Department of
Data Sciences
Undergraduate Program
The Associate Degree Program (ADP)
Program description
The Data Science emphasises both basic statistics and computer science principles, as well as foundational training in statistical and mathematical aspects of data processing. In addition, this programme establishes a foundation in basic computer science topics, such as algorithms, data structures, data management, and machine learning. The curriculum is appropriate for those who want to pursue a job in industry or more specialised graduate study. This programme combines core statistical concepts with computational principles from computer science to prepare students for a career in data analysis. The final year two-semester project, which teaches students how to apply statistical and computational methods to solve large-scale, real-world data analysis challenges, is a major component of this degree.
Objectives
Students studying data science learn how to:
- Individual and organisational information needs should be defined.
- To make data more valuable for tackling certain problems, select and transform it.
- To develop useful information, analyse and synthesise unstructured data.
- For data exploration and display, create information visualisations.
- From acquisition to disposal, manage extremely massive data sources.
- Data should be secured and preserved in accordance with legal and organisational requirements.
ADP in Data Science Scheme of Study
Year 1
Semester I
| Code | Course | CrHr |
|---|---|---|
| XXX | Introduction to Computing | 3 |
| XXX | Calculus and Analytical Geometry | 3 |
| XXX | Introduction to Programming | 3 |
| XXX | English Comprehension | 2 |
| XXX | Pakistan Studies and Global Perspectives | 2 |
| XXX | Fundamentals of Islamic Studies (for Muslim Students) or Principles of Ethics (for Non-Muslim Students) |
2 |
| Total | 15 |
Year 1
Semester II
| Code | Course | CrHr |
|---|---|---|
| XXX | Object Oriented Programming | 3 |
| XXX | Physics | 3 |
| XXX | Calculus II | 3 |
| XXX | Discrete Mathematics & Linear Algebra | 3 |
| XXX | Introduction to Data Science | 3 |
| XXX | Data Structures & Algorithms | 2 |
| Total | 17 |
Year 2
Semester III
| Code | Course | CrHr |
|---|---|---|
| XXX | Data Mining | 3 |
| XXX | Fundamentals of Big Data Analytics | 3 |
| XXX | Data Warehousing & Business Intelligence | 3 |
| XXX | Differential Equations | 3 |
| XXX | Database Management Systems | 3 |
| XXX | Statistics and Probability | 3 |
| Total | 18 |
Year 2
Semester IV
| Code | Course | CrHr |
|---|---|---|
| XXX | Fundamentals of Software Engineering | 3 |
| XXX | Data Analysis & Visualization | 3 |
| XXX | Parallel & Distributed Computing | 3 |
| XXX | Design & Analysis of Algorithms | 3 |
| XXX | Numerical Analysis | 3 |
| XXX | Business and Technical English Writing | 3 |
| XXX | Internship (Mandatory – 03 Months) | Pass/Fail |
| Total | 18 |
TOTAL CREDIT HOURS: 68
Structure
- (Program is equivalent to 14 years of education)
- Minimum Duration: 2 years; through 4regular semesters (Fall/Spring)
- Minimum Credits: [68]
- Semesters [4]
- Course Load per Semester [15-18 Cr hr]
- Number of courses per semester [6]
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