This chapter explores fundamental analytical techniques in data science, distinguishing between data analysis (backward-looking) and data analytics (forward-looking prediction).
Six key analysis categories are covered:
Descriptive Analysis examines current data through statistical measures (mean, median, mode) and visualizations to understand "what is happening."
Diagnostic Analytics investigates "why something happened" using correlation analysis, emphasizing the distinction between correlation and causation.
Predictive Analytics forecasts future outcomes using historical data and regression analysis.
Prescriptive Analytics determines optimal courses of action by analyzing potential decisions.
Exploratory Analysis discovers unknown relationships through visualization when questions aren’t predetermined.
Mechanistic Analysis examines exact variable changes and their effects.
The chapter emphasizes statistical literacy as essential for data scientists, covering key concepts like variable types, frequency distributions, measures of centrality and dispersion, and regression modeling. Hands-on examples demonstrate applications across business, healthcare, and social sciences.