Explore data-analysis tutorials for cleaning, tabular work, and clear conclusions, with guidance on pandas, SQL, spreadsheets, and reproducible practice.
11 learning resources in this collection
Data analysis turns a question and a dataset into a defensible explanation. Cleaning, grouping, and visualization are tools in that process. Begin by understanding where the data came from and what each row represents.
Choose pandas when Python fits your workflow, SQL for relational queries, or spreadsheets for a smaller practical task. Official tutorials are useful for focused operations. Practice with missing and inconsistent values instead of assuming every example dataset is already clean.
Answer one question using a small public dataset. Keep the cleaning steps repeatable and explain what the result cannot establish. Check a total or sample manually.
Coursera · University of Michigan — Christopher Brooks
Review Michigan’s analytics ethics course for public-sector practitioners, with political context, professional responsibilities and a policy case study.