MSc in Data Science

With the aim to extract knowledge and insights from data, MSc in Data Science offers students with hands-on skills for data wrangling, analysis, and visualization. Through advanced machine learning and deep learning techniques and Big Data processing, students will advance through this program to achieve their potential in the field of data science.

 

Mission

Prepare students for employment in various data science areas and /or for the pursuit of innovative research and advanced degrees in data science by educating them the concepts, knowledge, and skills of data management, data analytics, and data engineering, to empower the community and support its needs.

 

Program Objectives

1. To graduate students who can implement effectively, and intelligently the use of theoretical data science knowledge and its analytical tools.
2. To equip students with the knowledge and technical-based skills to acquire new knowledge independently in the data science discipline.
3. To cultivate creative scientific research in the field of data science via challenging data-driven projects.
4. To deliver contemporary curricula with required scientific and technical knowledge to meet the growing demand of the national economy in the field of data science.

 

Program Learning Outcomes

1. Identify appropriate statistical and analytical techniques to discover and deliver new insights to business problems.
2. Relate advanced domain knowledge to deliver a business or scientific focused analysis of opportunities and suggest techniques for organization process optimization.
3. Understand skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning.
4. Apply data science as a multifaceted discipline of critical thinking and problem-solving.
5. Develop data analytical applications and computational solutions to data analytic platform.
6. Survey and assess various data analytical tools to data driven decision making.
7. Apply skills needed to transform vast amounts of data into meaningful information for decision making.
8. Communicate effectively with academic and professional societies using scientific contribution.
9. Interpret and apply a professional code of ethics relevant to data science research and the profession
10. Effectively collaborate and undertake leadership roles in research, and projects, and Practice continuous learning. 

Specific PLO: Data Analytics and Engineering

1. Survey and assess various data analytical tools for data-driven decision-making.
2. Apply skills needed to transform vast amounts of data into meaningful information for decision-making.

Specific PLO: Business  Analytics

1. Identify appropriate statistical and analytical techniques to discover and deliver new insights to business problems.
2. Understand skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning.

 

Program Handbook

                For more information about the program, tuition fees, rules and regulations of study download the program handbook, Project Deliverables, and Milestones can be found here 

 

Admission Requirements

1. The applicant must have a university degree from a Saudi university or from another recognized institution with a suitable background with a minimum GPA of 2.75 out of 5.00 or 1.75 out of 4.00 (good), in the following disciplines: (Computer Science - Information Systems Information Technology - Software Engineering). Other related disciplines may be accepted after the approval of the department.
2. Two recommendation letters.
3. The minimum score for general aptitude test is 60%.
4. The minimum score for the STEP English test is 60 (should be provided within the application submission period, no provisional admission is granted) or its equivalent in other language tests as shown in the table below.

English Aptitude Test Equivalence

 

 

 Main Tracks

Data Analytics and Engineering

Business Analytics

 

Duration

Two years (Four Semesters)

 

Study Plan (32 Credit Hours)