Statistics becomes easier to understand when every concept is tested on data you can see and change. This DataCamp course uses Google Sheets to move from summary measures to charts, sampling and hypothesis tests. Instead of separating theory from software, the exercises ask you to calculate, visualize and interpret results inside a spreadsheet.
The course is aimed at beginners, although basic familiarity with cells, ranges and formulas will make the practice smoother. Its four chapters form a clear progression from describing a dataset to answering a question with evidence.
Four stages of analysis
| Chapter | Main ideas | What you do in Sheets |
|---|---|---|
| Getting to know your data | Mean, median, mode, variation and z-scores | Summarize and compare values |
| Statistical visualization | Distributions, correlation and categories | Build histograms, scatter plots and bar charts |
| Hypothesis testing | Sampling, t-tests, z-tests and chi-squared tests | Test differences and relationships |
| Dating profile case study | Distributions and multiple variables | Investigate one dataset from start to finish |
The progression matters. A statistical test has little value if you have not first checked the distribution, unusual values and scale of the variables involved.
Mean, median and mode are different answers
The first chapter uses examples to show why an average is a way to reduce information, not a complete description of a population. In Sheets, you apply functions for mean, median and mode, then compare the results.
Try a small dataset such as 12 monthly expenses. Calculate all three measures and then replace one value with an unusually high expense. The mean will react more strongly than the median. This makes the idea of robustness visible instead of leaving it as a definition.
The chapter also introduces standard deviation and z-scores, which help describe spread and compare observations measured against their own distributions.
Charts as part of the reasoning
Histograms reveal the shape of a distribution, scatter plots show how two numerical variables move together and bar charts compare categories. The course includes exercises on customer longevity, donations, sales and auction data.
Before creating a chart, write the question it should answer. Then check:
- whether the selected chart matches the variable type;
- whether axes and labels communicate the scale correctly;
- whether a trend line clarifies or oversimplifies the relationship;
- whether an unusual point changes the visual conclusion.
This approach prevents charts from becoming decoration. Each visualization should support an interpretation that can be stated in one or two sentences.
From a sample to a cautious conclusion
The third chapter introduces sampling and the central limit theorem before hypothesis tests. It includes comparisons with t-tests and z-tests as well as chi-squared tests for categorical relationships.
Keep a short analysis note beside every calculation: the question, variables, test, result and one limitation. This habit is more valuable than memorizing formulas without context.
The final case study
The dating profile dataset brings the previous skills together. You inspect age distributions, login behavior, possible correlations and independence between categories. Because the same data supports several questions, the case study shows how analysis is an iterative process.
For the best practice, duplicate the workbook at the end and answer one new question without following the exercise prompts. Organize the raw data, calculation and conclusion on separate sheets. The result becomes a compact portfolio example that demonstrates not only spreadsheet functions, but also the reasoning behind them.