Master of Science in Data Science MSDS

Educational Aims of the Program

The Program Objectives (POs) of the MSDS program are shown in table 1 below:

Table 1: Program Objectives of MSDS
PO1.	Present the students with techniques to manage complex data sets.
PO2.	Presents the students with data scientist skills to model and analyze data using algorithms and data driven computing programs.
PO3.	Present the students with data scientist skills to formulate and present analytics solutions that is appropriate to the stakeholders.

 

Program Learning Outcomes

The Program Learning Outcomes (PLOs) of the MSDS are shown in table 2 follows:

Table 2: Program Learning Outcomes of MSDS After graduation MSDS students will be able to:
PLO1.	Apply computing algorithms and techniques to manage large-scale, and complex data set. (Skill)
PLO2.	Identify and evaluate the opportunities, needs, ethics, social responsibility, bias and limitations of the data and algorithms to provide professional data science solutions.
PLO3.	Formulate, integrate and design solutions and methodologies using data analytics driven programs and research methods to propose a data analytics solution or research problem.
PLO4.	Interpret and communicate effectively as managers in multidisciplinary teams through demonstrating effective professional oral & writing skills for data analytics and making persuasive presentations at a managerial level.

 

MSDS Program Learning Outcomes

Please click on the following link to access the Mapping MSDS PLOs and the Level-9 descriptors of the strands of QFEmirates

Curriculum and Study Plan - Full Time

MSDS Program Curricula
The MSDS program is composed of 132 Credit Hours, distributed as illustrated below:

#	Category	Number of Courses	Credit Hours
1	Core Courses	7	24
2	Elective Courses	2	6
Total		9	30

Core Courses (All Required – 30 CH.) 

S.N0	Course Code	Course Title	Prerequisite	Co-requisite	C.H.
1	DS 601	Fundamentals of Data Science	None		3
2	DS 602	Statistics and Probability in Data Science	None		3
3	DS 603	Big Data Management Using Hadoop	DS 601		3
4	DS 604	Advanced Database Queries and Data Warehouse in Data Science	None		3
6	DS 620	Data Visualization & Data Representation Techniques	DS 601		3
7	DS 621	Research Methods in Data Science	None		3
8	DS 631	Data Science Thesis	Completion of 18 CH, including DS 621		6
	Total				24

 

Elective Courses (Student must select 2 of the following courses 6CH)

S.No	Course Code	Course Title	Prerequisite	Co-requisite	C.H.
1	DS 623	Machine Learning in Data Science	DS 601, DS 602		3
2	DS 624	Text mining in Data Science	DS 601		3
3	DS 625	Social Network Analysis in Data Science	DS 601		3
4	DS 626	Management in Data Science	DS 601		3
5	DS 628	Ethics in Data Science	DS 601		3
	Total				6

 

Completion Requirements

The Master of Science in Data Science (MSDS) program is awarded upon the successful completion of 30 credit hours and a CGPA of 3.0 or above, within the maximum specified timeframe of six (6) terms. Only students who have completed all degree requirements prior to the graduation ceremony are eligible to be considered for degree honors. 

The categories for academic distinction are based on the following scale which is based on the graduation/cumulative GPA:

•          Summa Cum Laude: CGPA >= 3.90 through 4.0 (Excellent) – with Highest Honors

•          Magna Cum Laude:  CGPA >= 3.70 through 3.89 (Very Good) – with Great Honors

This distinction shall appear on the student’s transcript and degree.

 

Semester 1

DS 601	Fundamentals of Data Science	3
DS 602	Statistics and Probability in Data Science	3
DS 604	Advanced Database Queries and Data Warehouse in Data Science	3

Semester 2

DS 620	Data Visualization & Data Representation Techniques	3
DS 621	Research Methods in Data Science	3
DS 603	Big Data Management Using Hadoop	3

semester 3

	Elective 1
	Elective 2

semester 4

DS 631

Data Science Thesis

6

Curriculum and Study Plan - Part Time

MSDS Program Curricula
The MSDS program is composed of 132 Credit Hours, distributed as illustrated below:

#	Category	Number of Courses	Credit Hours
1	Core Courses	7	24
2	Elective Courses	2	6
Total		9	30

Core Courses (All Required – 30 CH.) 

S.N0	Course Code	Course Title	Prerequisite	Co-requisite	C.H.
1	DS 601	Fundamentals of Data Science	None		3
2	DS 602	Statistics and Probability in Data Science	None		3
3	DS 603	Big Data Management Using Hadoop	DS 601		3
4	DS 604	Advanced Database Queries and Data Warehouse in Data Science	None		3
6	DS 620	Data Visualization & Data Representation Techniques	DS 601		3
7	DS 621	Research Methods in Data Science	None		3
8	DS 631	Data Science Thesis	Completion of 18 CH, including DS 621		6
	Total				24

 

Elective Courses (Student must select 2 of the following courses 6CH)

S.No	Course Code	Course Title	Prerequisite	Co-requisite	C.H.
1	DS 623	Machine Learning in Data Science	DS 601, DS 602		3
2	DS 624	Text mining in Data Science	DS 601		3
3	DS 625	Social Network Analysis in Data Science	DS 601		3
4	DS 626	Management in Data Science	DS 601		3
5	DS 628	Ethics in Data Science	DS 601		3
	Total				6

 

Completion Requirements

The Master of Science in Data Science (MSDS) program is awarded upon the successful completion of 30 credit hours and a CGPA of 3.0 or above, within the maximum specified timeframe of six (6) terms. Only students who have completed all degree requirements prior to the graduation ceremony are eligible to be considered for degree honors. 

The categories for academic distinction are based on the following scale which is based on the graduation/cumulative GPA:

•          Summa Cum Laude: CGPA >= 3.90 through 4.0 (Excellent) – with Highest Honors

•          Magna Cum Laude:  CGPA >= 3.70 through 3.89 (Very Good) – with Great Honors

This distinction shall appear on the student’s transcript and degree.

 

Semester 1

DS 601	Fundamentals of Data Science	3
DS 602	Statistics and Probability in Data Science	3

Semester 2

DS 604	Advanced Database Queries and Data Warehouse in Data Science	3
DS 621	Research Methods in Data Science	3

Semester 3

DS 620	Data Visualization & Data Representation Techniques	3
DS 603	Big Data Management Using Hadoop	3

Semester 5-8

DS 631

Data Science Thesis

6

Semester 4

	Elective 1
	Elective 2