Statistics for Data Analysis

Curriculum

1. 01Statistics for AnalystsWhat statistics does for an analyst, the difference between describing data and drawing conclusions from it, populations and samples, the kinds of variable you'll meet, and the three ways numbers most often mislead.25 min
2. 02Averages: Mean, Median and ModeCalculate the three averages, see how a few extreme values pull the mean, choose the right average for the question, and report it honestly.25 min
3. 03Spread: Range, IQR and Standard DeviationMeasure how spread out data is with the range, the interquartile range and the standard deviation, know which standard deviation to use, and compare variability fairly with the coefficient of variation.25 min
4. 04Distributions and OutliersSee the shape of your data with a histogram, recognise skewed and symmetric distributions, measure how unusual a value is with a z-score, flag outliers with the IQR rule, and decide what to do about them.25 min
5. 05Rates, Percentages and Weighted AveragesReport changes in rates without confusing percent and percentage points, calculate weighted averages correctly with SUMPRODUCT, and spot Simpson's paradox, where a trend in every group reverses in the total.25 min
6. 06CorrelationMeasure how strongly two numbers move together with a scatter chart and CORREL, interpret r and r², and avoid the classic traps, above all treating correlation as cause.25 min
7. 07Sampling and the Normal DistributionUnderstand why samples give different answers, measure that uncertainty with the standard error, use the 68–95–99.7 rule, and see why averages of samples behave predictably even when the data is skewed.25 min
8. 08Confidence IntervalsTurn a sample's answer into an honest range with a 95% confidence interval for a mean (CONFIDENCE.T) or a percentage, interpret it correctly, and use it to say when a difference might just be noise.25 min
9. 09Comparing GroupsTest whether a difference between two groups is more than chance, with T.TEST for averages and a two-proportion test for rates, read p-values correctly, and keep statistical significance separate from practical importance.25 min
10. 10Regression and TrendsFit a straight line to data with SLOPE, INTERCEPT and RSQ, use it to estimate and forecast with FORECAST.LINEAR, read how much the line explains, and know when a trend line will mislead you.25 min
11. 11Final ProjectPlan and start your final project, a statistical review of Harbourline Freight's delivery performance that uses every tool in the course, and check your set-up with three warm-ups.20 min
12. Final assessmentPass it (60% or more) to earn the Statistics for Data Analysis course completion badge.