Executive Master’s in Data Science and Management

A- Knowledge and Comprehension:
❖ Acquiring comprehensive knowledge of data science and management principles, encompassing current trends, research, and application-based understanding.
❖ Understand advanced data analysis techniques and data technologies to support organizational decision-making.
❖ Comprehend the critical aspects of data quality and management within organizations, including associated tools and techniques.

B- Mental Skills:
❖ Gain advanced skills in analyzing data and discovering patterns and trends.
❖ Develop and apply advanced skills in critical analysis, evaluation and synthesis in consideration of the range of strategy, governance, theories, concepts and techniques in use within the domain of data science and management.
❖ Identify major aspects associated with the quality and management of organizational data and recommend tools/techniques for dealing with them.
❖ Integrate knowledge of analyzing, evaluating, and dealing with complex problems, issues, and situations related to the data in various work environments.
❖ Identify and formulate questions to plan and execute advanced tasks within given time frames as well as to evaluate this work.

C- Professional and Practical Skills:
❖ Demonstrate critical awareness of current legal, social, ethical and professional issues related to the data.
❖ Develop and utilize advanced problem-solving skills to resolve issues related to data management and analysis.
❖ Gain the ability and skill to apply data science, management, and analysis techniques to support the decision-making process in the work environment.

D- General Skills:
❖ Acquire advanced and specialized skills in the field of data science and management.
❖ Acquire advanced data management and data analysis skills capable of achieving goals of knowledge-based economy.
❖ Acquire effective communication skills in a data-driven work environment.
❖ Work autonomously and within teams, as appropriate, demonstrating a capability for both taking and critically reflecting on roles and responsibilities.

In addition to the admission requirements stated in the unified regulations for graduate studies in Saudi universities, and the organizational and executive rules and procedures for postgraduate studies at King Saud University, the department requires the following for enrollment in the program:

1. Applicants must hold a Bachelor’s degree in Information Systems, Computer Science, Information Technology, Software Engineering, Computer Engineering, Computer Education, or from the College of Business Administration, College of Engineering, or College of Science at King Saud University or a university recognized by the Ministry of Education, with a minimum grade of Good and a cumulative GPA of at least 2.75/5.00 or equivalent.

2. Applicants must submit an English language test result or provide proof that the medium of instruction for their Bachelor’s or diploma was English.

3. Applicants must have a minimum of 2 years of prior work experience in an IT-related field.

4. Students holding a Bachelor’s degree from non-Saudi universities must provide an equivalency certificate from the Ministry of Education in Saudi Arabia.

First Year – First Semester

EDSM510 – Advanced Data Management – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM511 – Databases, Data Integration, and Data Warehousing – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM512 – Data Science Concepts and Techniques – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None

Total: 9 Study Units

First Year – Second Semester

EDSM520 – Advanced Data Governance – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM521 – Machine Learning for Data Science and Management – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM522 – Distributed Data Intensive Systems – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None

Total: 9 Study Units

Second Year – First Semester

EDSM530 – Advanced Data Quality – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM531 – Temporal and Sequential Data Mining – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM532 – Data Visualization – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None

Total: 9 Study Units

Second Year – Second Semester

EDSM540 – Data Management Strategy – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM541 – Enterprise Content Management – 3 (3+0) Credit Hours – Lecture – GPA: Included – Prerequisite: None
EDSM599 – Capstone Project – 3 (0+6) Credit Hours – Project – GPA: Included – Prerequisite: None

Total: 9 Study Units

Grand Total: 36 Study Units