Data analytics: from zero to job-ready (statistics, Excel, SQL, Python, Power BI): free course
Learn data analytics step by step with Kenyan examples. The analyst's process, types of data, statistics you'll actually use (averages, spread, outliers, sampling, correlation, regression, A/B tests), data cleaning, Excel PivotTables, SQL for analysis, Python, charts and dashboards, Power BI, storytelling with data, a portfolio project and how to get hired or freelance.
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Lessons
- 1 Start here: what data analysts do and your roadmap from zero to job-ready
- 2 Types of data, where data comes from and what makes data good
- 3 Averages and spread: mean, median, mode, range and standard deviation
- 4 Distributions, percentiles and outliers: seeing the shape of your data
- 5 Sampling and probability: surveys, bias, margin of error and why size matters
- 6 Correlation and regression: finding relationships (and why correlation isn't causation)
- 7 A/B tests and hypothesis testing: proving what works (with p-values explained simply)
- 8 Data cleaning: duplicates, missing values, messy formats and a cleaning checklist
- 9 Excel and Google Sheets for analysis: PivotTables, lookups, conditional formatting and charts
- 10 SQL for analysis: GROUP BY, HAVING, JOINs, CASE and window functions
- 11 Python for data analysis: CSV files, grouping, and an introduction to pandas
- 12 Data visualisation: choosing the right chart and designing clear dashboards
- 13 Power BI: from raw data to an interactive dashboard (Power Query, data model, DAX)
- 14 Storytelling with data: turning analysis into reports and presentations people act on
- 15 Your portfolio project and getting hired or freelancing as a data analyst