Classify variables correctly—categorical, numerical, discrete, continuous—and recognise structural patterns like cross-sectional and time-series data.
Categorical variables record group membership.
Categories without any inherent order.
Categories with a natural order but no fixed spacing.
Counts that take isolated values, often integers.
Example: number of calls handled by a support agent.
Measurements that can take any value over an interval.
Example: temperature recorded every hour.
Visualise discrete data with bar charts or dot plots; continuous data often require histograms or density plots.
Multiple units observed at a single point in time. Ideal for comparing groups.
Example: income of all IITM students in the 2025 graduating batch.
Single unit observed across time. Ideal for trend and seasonality analysis.
Example: daily rainfall in Chennai during July 2020.
Some datasets combine both dimensions, leading to panel data (multiple units tracked over time). Panel data techniques appear later in the programme.
| Data type | Typical summaries | Visualisations | Inferential techniques |
|---|---|---|---|
| Nominal categorical | Counts, proportions, mode | Bar chart, Pareto chart | Chi-square tests for independence |
| Ordinal categorical | Medians, percentiles | Ordered bar chart | Non-parametric tests (Mann-Whitney, Kruskal-Wallis) |
| Discrete numerical | Mean, variance | Stem-and-leaf, bar chart | Poisson/Binomial models |
| Continuous numerical | Mean, standard deviation | Histogram, box plot | t-tests, regression, ANOVA |