BS Data
Science
Department
Computer Science
Level
Undergraduate
College
College of Computer Sciences & Information Systems
Program Detail
BS (Data Science) focuses on statistical analysis and theoretical computer science to develop solutions that employ robust mathematical models. These models help AI (Artificial Intelligence) and other predictive tools for data mining and reliable analysis.
This program has a twofold approach: basic principles and foundational training of statistical and mathematical aspects of data analysis. This program is, additionally, based on broad computer science principles, including algorithms, data structures, data management, and machine learning. The program is suitable for students interested either in a career in industry or more specialized graduate study. This program will prepare students for a career in data analysis, combining foundational statistical concepts with computational principles from computer science. A major component of this degree is the final year two- semester project that teaches students how to apply Data Science principles for solving large-scale, real-world data analysis problems BS (Data Science) is a four-year degree program. It requires completion of 138+2* credit hours of coursework and compulsory internship (3 credit hours) of at least six weeks at an organization ap-proved by the Institute.
Students from pre-medical background are required to additionally complete the following two mathematics courses: MAT011 Basic College Mathematics
MAT012 Intermediate College Mathematics
Eligibility Criteria: The eligibiliity criteria for admission into BS Data Science is given on Page No. 175.
Learning Outcomes for Data Science:
- Knowledge of how to apply analytical techniques and algorithms (including statistical and data mining approaches) to large data sets for extracting meaningful insights.
- Acquisition of hands-on experience with relevant software tools, languages, data models, and environments for data processing and
- Ability to communicate results (visually and verbally) to a broad
Career Path:
1. Data Architect | 2. | Infrastructure Architect |
3. Data Scientist | 4. | Data Analyst |
5. Data Engineer | 6. | Machine Learning Engineer & Architect |
Prospective Firms/Companies:
1. Real Estate Industry | 2. | Hospital Industry |
3. Social Media Data Analytics Firms | 4. | Food and Supply Industry |
5. Banking Sector | 6. | Airline Industry |
7. Communication & Transportation Industry | 8. | Government & Private Sector |
9. Insurance Industry |
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BS Data Science: Course Distribution:
Area | Credit Hours | Courses |
Computing Core [CC] | 49 | 14 |
Domain Core [DC] | 18 | 6 |
Domain Elective [DE] | 21 | 7 |
Mathematics & Supporting Courses [MSC] | 12 | 4 |
Elective Supporting Courses [ESC] | 3 | 1 |
General Education Requirements [GER] | 31 | 13 |
Professional Certification | 3 | 1 |
Internship | 3 | 1 |
Total | 140* | 48* |
Note for Non-Muslim Students
Non-Muslim students will take a suitable 2-credit-hour General Education course in place of Understanding of the Holy Quran, as approved by the Institute’s statutory bodies.
A nine-hour non-credit ‘Pre-sessional Intensive English module’ is mandatory for freshmen who scored below 50% in the English portion of the IOBM admission test. This nine-hour pre-sessional course is conducted one week before the start of the Semester.
Program Structure (Semester Wise):
Semester I
Course Code | Course Name | Credit Hours | Pre-requisite |
CSP111 CSP111 | Introduction to Information & Communication Technology [GER] Introduction to Information & Communication Technology Lab | 2+0 0+1 | – |
CSP121 CSP121 | Programming Fundamentals [CC] Programming Fundamentals Lab | 3+0 0+1 | – |
BCN101 | Academic English [GER] | 3+0 | – |
PHY112 PHY112 | Applied Physics [GER] Applied Physics Lab | 2+0 0+1 | – |
MAT110 | Calculus and Analytical Geometry [GER] | 3+0 | – |
Semester Total Credit Hours | 16 (13+3) |
Semester II
Course Code | Course Name | Credit Hours | Pre-requisite |
CSP122 CSP122 | Object Oriented Programming [CC] Object Oriented Programming Lab | 3+0 0+1 | CSP121 |
BCN201 | Persuasive & Analytical Writing for Business Communication [MSC] | 3+0 | BCN101 |
CSP231 | Discrete Structure [GER] | 3+0 | – |
CSP141 CSP141 | Digital Logic and Design [CC] Digital Logic and Design Lab | 3+0 0+1 | PHY112 |
MAT211 | Multivariable Calculus [MSC] | 3+0 | MAT110 |
REL102/ REL112 | Understanding of Holy Quran-I [GEC] / Comparative Religion-I [GEC] | 0+1 0+1 | – |
Semester Total Credit Hours | 18 (16+2) |
Semester III
Course Code | Course Name | Credit Hours | Pre-requisite |
CSP221 CSP221L | Data Structures and Algorithms [CC] Data Structures and Algorithms Lab | 3+0 0+1 | CSP121 |
BCN202 | Business and Professional Speech [GER] | 3+0 | COM107 |
CSP241 CSP241 | Comp. Organization & Assembly Lang. [CC] Comp. Organization & Assembly Lang. Lab | 3+0 0+1 | – |
MAT201 | Linear Algebra [MSC] | 3+0 | MAT110 |
REL101 | Islamic Studies [GER] | 2+0 | – |
REL103/ REL113 | Understanding of Holy Quran-II [GEC] / Comparative Religion-I GEC | 0+1 0+1 | REL102/ REL112 |
Semester Total Credit Hours | 17 (15+2) |
Semester IV
Course Code | Course Name | Credit Hours | Pre-requisite |
CSP261 CSP261 | Introduction to Database Systems [CC] Introduction to Database Systems Lab | 3+0 0+1 | – |
DSP162 DSP162 | Introduction to Data Science [DC] Introduction to Data Science Lab | 2+0 0+1 | – |
STS101 | Probability Theory and Statistics [MSC] | 3+0 | – |
CSP222 CSP222 | Operating Systems [CC] Operating Systems Lab | 3+0 0+1 | CSP221 |
CSP228 | Design & Analysis of Algorithms [CC] | 3+0 | CSP221 |
Semester Total Credit Hours | 17 (14+3) |
Semester V
Course Code | Course Name | Credit Hours | Pre-requisite |
DSP365 DSP365 | Advanced Statistics [DC] Advanced Statistics Lab | 2+0 0+1 | STS101 |
CSP371 CSP371 | Computer Networks [CC] Computer Networks Lab | 2+0 0+1 | – |
MGT102 | Introduction to Management & Organizational Behavior [GER] | 2+0 | – |
CSP311 | Professional Practices [GER] | 2+0 | > 60 Credit Hours |
DSP366 DSP366 | Data Mining [DC] Data Mining Lab | 2+0 0+1 | – |
CSP381 CSP381 | Artificial Intelligence [CC] Artificial Intelligence Lab | 2+0 0+1 | STA203 |
PSC101 | Pakistan Studies [GER] | 2+0 | |
Semester Total Credit Hours | 18 (15+3) |
Semester VI
Course Code | Course Name | Credit Hours | Pre-requisite |
DSPxxx | DS Elective 1 [DE] | 3+0 | – |
DSPxxx | DS Elective 2 [DE] | 3+0 | – |
SEP151 | Introduction to Software Engineering [CC] | 3+0 | – |
DSP368 DSP368 | Data Visualization [DC] Data Visualization Lab | 2+0 0+1 | – |
DSP367 DSP367 | Data Warehouse and Business Intelligence [DC] Data Warehouse and Business Intelligence Lab | 2+0 0+1 | – |
CSP313 | Tech Entrepreneurship [GER] | 2+0 | – |
Semester Total Credit Hours | 17 (15+2#) |
Semester VII
Course Code | Course Name | Credit Hours | Pre-requisite |
DSPxxx | DS Elective 3 [DE] | 3+0 | – |
DSPxxx | DS Elective 4 [DE] | 3+0 | – |
MRK101 | Fundamentals of Marketing Management [ESC] | 3+0 | – |
SSC303 | Service Learning and Civic Responsibilities [GER] | 2+0 | – |
CSP443 | Parallel and Distributed Computing [DC] | 3+0 | CSP222 & CSP371 |
DSPxxx | Professional Certification | 3+0 | – |
DSP491 | Final Year Project I [CC] | 0+3 | – |
Semester Total Credit Hours | 20 (17+3#) |
Semester VIII
Course Code | Course Name | Credit Hours | Pre-requisite |
DSPxxx | DS Elective 5 [DE] | 3+0 | |
DSPxxx | DS Elective 6 new [DE] | 3+0 | |
DSPxxx | DS Elective 7 new [DE] | 3+0 | |
CSP472 | Information Security [CC] | 3+0 | |
PSC302 | Ideology and Constitution of Pakistan [GER] | 2+0 | |
DSP492 | Final Year Project II [CC] | 0+3 | DSP491 |
Semester Total Credit Hours | 17 (14+3#) |
Total 138 Credit Hours (#Lab depends on the selection of elective courses by students)
Domain Elective:
DSPxxx Large Language Models | DSP468 Social Network Analysis |
DSP481 Machine Learning | DSP462 Information Extraction |
DSP483 Artificial Neural Networks | CSP331 Theory of Automata & Formal Lang. |
DSP465 Information Retrieval | DSP485 Deep Learning |
DSP461 Platform & Architecture for Data Science | CSP325 HCI and Computer Graphics CSP325 HCI and Computer Graphics Lab |
DSP464 Speech Processing | DSP467 Big Data Analytics |
CSP362 Advanced Database Systems CSP362 Advanced Database Systems Lab | DSP463 Natural Language Processing |
DSP469 Topics in Data Science | CSP479 Cloud Computing |
Course Detail
BS (Mathematics & Economics) is an inter-disciplinary undergraduate program for students with robust mathematical skills and keen interest in economics. This program is a perfect blend of pure and applied mathematics which does not only ensure a solid quantitative foundation for both disciplines but also provide phenomenal coverage of Statistics, Actuarial sciences and Finance. The training this degree provides is a unification of critical economic analysis and strong mathematical skills, which can help student pursue an esteemed position in financial services industry, growth and development centers, business enterprises, as well as public sector. Graduates with this degree are increasingly valued by employers because of their critical reasoning and sound knowledge as much of the economic theory is currently presented in terms of mathematical models. This opens more career options than the ones traditionally available to either mathematics or economics majors. This degree is also a paragon for those who aim for Master/ Doctoral degree in Economics, Finance, Mathematics, Statistics, Actuarial Sciences or other related fields.
The BS (Mathematics & Economics) is a four-year program. Applicants who have successfully completed H.Sc with minimum 50% marks in Pre-Engineering or in General Group (with Mathematics) or A-Levels with a minimum 2 Cs in three principal subjects (with Mathematics) are eligible to apply for admission. Graduation requirement is the completion of 141 credit hours of course work and 3 credit hours of project approved by college. Students must take a minimum load of 12 credit hours (four courses) or a maximum load of 18 credit hours (six courses) in a semester. In order to obtain the BS degree in four years, a student is required to cover twelve courses in a year. Full load of six courses can be taken each in the Fall and Spring semesters with an option of four courses in the latter and making up for the short fall in summer session. Students must maintain a CGPA of 2.5 for the conferment of degree.
BS Mathematics and Economics students learn to:
An ability to develop mathematical thinking, evolving from a computational / procedural understanding of mathematics to a broad understanding that involves logical reasoning, generalization, inference and formal proof.
Learn the fundamental aspects of economics, formulation and use of quantitative models arising in social science, business and other contexts.
Acquire an understanding of basic pure mathematics, and of the role of logical argument in mathematics.
- Learn to use basic econometric methods to quantify uncertainty with confidence intervals; use regression to infer causal relationships; and use regressions for prediction
Learning Outcomes for Business Analytics Students include:
An ability to communicate effectively with the educational and business community and with society at large about mathematical and economical principles, concepts, and solution to problems with precision and adherence in written, oral and graphical form about concrete questions and to prepare well-organized written arguments that clearly state assumptions/ hypotheses supported by evidence.
An ability to optimally apply economic analysis to everyday economic problems in the real world. This shall allow them to understand current events and evaluate potential policy proposals. Moreover, an appreciation shall be developed to evaluate the role played by assumptions in situations that reach various conclusions to a specific economic or policy problem.
Be equipped with skills to apply optimization models to consumer, producer, and market theories and to use game theory to analyze the strategic behavior of individuals and firms.
Be equipped with the investigative skills necessary for conducting original economic research and participating effectively in project teams.
Career Path
Data analyst
Investment analyst
Research scientist
Statistician
Required Courses
| Area | Course Code/Title |
|---|---|
| Mathematics | MTH104 Calculus I MTH105 Calculus II MTH203 Introduction to Formal Mathematics MTH204 Linear Algebra MTH213 Introduction to Computing MTH224 Multivariable Calculus MTH251 Number theory MTH301 Real Analysis I MTH311 Real Analysis II MTH344 Introduction to Differential Equations MTH346 Partial Differential Equations MTH350 Topics in Mathematical Economics MTH405 Numerical Analysis MTH427 Topology MTH401 Complex Analysis MTH433 Optimization Techniques MTH437 Functional Analysis |
| Economics | ECO101 Principles of Microeconomics ECO102 Principles of Macroeconomics ECO103 Intermediate Microeconomics ECO105 Intermediate Macroeconomics ECO207 Game Theory ECO208 Development Economics ECO301 Managerial Economics ECO302 International Trade ECO303 Financial Economics ECO307 Monetary theory and Policy ECO402 Pakistan Economic Policy ECO410 Econometrics I ECO412 Econometrics II |
| Management Information System | MIS402 Computer Concepts and Applications |
| Political Sciences | PSC301 Pakistan Studies |
| Religious Studies | REL101 Islamic Studies |
| Statistics | STA203 Probability Theory and Statistics STA301 Model and Inference STA302 Methods of Data Analysis STA303 Time Series Analysis STA305 Applied Regression Analysis |
| Communication | COM107 Academic English COM202 Business and Professional Speech COM203 Methods in Business Writing |
| Language | LAN 10* Foreign Language I LAN 20** Foreign Language II *1 = Introduction to Arabic *2 = Introduction to French *4 = Introduction to German *6 = Introduction to Italian *8 = Introduction to Chinese **1 = Intermediate Arabic **2 = Intermediate French **4 = Intermediate German **6 = Intermediate Italian **8 = Intermediate Chinese |
Elective Courses
| Area | Course Code/Title |
|---|---|
| Mathematics | MTH211 Actuarial Mathematics MTH205 Financial Mathematics MTH207 Stochastic Models and Mathematical Finance MTH421 Abstract Algebra MTH423 Combinatorics MTH430 Operations Research MTH439 Introduction to Dynamical Systems |
| Economics | ECO305 Topics in Microeconomics ECO306 Topics in Macroeconomics ECO414 Islamic Economics ECO416 Growth Theories ECO418 Resource & Environmental Economics ECOXXX Energy Economics ECOXXX Public Finance ECOXXX Economics of Logistics ECOXXX History of Economics Idea ECOXXX Agronomics |
Course Structure
| Semester One | Semester Two | Semester Three | Semester Four |
|---|---|---|---|
| Islamic studies Academic English Principles of Microeconomics Calculus I Foreign Language I Computer concepts and applications | Pakistan Studies Methods in Business Writing Principles of Macroeconomics Calculus II Foreign Language II Probability & Statistics | Business and Professional Speech Intermediate Microeconomics Model and inference Introduction to Formal Mathematics Multi variable Calculus Introduction to Computing | Intermediate Macroeconomics Development Economics Game Theory Methods of Data Analysis Number theory Linear Algebra |
| Semester Five | Semester Six | Semester Seven | Semester Eight |
| International Trade Managerial Economics Applied Regression Analysis Topics in Mathematical Economics Real Analysis I Introduction to Differential Equations | Monetary theory & Policy Financial Economics Econometrics I Numerical Analysis Real Analysis II Partial Differential Equations | Econometrics II Pakistan Economic Policy Topology Complex Analysis Economics Elective I Mathematics Elective I | Time Series Analysis Optimization Techniques Functional Analysis Economics Elective II Mathematics Elective II Project |