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Statistics for Data Science (229711) - Chapter 9: Data Classification
This chapter shifts the focus to the most popular domain of Supervised Learning: Data Classification. Students will learn how to build models that can "decide" and "predict" categorical labels for new data. From determining whether an email is spam to diagnosing a medical condition, this chapter provides a robust toolkit for making evidence-based predictions by learning from historical patterns.
Core Topics covered:
Introduction to Classification
Logistic Regression
K-Nearest Neighbors
Decision Trees
Random Forest and Ensemble Methods
Model Evaluation
Model Comparison and Selection
Chapter Lab Activity: Medical Diagnosis Classification with Pima Data
Cross-Country Comparison of Foundational Learning Outcomes in Africa
This project analyzes foundational learning outcomes in mathematics and reading among 10-year-old children across selected African countries (Kenya, Mali, Mozambique, Senegal, Tanzania, and Uganda). Using survey-based data and weighted estimates, it compares the proportion of children meeting minimum proficiency levels in numeracy and literacy. The analysis highlights cross-country disparities and overall regional performance, providing evidence on learning gaps to inform education policy and improve foundational skills outcomes in Africa.