This course provides a rigorous and in-depth exploration of advanced techniques and methodologies in data analytics, equipping graduate students with the skills to extract actionable insights from complex, high-dimensional, and often unstructured datasets. This course covers machine learning concepts, algorithms, and evaluation metrics.
Students will engage with real-world datasets and industry-standard tools to perform sophisticated analyses involving regression, classification, clustering, time-series forecasting, and anomaly detection. The course also emphasizes data-driven decision-making, ethical considerations in data use, and effective communication of analytical results.