COURSE CURRICULUM

In Year 1, the curriculum establishes strong theoretical and technical foundations. Machine Learning Fundamentals introduces supervised and unsupervised learning methods and their practical applications. Statistical Foundations of Data Science develops probabilistic reasoning, statistical modeling, and hypothesis testing skills essential for reliable data analysis. Optimization in Data Science focuses on mathematical techniques used to improve model performance and computational efficiency. Data Visualization Methods trains students to present complex analytical results clearly and effectively. Data-oriented Programming emphasizes programming paradigms and tools designed for handling large-scale datasets. Cloud Computing introduces distributed infrastructures and scalable data processing solutions. Information Security addresses data protection, privacy, and cybersecurity principles, while Data Ethics explores responsible data usage, fairness, transparency, and regulatory considerations.In Year 2, the program advances toward specialized and cutting-edge topics. Modern Deep Learning Frameworks and Advanced Machine Learning deepen expertise in neural networks and complex predictive models. Advanced Reinforcement Learning examines adaptive decision-making systems. Big Data Technologies focuses on large-scale data storage and processing architectures. Advanced Robotics and Self-driving Cars explore intelligent autonomous systems. AI Security addresses the protection and robustness of artificial intelligence systems. Financial Models and Genetics and Big Data demonstrate interdisciplinary applications of data science in finance and life sciences.