Data Analysis (A.Y. 2023/24)

Contents

Introduction [ISL, Ch. 1]

• Introduction to data analysis

• Review of probability and statistics

Data set description [PSES, Ch. 2,7]

• Descriptive statistics

• Sample mean and variance

• Confidence intervals

• Estimation of probability distributions

Hypothesis testing [PSES, Ch. 8]

• Significance, p-value

• Tests on the mean

• Tests on the variance

Regression [ISL, Ch. 2,3]

• Simple linear regression

• Multiple linear regression

• Extensions of linear models

Classification [ISL, Ch. 4]

• Logistic regression

• Linear discriminant analysis

• Quadratic discriminant analysis

• Naive Bayes

• K-Nearest Neighbors

Throughout the course, exercises will be worked out by using small Python scripts. This is not a course about Python programming. Students will learn how to use some Python packages to carry out basic data analysis.

Prerequisites

• Basic notions of probability and statistics

• No prior knowledge of Python is required (although useful)