Earn Google Data Analytics Certificate

Proof required

Submit: (a) completion badges or screenshots from the first three certificate courses, and (b) a spreadsheet analysis on a real public dataset demonstrating: data cleaning (at least 3 data quality issues identified and resolved), pivot tables summarising by at least 2 dimensions, at least 2 charts with axis labels and interpretive titles, and a 300-word written summary of what the analysis revealed.

What gets checked

• Dataset is real and publicly available — not a certificate teaching dataset
• Data cleaning is documented — what issues were found and what action was taken
• Charts have interpretive titles that state the finding, not just describe the data axis

Common mistakes

• Using the certificate's own teaching datasets — the point is applying skills to a new independent dataset
• Charts with generic titles like 'Sales by Month' rather than interpretive titles like 'Q3 Sales Declined 18% Across All Categories'

Resources

What a verifier looks for

• Verify the independent analysis uses a different dataset from the certificate teaching materials — this is the key test of whether skills were actually learned.
• Review the data cleaning documentation: does it identify specific issue types and state what action was taken for each?

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Proof required

Submit: (a) completion screenshots for the SQL and R certificate courses, (b) a SQL script on a real public database (BigQuery Public Datasets, SQLite, or PostgreSQL) performing at minimum a JOIN across two tables, an aggregate with GROUP BY, and a filter — with a 200-word explanation of what it answered, and (c) an R script on a real dataset that reads data, cleans it, produces two ggplot2 visualisations, with a 300-word analysis of results.

What gets checked

• SQL script uses a real public database, not a local teaching database
• R script is reproducible — another analyst can run it on the same dataset
• Both written analyses state a finding — not just a description of what was plotted

Common mistakes

• SQL script uses SELECT * without filtering or aggregation
• R visualisations are default output of example code without modification

Resources

What a verifier looks for

• Review the R script for reproducibility: are all library() calls present? Does the data source reference a URL or public dataset rather than a local file?
• Check the SQL script: does it answer a real question, or is it a demonstration of syntax?

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Proof required

Submit: (a) your Google Data Analytics Certificate completion credential, (b) an independent case study (minimum 1,000 words) presenting a business question, data source, cleaning and analysis methodology, visualisations, and a specific written recommendation, and (c) a Q&A log from presenting to a data professional (minimum 300 words with at least two methodological challenges and your responses).

What gets checked

• Certificate completion credential is from Google or Coursera — not a third-party mirror
• Case study is independent of the certificate capstone — new question, new dataset
• Recommendation is actionable and specific — 'further research is needed' is not a recommendation

Common mistakes

• Case study uses the same dataset as the certificate capstone
• Recommendation not grounded in the data

Resources

What a verifier looks for

• Review the case study for the full data analysis lifecycle: question → data → cleaning → analysis → visualisation → recommendation. Missing steps suggest incomplete work.
• Your challenges should probe methodological choices: 'Why did you exclude these outliers?' 'How did you handle missing data in column X?'

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