Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.
Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.
Explores levels of data abstraction, model building, usage, and representation, and the utility of information systems for decision-making.
