Course Curriculum

The Data Science and Machine Learning diploma program is a two-year course designed to build strong foundations in data handling, statistical analysis, and machine learning techniques, with a practical orientation towards real-world applications. In Year 1, Term 1, students begin with essential subjects including Communication Strategies, Introduction to Programming Logic, Introduction to Data Science and Machine Learning, Statistics, and Linear Algebra, totaling 27 credit hours. These courses provide the core mathematical and technical competencies needed for advanced learning in data science. In Term 2, students expand their skills in professional communication and data technologies through Communication for the Workplace, Transforming Data Into Databases, Python Essentials with Data Analysis, Data Management, and Supervised Machine Learning, amounting to another 27 credit hours. This term emphasizes hands-on programming and practical application of supervised learning techniques. In Year 2, Term 3, students continue with Communication for Systems and Innovative Thinking, and deepen their knowledge with Introduction to Artificial Intelligence and Unsupervised Machine Learning. They also choose electives such as Introduction to Bioinformatics, Information and Data Architecture, Neural Networks and Deep Learning, or Robotics and Automation, depending on their career interests. These electives provide specialized knowledge in high-demand areas. In the final term, Term 4, students choose between a 6-credit Co-op placement or a Data Science and Machine Learning Industry Project, both of which offer valuable real-world experience in collaboration with industry partners. This structure ensures students graduate with a solid theoretical background, advanced technical skills, and applied experience. The program not only builds technical expertise but also fosters critical thinking, innovation, and effective communication skills, preparing students for roles in a variety of sectors including healthcare, finance, technology, and government, where data-driven decision-making and machine learning are becoming increasingly vital.