Applied Data Analysis

Introduction to statistical learning and R, overview of regression and classification problems Linear regression (Illustrations: effects of budgets allocated for TV, newspaper, radio advertisement on annual sales, prediction of credit card balance from income, limit, rating, age, number of cards, and education level) Linear regression continued and k-nearest neighbour regression (Illustration: conjoint analysis from marketing science; how can you design a new product with a higher market penetration?) Logistic regression (Illustration: loan default probability estimation from credit card balance, income, occupation) Multinomial and Poisson regressions (Illustration: would it have been possible to predict the Challenger diasaster? https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster) Linear discriminant analysis (Illustrations: revisit credit card default probability estimation and Challenger disaster) Cross-validation, linear model selection, subset selection (Illustrations: what are the variables among income, limit, rating, age, number of cards, and education level that explain the credit card balance or default probability best? Is logistic regression or linear discriminant model best for predicting the loan default probability?) Shrinkage methods, ridge regression and lasso (What if the number of predictors is large--comparable to number of examples? Illustration: prediction of salaries of baseball players from various measures of their performances in the past games) Polynomial regression, regression splines, smoothing splines (Illustration: modeling the wage as a function of age, the amount pollutants in a residential area as a function of its distance from employment centers) Local regression, generalized additive models for quantitative and categorical variables (Illustrations: revisit wage and pollutant examples) Regression trees (Illustrations: predict the baseball player salaries, car-seat sales) Classification trees (Illustrations: email spam filtering--when is an email message spam? Predict crime rate in a residential area) Bagging, random forests, boosting (Illustrations: revisit baseball player salary email spam, crime-rate examples) Principal component analysis, k-means and hierarchical clustering (Illustrations: handwritten digit recognition, clustering cancer cell according to micro-array data, market-basket data) ECTS - Workload Table: Activities Number Hours Workload Quiz 3 2 6 Preparation for Final exam 1 16 16 Preparation for Quiz 3 5 15 Individual or group work 14 3 42 Midterm exam 1 2 2 Course hours 14 3 42 Preparation for Midterm exam 1 6 6 Final exam 1 2 2 Homework 5 5 25 Total Workload: 156 Total Workload / 30: 156 / 30 5.2 ECTS Credits of the Course: 5 Type of Course: Lecture Teaching Methods: Lecture - Exercises - Assignment