
Cal. & Anal. Geometry |
Semester-4
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-2007 |
Computer Networks |
4(3-1) |
|
|
2 |
DS-2104 |
Computer Org. & Assembly Language |
4(3-1) |
Digital Logic Design |
|
3 |
DS-2103 |
Analysis of Algorithms |
3(3-0) |
Data Structures & Algo |
|
4 |
DS-2402 |
Introduction to Data Science |
3(2-1) |
Artificial Intelligence |
|
5 |
DS-2401 |
Advance Statistics |
3(3-0) |
Probability & Statistics |
Semester-5
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-3008 |
Operating System- |
4(3-1) |
Data Structures & Algorithms |
|
2 |
DS-3403 |
Data Mining |
3(2-1) |
Adv Stat, Intro. to DS |
|
3 |
DS-3405 |
Data Warehousing & Business Intel. |
3(2-1) |
Intro. to Data Science |
|
4 |
|
DS Elective-1 |
3(3-0) |
|
|
5 |
|
DS Elective-2 |
3(2-1) |
|
|
6 |
|
University Elective-1 |
3(3-0) |
|
Semester-6
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-3105 |
Parallel & Distributed Computing |
3(2-1) |
OOP, Operating Sys |
|
2 |
DS-3406 |
Big Data Analytics |
3(2-1) |
Intro. to Data Science |
|
3 |
DS-3404 |
Data Visualization |
3(2-1) |
Data Warehouse & BI |
|
4 |
|
DS Elective-3 |
3(3-0) |
|
|
5 |
|
DS Elective-4 |
3(3-0) |
|
|
6 |
|
University Elective-2 |
3(3-0) |
|
Semester-7
| S.NO | COURSE CODE | COURSE | CH | PRE-REUISITE |
|---|---|---|---|---|
|
1 |
DS-4011 |
Final Year Project – I |
2(0-2) |
|
|
2 |
DS-4009 |
Software Engineering |
3(3-0) |
|
|
3 |
|
University Elective-3 |
3(3-0) |
|
|
4 |
HU-2009 |
Technical & Business Writing |
3(3-0) |
Communication & Presentation Skills |
|
5 |
HU-2201 |
Islamic Studies/ Ethics |
2(2-0) |
|
Semester-8
| S.NO | COURSE CODE | COURSE | CH | PRE-REQUISITE |
|---|---|---|---|---|
|
1 |
DS-4012 |
Final Year Project – II |
4(0-4) |
Final Year Project – I |
|
2 |
|
University Elective-4 |
3(3-0) |
|
|
3 |
DS-4201 |
Professional Practices |
3(3-0) |
|
|
4 |
|
University Elective – IV |
|
|
|
5 |
HU-2101 |
Pakistan Studies |
2(2-0) |
|
| # | COURSE GROUP | CREDIT HOURS | MIN. NO OF COURSES |
|---|---|---|---|
|
1 |
General Education |
19 |
7 |
|
2 |
University Electives |
12 |
4 |
|
3 |
Mathematics and Science Foundation |
12 |
4 |
|
4 |
Computing Core |
39 |
11 |
|
5 |
Computer Science Core |
18 |
5 |
|
6 |
DS Core (Domain Core) |
18 |
6 |
|
7 |
DS Electives (Domains Electives |
12 |
4 |
|
Total |
130 |
47 |
|
| PROGRAM | ONE TIME CHARGES (ONLY IN 1ST SEMESTER FEES) | PER SEMESTER | ||||
|---|---|---|---|---|---|---|
|
|
ADMISSION FEE |
CAUTION MONEY(REFUNDABLE) |
IT CHARGES |
MISC. CHARGES
|
LAB CHARGES(PER CREDIT HOUR)
|
TUITION FEE (PER CREDIT HOUR)
|
|
BS(Data Science) |
15,000 |
10,000 |
5,500 |
6,850 |
10,000 |
5,370 |
| PROGRAMS | 1ST SEMESTER FEE AMOUNT | SEMESTER FEE (WITHOUT ADM & C/ MONEY) | CREDIT HOURS 1ST SEMESTER |
|---|---|---|---|
|
BS(Data Science) |
132,530 |
107,530 |
14+ 2 |
NOTE:
- The University reserves the right to change fee/charges on yearly basis.
- MS/PhD Research Fee will be charged as per credit hour Tuition Fee.
- PhD Comprehensive Examination Fee is RS. 5000/-
- There will be an additional fee for PhD Thesis evaluation by foreign and local evaluators.
- Advance Tax (under section 236-I) shall be collected @ 5% on the entire amount of fee (if the student’s payable fee exceeds Rs. 200,000/- per annum, excluding the amount refundable).
| PLO NO | Program Objective | GA-1 | GA-2 | GA-3 | GA-4 | GA-5 | GA-6 | GA-7 | GA-8 | GA-9 | GA-10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
|
PLO-1 |
To be able to apply data analytical skills for evaluating data from multiple disciplines for fulfilling futuristic organizational decision-making needs. |
þ |
þ |
|
|
þ |
|
|
|
|
þ |
|
PLO-2 |
To demonstrate effective communication and comprehension capabilities required for data gathering, analysis, inference, and expression of derived knowledge. |
|
|
þ |
þ |
þ |
|
|
|
|
þ |
|
PLO-3 |
To become effective team players who implement data analytics solutions, while ensuring all possible ethical aspects. |
|
|
|
|
|
þ |
þ |
þ |
þ |
|
List of Graduate Attributes (GAs)
- Academic Education
- Knowledge for Solving Computing Problems
- Problem Analysis
- Design/ Development of Solutions
- Modern Tool Usage
- Individual and Team Work
- Communication
- Computing Professionalism and Society
- Ethics
- Life-long Learning
