Department

Data Science

Level

Undergraduate

Colleges

College of Computer Sciences & Information Systems

Course Detail

BS (Data Science) has a dual emphasis on basic principles of statistics and computer science, with foundational training in statistical and mathematical aspects of data analysis. This program, in addition, develops foundation on broad computer science principles, including algorithms, data structures, data management and machine learning. The program is suitable for students interested in either a career in industry or who wish to pursue 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 statistical and computational principles to solve large-scale, real-world data analysis problems. BS (Data Science) is a four year degree program. It requires completion of 144 credit hours of course work and 2 credit hours of internship of at least six weeks at an organization approved by the Institute.

Data Science students learn to:

  • Define information needs of individuals and organizations; 
  • Select and transform data to increase usefulness for solving particular problems; 
  • Analyze and synthesize unstructured data to create actionable information; 
  • Create information visualizations for data exploration and presentation; 
  • Manage very large volume data sources from acquisition through disposal; 
  • Secure and preserve data in ways consistent with legal and organizational considerations

Learning Outcomes for Data Science Certificate students include:

  1. Knowledge of how to apply analytic techniques and algorithms (including statistical and data mining approaches) to large data sets to extract meaningful insights.
  2. Acquisition of hands-on experience with relevant software tools, languages, data models, and environments for data processing and visualization.
  3. Ability to communicate results of analysis effectively (visually and verbally) to a broad audience.

1. Knowledge of how to apply analytic techniques and algorithms (including statistical and data mining approaches) to large data sets to extract meaningful insights.

2. Acquisition of hands-on experience with relevant software tools, languages, data models, and environments for data processing and data visualization.

3. Ability to communicate results of analysis effectively (visually and verbally) to a broad audience.

Mission Statement of Data Science

Equipping people with knowledge of data science to foster innovations and have an influence on society. Cultivating an environment that values variety, ethical behavior, and research. Motivating the upcoming generation of data scientists to lead constructive global change

.Program Objective of Data Science

The following are the primary objectives of the BS. (Data Science) program that shapes our curriculum:

PO-I:

Acquire knowledge of the fundamental concepts of data science and computing and analytical skills.

PO-2:    

Leverage data science techniques to apply computational knowledge to real-world problem-solving.

PO-3:    

Effectively and ethically communicate data science findings besides addressing the societal implications in lifetime education.

Mapping of PO to the University’s Mission, Vision, and Program Mission

BS Data Science

 

Vision and Mission

Program Objectives (POs)
 

PO-1

PO-2

PO-3

University Vision

The Institute of Business Management aspires to be one of the leading institutions, nationally and internationally, for learning, research, innovation and adding value to society.

 

 

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University Mission

The Institute of Business Management (IoBM) is committed to cater to the demands of the evolving challenges of learning and teaching by enabling and leveraging technology in the pursuit of scholarship. Insightful as well as relevant research is undertaken that creates economic and societal impact. 

IoBM tutors innovative mindsets by providing a supportive environment to nurture entrepreneurship and intrapreneurship. IoBM aims to foster the ability of critical thinking through experiential learning, inquiry-based learning and case teaching across several dimensions. Aiming to prepare for the challenges of inclusive growth and sustainability, it advocates the development of future leaders to meet the economic challenges emanating from the evolving local and global paradigms.

 

 

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Department’s Vision

The College of Computer Science and Information Systems (CCSIS) is committed to emerge as one of the leading college, nationally and internationally, in computing and analytics by focusing on learning, research, technological innovation, and enhancing value to society.

 

 

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Program

 Mission

Equipping people with knowledge of data science to foster innovations and have an influence on society. cultivating an environment that values variety, ethical behavior, and research. Motivating the upcoming generation of data scientists to lead constructive global change.

 

 

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Graduate Attributes

The ten Graduate Attributes (GAs) defined by NCEAC are in alignment with the Graduate Attributes laid down in the Seoul Accord document for computing professionals. These Student Outcomes (SOs) or GAs or Graduate Attributes (GAs) provided in NCEAC Manual 2023 have been adopted by the CCSIS Department of the Institute of Business Management (IoBM). It is ensured that direct and indirect assessment methods achieve the GAs. The ten SOs or GAs or GAs are as follows:

  1. Academic Education - To prepare graduates as computing professionals
  2. Knowledge for Solving Computing Problems - Apply knowledge of computing fundamentals, knowledge of a computing specialization, and mathematics, science, and domain knowledge appropriate for the computing specialization to the abstraction and conceptualization of computing models from defined problems and requirements
  3. Problem Analysis - Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines
  4. Design/ Development of Solutions - Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations
  5. Modern Tool Usage - Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations
  6. Individual and Team Work - Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings
  7. Communication - Communicate effectively with the computing community and with society at large about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give understand clear instructions
  8. Computing Professionalism and Society - Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice
  9. Ethics - Understand and commit to professional ethics, responsibilities, and norms of professional computing practice
  10. Life-long Learning - Recognize the need and have the ability to engage in independent learning for continual development as a computing professional

Mapping of GA to PO 

 

Graduate Attributes (GAs)

Program Objectives (POs)
 

PO-1

PO-2

PO-3

GA-1: Academic Education

 

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GA-2: Knowledge for Solving Computing Problems

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GA-3: Problem Analysis

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GA-4: Design/Development of Solutions

 

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GA-5: Modern Tool Usage

 

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GA-6: Individual and Teamwork

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GA-7: Communication

 

 

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GA-8: Computing Professionalism and Society

 

 

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GA-9: Ethics

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GA-10: Life-long Learning

 

 

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  1. Data Architect
  2. Infrastructure Architect
  3. Data Scientist
  4. Data Analyst
  5. Data Engineer
  6. Machine Learning Engineer

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

Communication
COM107 Academic English
COM202 Business and Professional Speech
COM205 Persuasive & Analytical Writing for Bus. Comm

Information Technology
CSC111 Information and Communication Technology
CSC111 Information and Communication Technology LAB
CSC219 Professional Practices

Religious Studies
REL101 Islamic Studies

Political Sciences
PSC301 Pakistan Studies

Social Sciences
SSC301 History of Ideas

Economics
ECO104 Micro and Macroeconomics

Natural Sciences
PHY111 Applied Physics
SSC202 Environmental Studies

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

Mathematics & Statistics
MTH107 Calculus & Analytical Geometry
MTH204 Linear Algebra
MTH215 Differential Equations
STA203 Probability Theory and Statistics I

Computing (Core Courses)
CSC113 Programming Fundamentals
CSL113 Programming Fundamentals LAB
CSC213 Object Oriented Programming
CSL213 Object Oriented Programming LAB
CSC215 Data Structures & Algorithms
CSL215 Data Structures & Algorithms LAB
CSC217 Intro to Database Systems
CSL217 Intro to Database Systems LAB
CSC231 Discrete Structures
CSC313 Operating Systems
CSL313 Operating Systems LAB
CSC317 Intro to Software Engineering
CSC319 Computer Networks
CSL319 Computer Networks LAB
CSC419 Information Security
BDS491 Final Year Project I
BDS492 Final Year Project II

Computer Science (Core Courses)
CSC115 Digital Logic & Design
CSC115 Digital Logic & Design LAB
CSC211 Computer Organization and Assembly Language
CSL211 Computer Organization & Assembly Language LAB
CSC315 Design Analysis of Algorithms
CSC413 Artificial Intelligence
CSL413 Artificial Intelligence LAB
CSC418 Parallel & Distributed Computing

Data Science (Core Courses)
STA205 Probability Theory & Statistics II
BDS101 Introduction to Data Science
BDS301 Data Mining
BDS401 Data Visualization
BDS403 Big Data & Analytics
BDSxxx Data Warehousing & Business Intelligence
STA301 Model & Inference
STA302 Methods of Data Analysis

Data Science
BDS410 Stochastic Processes
BDS404 Machine Learning
CSC311 Theory of Automata & Formal Languages
BDS201 Business Process Analysis
BDS402 Big Data & Concepts
BDS411 Time Series Analysis & Forecasting
BDS418 Health Informatics
BDS413 Bioinformatics
BDS414 Game Theory
BDS415 Financial Data Analytics
BDS421 Deep Learning
BDS417 Artificial Neural Networks
BDS422 Platform & Architecture for Data Science
BDS423 Privacy Preservation
BDS424 Speech Processing
BDS425 Text Mining
ECO304 Introduction to Econometrics
BDS429 Topics in Data Science

Semester One (19 Credit Hours) Semester Two (19 Credit Hours)

Programming Fundamentals (3+0) [Pre Req. NA]
Programming Fundamentals LAB (0+1) [Pre Req. NA]
Applied Physics (3+0) [Pre Req. NA]
Information and Comm. Tech. (2+0) [Pre Req. NA]
Information and Comm. Tech. LAB (0+1) [Pre Req. NA]
Academic English (3+0) [Pre Req. NA]
Micro & Macroeconomics (3+0) [Pre Req. NA]
Islamic Studies (3+0) [Pre Req. NA]

Environmental Studies (3+0) [Pre Req. NA]
Object Oriented Programming (3+0) [Pre Req. CSC113]
Object Oriented Programming LAB (0+1) [Pre Req. CSC113]
History of Ideas (3+0) [Pre Req. NA]
Discrete Structures (3+0) [Pre Req. NA]
Persuasive & Analytical Writing for Bus. Comm. (3+0) [Pre Req. COM107]
Professional Practices (3+0) [Pre Req. NA]
Semester Three (19 Credit Hours) Semester Four (16 Credit Hours)

Pakistan Studies (3+0) [Pre Req. NA]
Calculus & Analytical Geometry (3+0) [Pre Req. NA]
Probability Theory & Statistics I (3+0) [Pre Req. NA]
Digital Logic & Design (3+0) [Pre Req. PHY111]
Digital Logic & Design LAB (0+1) [Pre Req. PHY111]
Business & Professional Speech (3+0) [Pre Req. COM107]
Foreign Language I (3+0) [Pre Req. NA]

Differential Equations (3+0) [Pre Req. MTH224]
Comp. Organization & Assembly Lang. (3+0) [Pre Req. NA]
Comp. Organization & Assembly Lang. LAB (0+1) [Pre Req. NA]
Probability Theory & Statistics II (3+0) [Pre Req. STA203]
Introduction to Data Science (2+0) [Pre Req. NA]
Introduction to Data Science LAB (0+1) [Pre Req. NA]
Foreign Language II (3+0) [Pre Req. LAN10*]
 Semester Five (17 Credit Hours) Semester Six

Model & Inferences (3+0) [Pre Req. MTH205]
Computer Networks (3+0) [Pre Req. NA]
Computer Networks LAB (0+1) [Pre Req. NA]
Linear Algebra (3+0) [Pre Req. MTH107]
Data Structures & Algorithms (3+0) [Pre Req. CSC213]
Data Structures & Algorithms LAB (0+1) [Pre Req. CSC213]
Intro. to Software Engineering (3+0) [Pre Req. NA]

Intro. to Database Systems (3+0) [Pre Req. CSC215]
Intro. to Database Systems LAB (0+1) [Pre Req. CSC215]
Methods of Data Analysis (3+0) [Pre Req. BDS301]
Data Visualization (3+0) [Pre Req. BDS301]
Operating Systems (3+0) [Pre Req. CSC215]
Operating Systems LAB (0+1) [Pre Req. CSC215]
Design & Analysis of Algorithms (3+0) [Pre Req. CSC215]
Semester Seven (19 Credit Hours) Semester Eight (18 Credit Hours)

Artificial Intelligence (3+0) [Pre Req. CSC231]
Artificial Intelligence LAB (0+1) [Pre Req. CSC231]
Big Data & Analytics (3+0) [Pre Req. NA]
Data Mining (3+0) [Pre Req. NA]
Elective I (3+0) [Pre Req. NA]
Elective II (3+0) [Pre Req. NA]
Final Year Project I (0+3) [Pre Req. NA]

Data Warehousing & Bus. Intelligence (2+0) [Pre Req. NA]
Data Warehousing & Bus. Intelligence LAB (0+1) [Pre Req. NA]
Information Security (3+0) [Pre Req. NA]
Parallel & Distributed Computing (3+0) [Pre Req. CSC313]
Elective III (3+0) [Pre Req. NA]
Elective IV (3+0) [Pre Req. NA]
Final Year Project II (0+3) [Pre Req. NA]

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