Introduction
A postgraduate diploma is a qualification that is typically taken after completing a bachelor's degree or equivalent. It is a higher education qualification that provides specialized knowledge and skills in a particular field of study.
Postgraduate diploma programs vary in length, but generally take between six months to one year to complete. They are usually offered by universities, colleges, and other higher education institutions, and are designed to provide students with advanced skills and knowledge in their chosen field. The Postgraduate Diploma - Data Science (PGD-DS) is designed especially for those candidates who wants to learn data science, whether they are experts in the field or amateurs. Professionals in marketing, software and IT development, and engineering can enroll in this data science diploma program.
Eligibility Criteria
- Candidates who have completed a 14-year bachelor’s degree in science or any other field with mathematics as a subject, and have obtained a minimum 2nddivision from institutions recognized by the Higher Education Commission (HEC), are eligible for admission into this program.
- Admission will be granted to all candidates satisfying the eligibility requirements.
- All eligible candidates are offered admission in PGD-DS program, subject to formation of viable group of students.
- All eligible candidates are required to deposit fee of the program as per laid down procedure.
Teaching Methodology
PGD (Data Science) program offered by DCS in a face to face/Synchronous mode of teaching through Learning Management System (LMS).
PROGRAM STATUS:
PGD (Data Science) Program is non-merit based program.
AWARD OF DEGREE
The minimum marks required for the award of PGD (Data Science) Degree is 50% of passing marks.
ASSESSMENT CRITERIA
Each of the course will be assessed on the basis of quizzes, assignments, midterm and final term. The detail is as:
|
Continuous (Pass Percentage is 50%) |
Final (Pass Percentage is 50%) |
|
|
Assignment/Quizzes |
Midterm/Presentation/Semester Project |
|
|
10% |
20% |
70% |
The 70% percent attendance is mandatory in each course.
- Semester: 1
- 2
- 3
- 4
- 5
- 6
- 7
- Semester: 2
|
Code |
Title |
Credit Hours |
Remarks |
|
New |
Fundamentals of Data Science |
3 (2 - 1) |
|
|
New |
Statistics and Probability |
3 (3 - 0) |
|
|
New |
Data Analysis and Visualization |
3 (2 - 1) |
|
|
New |
Data Management and Warehousing |
3 (2 - 1) |
|
|
New |
Data Structure and Applications |
3 (2 - 1) |
|
|
Total Credit Hours |
15 (11 – 4) |
|
|
|
Code |
Title |
Credit Hours |
Remarks |
|
New |
Data Mining |
3 (2 - 1) |
|
|
New |
Machine Learning and Artificial Intelligence |
3 (2 - 1) |
|
|
New |
Statistical Modelling and Inference |
3 (3 - 0) |
|
|
New |
Data Ethics and Privacy |
3 (3 - 0) |
|
|
New |
Big Data Analytics |
3 (2 - 1) |
|
|
Total Credit Hours |
15 (12 – 3) |
|
|
|
Grand Total |
30 (23 – 7) |
|
|