Data Analysis with Python

Understanding the concept and significance of classification as a supervised learning method, students will identify and describe different classifiers and evaluate their performance. They will learn to apply each classifier to perform binary and multiclass classification tasks on diverse datasets, select and fine-tune classifiers based on dataset characteristics and learning requirements.

Students will comprehend regression analysis principles and significance in supervised learning, implementing cross-validation methods to assess model performance and optimize hyperparameters. They will also learn about ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.

Throughout this module, students will understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction. They will apply clustering techniques to diverse datasets for pattern discovery and data exploration and implement Principal Component Analysis (PCA) for dimension reduction.

Grasping the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items, students will apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points. They will also understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection.

Students will define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives. They will apply various classification and regression algorithms, implement cross-validation and ensemble techniques to enhance model performance, and apply various clustering, dimension reduction, association rule mining, and outlier detection algorithms for unsupervised learning models.