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BS (Data Science)

CS > Programs > Program Details
  • Program Overview
  • Tentative Study Plan

Program Overview

Learning Outcomes
By the time of graduation, the students develop an ability to:

  • Apply knowledge of computing and mathematics that is appropriate for the program.
  • Analyse a problem and define computing requirements that are appropriate to its solution.
  • Design, implement, and evaluate a computer-based system, process, component or program to meet desired needs.
  • Work in a team to accomplish a common goal.
  • Understand professional, ethical, and social issues and responsibilities.
  • Communicate effectively with different audiences.
  • Learn programming for large-sized datasets
  • Identify useful and hidden patterns from data.
  • Improve decision making skills by mining data from various aspects.
  • Solve real world problems by applying mathematical and computational approaches.
  • Change the world for the better – in areas like healthcare, transportation, and education etc.

Award of Degree

For the award of BS (Data Science) degree, a student must have:

  • Passed courses with a total of at least 132 credit hours, including all those courses that have been specified as core courses
  • Obtained a CGPA of at least 2.00

Eligibility:

  • At least 60% marks in SSC (Matric) or an equivalent examination AND
  • At least 50% marks in the HSSC or an equivalent examination.
  • Must have studied Mathematics at the HSSC level.

Selection Criteria:

Admission on the basis of NTS-NAT Marks
  • Selection is based on marks obtained in NTS NAT IE, or NAT-ICS.
  • Cut-off marks to be determined by the University.
Admission on the basis of NU Admission Test
  • Merit List is prepared by assigning 50% weight to marks obtained in Intermediate (part-I) (or an equivalent exam) AND
  • 50% weight is assigned to score obtained in NU Admission Test.
  • In case, Intermediate result is not available, Matriculation marks are used and multiplied by a factor of 0.9 (to equate it to average Intermediate marks).
Admission on the basis of SAT score
  • Combined score of 1,500 or more in the SAT-I examination AND
  • At least 550 in the SAT-II (Math Level IIC) examination.

Tentative Study Plan


Semester-1
Code Course Name Credit Hours Course Type Pre-requisite
CL 117 Intro to Info. & Comm. Technologies 1 Core None
NS 101 Applied Physics 3 Core None
MT 119 Calculus & Analytical Geometry 3 Core None
SS 113 Pakistan Studies 3 Core None
SS 150 English Composition & Comprehension 2+1 Core None
CS 214 Programming Fundamentals 3+1 Core None
Semester-2
Code Course Name Credit Hours Course Type Pre-requisite
MT 224 Differential Equations 3 Core MT 119
SS 111 Islamic & Religious Studies 3 Core None
SS 152 Communcation & Presentation Skills 2+1 Core None
CS 217 Object Oriented Programming 3+1 Core CS 214
DS 201 Fundamentals of Data Science 3 Core None
Semester-3
Code Course Name Credit Hours Course Type Pre-requisite
CS 211 Discrete Structures 3 Core None
CS 218 Data Structures 3+1 Core CS 217
MT 104 Linear Algebra 3 Core None
MT 206 Probability & Statistics 3 Core None
DS 202 Exploratory Data Analysis and Visualization 3+1 Core None
Semester-4
Code Course Name Credit Hours Course Type Pre-requisite
CS 219 Database Systems 3+1 Core Cs 218
CS 220 Operating Systems 3+1 Core CS 218
CS 302 Design & Analysis of Algorithms 3 Core CS 218
CS 307 Computer Networks 3+1 Core CS 218
SS/MS Social Science Elective 3 Elective None
Semester-5
Code Course Name Credit Hours Course Type Pre-requisite
CS 303 Software Engineering 3 Core None
DS DS Elective I 3 Elective None
MT 402 Stochastic Processes 3 Core MT 206
DS 302 Big Data Mining 3+1 Core Cs 219
DS 301 Big Data Programming 3+1 Core CS 217, CS 218, CS 219
Semester-6
Code Course Name Credit Hours Course Type Pre-requisite
CS 326 Parallel and Distributed Computing 3 Core CS 220
CS 462 Information Security 3 Core None
DS 106 Image Processing and Analysis 3+1 Core None
DS DS Elective II 3 Elective None
SS 142 Technical Report Writing 3 Core None
Semester-7
Code Course Name Credit Hours Course Type Pre-requisite
DS DS Elective III 3 Elective None
SS/MS Social Science Elective II 3 Elective None
DS 491 Final Year Project I 3 Core None
DS 401 Deep Learning and Applications 3+1 Core DS 302
DS 402 Natural Language Processing 3+1 Core None
Semester-8
Code Course Name Credit Hours Course Type Pre-requisite
CS 463 Professional Practices 3 Core Sr. Year
DS DS Elective IV 3 Elective None
DS DS Elective V 3 Elective None
SS/MS Social Science Elective III 3 Elective None
DS 492 Final Year Project II 3 Core Sr. Year