Ch 6 introduces data literacy — understanding data types, collection methods, basic statistical measures, visualisation techniques, and how to derive meaningful insights from data.
Data types: Qualitative/Categorical (colours, categories — nominal, ordinal) and Quantitative/Numerical (numbers — discrete, continuous). Sources: Primary (surveys, experiments, observations — you collect) and Secondary (government data, research papers, databases). Collection methods: surveys, interviews, observations, sensors, web scraping. Data cleaning: handling missing values (remove, fill with mean/median), removing duplicates, correcting errors, standardising formats. GIGO: Garbage In, Garbage Out — data quality determines analysis quality.
Central tendency: Mean (average: sum/count), Median (middle value when sorted), Mode (most frequent). Dispersion: Range (max−min), Variance, Standard deviation. Frequency distribution: how often each value appears. Visualisation: Bar chart (compare categories), Line chart (trends over time), Pie chart (proportion of whole), Histogram (distribution of numerical data), Scatter plot (relationship between two variables). Python: matplotlib library for charts. Choose the right chart for your data type and story.
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Mean (average) is best when data is symmetrically distributed with no extreme outliers. Median (middle value) is better when data is skewed or has outliers. Example: salaries in a company — if most earn ₹50K but the CEO earns ₹50L, the mean salary is misleadingly high. The median gives a better picture of typical salary. Rule: for income, housing prices, or any skewed data, use median. For exam scores, temperatures, or symmetrical data, mean works well. Report both when possible.
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