Data Analytics · Beginner
Data Analytics with Excel, SQL & Power BI
Free Data Analytics Course for beginners: clean and analyze data in Excel and Google Sheets, write SQL queries, model data and build Power BI dashboards for business reporting and visualization. Includes 56 guided lessons, five 12-question checkpoint quizzes, a 40-question final exam and a verifiable completion certificate.
56 steps, written for someone starting from zero: the lessons plus a checkpoint quiz task after every ten. Each lesson opens with a starting point you have to change, not the finished answer. Work through it in order and you finish with a program you wrote yourself — the kind of thing you can show in an interview, not a certificate you bought.
Data literacy and spreadsheet foundations
Define useful questions and build reliable, structured spreadsheet workflows.
- 1 Questions, measures and data types Data Analytics —
- 2 Rows, columns and tidy tables Data Analytics —
- 3 Excel and Sheets workbook structure Data Analytics —
- 4 Cell references and formula basics Data Analytics —
- 5 Number formats and underlying values Data Analytics —
- 6 Sorting and filtering safely Data Analytics —
- 7 Tables, headers and structured ranges Data Analytics —
- 8 Data validation and controlled entry Data Analytics —
- 9 Charts that answer a question Data Analytics —
- 10 Spreadsheet capstone: a small sales register Data Analytics —
Spreadsheet cleaning and analysis
Clean data, use formulas and produce repeatable spreadsheet summaries.
- 11 Duplicates, blanks and missing values Data Analytics —
- 12 Whitespace, case and consistent labels Data Analytics —
- 13 Data types and dates Data Analytics —
- 14 IF and conditional logic Data Analytics —
- 15 SUMIF and COUNTIF Data Analytics —
- 16 Lookup and matching keys Data Analytics —
- 17 Text functions for cleanup Data Analytics —
- 18 Pivot tables and summaries Data Analytics —
- 19 Conditional formatting as a signal Data Analytics —
- 20 Spreadsheet cleaning project Data Analytics —
SQL foundations and relational data
Query relational data with filters, aggregates, joins and safe practices.
- 21 Relational tables and keys Data Analytics —
- 22 SQL SELECT and FROM Data Analytics —
- 23 Filtering with WHERE Data Analytics —
- 24 Sorting with ORDER BY Data Analytics —
- 25 Aggregates: COUNT, SUM, AVG Data Analytics —
- 26 GROUP BY and grouped results Data Analytics —
- 27 HAVING and aggregate filters Data Analytics —
- 28 NULL values and COALESCE Data Analytics —
- 29 JOIN types and matching rows Data Analytics —
- 30 SQL practice: build a clean query Data Analytics —
Applied SQL and data quality
Use practical SQL patterns, validate output and answer business questions.
- 31 Date ranges and time periods Data Analytics —
- 32 CASE expressions and categories Data Analytics —
- 33 Subqueries and common table expressions Data Analytics —
- 34 Window functions and ranking Data Analytics —
- 35 Safe parameters and SQL injection awareness Data Analytics —
- 36 Query performance and indexes Data Analytics —
- 37 Data quality checks in SQL Data Analytics —
- 38 SQL analysis project: answer a business question Data Analytics —
- 39 Power BI Desktop and report workflow Data Analytics —
- 40 Importing data and choosing sources Data Analytics —
Power BI modeling and dashboards
Prepare data, build a semantic model and create clear Power BI reports.
- 41 Power Query and repeatable transformations Data Analytics —
- 42 Relationships and data models Data Analytics —
- 43 Measures, columns and DAX context Data Analytics —
- 44 KPI cards and summary visuals Data Analytics —
- 45 Choosing a visual and accessible color Data Analytics —
- 46 Filters, slicers and drill-through Data Analytics —
- 47 Dashboard layout and visual hierarchy Data Analytics —
- 48 Power BI capstone: build a trustworthy dashboard Data Analytics —
- 49 Business reporting and audience needs Data Analytics —
- 50 Sampling, bias and representativeness Data Analytics —
Business reporting and analytics capstone
Communicate insights responsibly and deliver reproducible analysis.
- 51 Correlation, causation and careful claims Data Analytics —
- 52 Forecasts, uncertainty and assumptions Data Analytics —
- 53 Report QA and reconciliation Data Analytics —
- 54 Sharing reports and access control Data Analytics —
- 55 Data privacy, retention and responsible use Data Analytics —
- 56 Capstone: deliver a reproducible analytics report Data Analytics —
Final exam and certificate
40 questions from the whole track. Pass mark is 60%. Retakes are free and the best score is the one printed on your certificate.
Create a free account to take the exam