Data Analysis and Visualization

The lecture is essentially divided into two parts. In the first part, the understanding of statistical methods and techniques are further enhanced. This includes formulating hypotheses, testing statistical significance, applying various regression models, and identifying and understanding outliers and anomalies in data. In the second part, students learn advanced techniques for data and information visualization. They use a range of tools and techniques to visually represent complex datasets and effectively communicate their findings.