This course aims to equip students with a robust understanding of the data mining pipeline, from raw data to actionable insights. By the end of the course, students should be able to:
Master data preparation techniques: Learn to identify and address common data issues such as missing values, noisy data, and outliers.
Apply advanced data analysis methods: Gain proficiency in exploratory data analysis (EDA) and use modern visualization tools to uncover patterns and relationships in data.
Utilize feature engineering and selection: Understand and apply various data transformation, encoding, and feature selection methods to prepare data for machine learning models.
Implement regularization and dimension reduction: Apply techniques like Ridge, LASSO, and Elastic Net to handle multicollinearity and prevent overfitting. Additionally, learn to use methods like PCA and t-SNE to reduce data dimensionality for better visualization and model performance.
Build and evaluate predictive models: Develop a strong foundation in a variety of modeling techniques, including regression, and understand the trade-offs involved in model building, such as the bias-variance trade-off.
Explore advanced data mining applications: Gain exposure to specialized topics like association rules and recommendation systems, understanding their underlying principles and practical applications.