Analyzing Trends and Insights

Students move from describing data to interpreting and modeling it. They explore correlation vs. causation, trend lines, predictive modeling, and the ethics of data bias using real-world datasets and case studies.

Correlation vs Causation

Students distinguish between variables that are coincidental and those that have a causal relationship. They analyze spurious correlations to build critical thinking skills in data analysis.

Trend Lines and Models

Students learn to fit simple trend lines to scatter plots to model data direction. They practice interpolation to estimate missing values within a data range.

Predicting the Future

Students use trend lines to extrapolate future values and discuss the reliability of long-term predictions. They learn about the utility and limits of predictive modeling.

Bias in the Machine

Students identify how missing or biased data collection leads to unfair models. They analyze ethics in data science through real-world case studies of algorithmic bias.

Data Consultant Capstone

Students act as consultants to solve a real-world problem using a complex dataset. They synthesize cleaning, visualization, and modeling to provide actionable recommendations.