The Egyptian Ministry of Communications and Information Technology (MCIT) through Digital Egypt Builders Initiative (DEBI) and the Information Technology Industry Development Agency (ITIDA) have launched an initiative to:
- enhance the skills of professionals and newly-graduated students;
- support digital transformation projects;
- improve the competitiveness of Egyptian youth in the international labor market; and
- build nationwide capacity in modern technologies including Artificial Intelligence and Data Science.
The Master of Data Science and Machine Learning is a 12-month program offered by Queen's School of Computing addressing the growing demand for graduates with a Data Science and Machine Learning background from leading technology firms, healthcare companies, automobile manufacturers, research labs, and government agencies. Data Science and Machine Learning play a critical role in understanding customers, making effective decisions, recommending relevant information, detecting cyber-intrusions or financial fraud, and much more. The creation of this professional program will help to distinguish Computing graduates and increase their competitiveness for these highly skilled positions.
Key program learning outcomes include:
- Develop a rigorous understanding of fundamental concepts in Data Science and Machine Learning.
- Model real-world problems or decomposed sub-problems from a data-driven perspective.
- Design and execute rigorous processes for modeling complex systems and collecting data that is appropriate, effective, and revealing.
- Design, evaluate, and refine data-driven solutions, processes, and infrastructure for effective problem solving.
- Develop a thorough understanding of the ethical, security, and privacy implication of data-driven solutions.
The Program is offered remotely to up to 100 Egyptian students. There will be three cohorts of students.
- The first cohort started their studies in January 2022,
- the second cohort will begin in January 2023, and
- the third cohort will begin in January 2024 and complete in December 2024.
The program requires that a student completes 26 credits by:
- taking the 6 courses covering different subfields of Data Science and Machine Learning; and
- completing a research-based capstone project that is related to Data Science, Machine Learning, or any of their subfields.
- CSAI-801*/3.0: Fundamentals of Artificial Intelligence and Machine Learning
- CISC 839*/3.0: Topics in Data Analytics
- CISC 856*/3.0: Reinforcement Learning
- CISC 867*/3.0: Deep Learning
- CISC 873*/3.0: Data Mining
- CISC 886*/3.0: Cloud Computing and Big Data
- CISC 898*/8.0: Master's Project
- Dr. Hazem Abbas: machine learning, computational intelligence, image processing
- Dr. Dorothea Blostein: biomechanics; adaptive tensegrity; pattern recognition
- Dr. Yuanzhu Chen: mobile computing, cyber-physical systems, edge learning
- Dr. Steven Ding: data mining; machine learning; security
- Dr. Qingling Duan: machine learning; biomedical computing; bioinformatics
- Dr. Randy Ellis (IEEE fellow): medical data analysis, computer-assisted surgery
- Dr. Gabor Fichtinger (Canada Research Chair, IEEE Fellow): computer-assisted surgery and interventions
- Dr. Sidney Givigi: machine learning applied to autonomous vehicles; reinforcement learning; deep learning
- Dr. Ahmed Hassan (IEEE fellow, Member of New College of Royal Society, Steacie Fellow): software engineering and analytics, intelligent systems, data analytics
- Dr. Hossam Hassanein (IEEE fellow): wireless and mobile networks, architecture, and protocols; edge services, intelligent systems
- Dr. Anwar Hossain: cloud computing, big data
- Dr. Ting Hu: evolutionary computing; machine learning; complex networks; bioinformatics
- Dr. Parvin Mousavi (Member of New College of Royal Society): machine learning in computer assisted diagnosis and interventions; image-guided interventions; ultrasound imaging; medical image computing; computational biology; bioinformatics; systems biology
- Dr. Christian Muise: automated planning; model understanding, learning, and acquisition; goal-oriented dialogue systems
- Dr. Amber Simpson (Canada Research Chair): machine learning; medical image analysis; computer-aided surgery
- Dr. David Skillicorn: adversarial knowledge discovery, cybersecurity, data analytics
- Dr. Yuan Tian: deep learning; software engineering; machine learning, recommender systems
- Dr. Farhana Zulkernine: big and streaming data management and analytics; deep learning and decision support systems (DSS); cognitive computing; cloud and services computing