Abschnittsübersicht

◄Part 4: Data Acquisition and Preparation
►Part 6: Explorative Analysis 2 – Clustering and distance functions

    • Lecture content: Descriptive statistics: univariate data analysis (measures of central tendency, variability, skewness, and kurtosis), bivariate and multivariate data analysis (covariance, correlation), Data visualization (scatter and box plots, histograms).

      Tutorial content: Computing means, variances, and correlations (Python libraries: numpy and scipy); Plotting data points and visualizing data analysis results (Python libraries: matplotlib and seaborn).

      Homework: Basic (visual) exploratory description of a given dataset.

    • Notebook: Descriptive Statistics (HTML-Version) Datei
    • Notebook: Deep-dive into Seaborn (HTML-Version) Datei
    • Homework: Gather a dataset. Aufgabe
◄Part 4: Data Acquisition and Preparation
►Part 6: Explorative Analysis 2 – Clustering and distance functions