Data Analysis with Programming

Introduction

This course introduces the basics of statistics and machine learning in the context of Python. It covers:

• Inferential statistics, such as the bootstrap, p-values, and confidence intervals.
• Methods for constructing and evaluating statistical estimators.
• The fundamentals of the two most important regression models: Linear regression and logistic regression.

Additionally, the course introduces students to the Python packages NumPy, SciPy, and statsmodels

Course content

The course covers the following topics:

• NumPy and SciPy.
• Statistical simulation in Python.
• Exploratory data analysis.
• Statistical models and the bootstrap.
• Unbiased estimators and the efficiency of estimators.
• Construction of estimators.
• Confidence intervals.
• Hypothesis tests and p-values.
• The t-test.
• Foundations of machine learning.
• Linear regression.
• Inference for linear regression.
• Linear regression using categorical covariates (ANOVA).
• Binary regression, such as logistic regression.