Fundamentals of Data Science (DSM301)

DSM301 Fundamentals of Data Science provides students with a comprehensive exploration of end-to-end pipelines, spanning from data ingestion to model output. This course is designed to equip students with essential knowledge and skills, guiding them through a transformative journey into the field of data science. From mastering the basics of databases to machine learning interpretability, students will engage with a diverse range of topics essential for a successful career in data science.

• Introduction to SQL databases
• SQL basics
• Advanced SQL queries
• Data cleaning and pre-processing
• Feature engineering and extraction
• Introduction to machine learning
• Introduction to machine learning
• Supervised machine learning algorithms
• Cross-validation techniques
• Model evaluation metrics
• Why model interpretability matters
• SHapley Additive exPlanations (SHAP)
• Local Interpretable Model-agnostic Explanations (LIME)

• Show the basic structure and syntax of SQL queries.
• Discuss varies techniques for feature engineering.
• Demonstrate the principles and techniques of machine learning cross-validation techniques.
• Use complex SQL queries to retrieve and pre-process data for effective data manipulation.
• Implement appropriate metrics for model evaluation.
• Interpret machine learning models using SHAP or LIME.