Key takeaways
- Each task has a situation, a hint and a way to check your work.
- Tasks cover Excel, SQL, Python, statistics and dashboards.
- Work on a copy of any data and keep the original untouched.
- Checking your result in two ways is a habit employers value.
- Attempt every task before reading the hint.
What is data analytics practice questions?
How should I use these practice questions?
Read the task, try it for ten minutes without help, then read the hint. Always check the answer in a second way, for example with a different formula or a hand count on a sample. Use made-up or public data, never confidential company data.
What are the Excel tasks?
Task 1: revenue by city. A sheet has columns City, Product, Quantity and Price. Build revenue per city. Hint: add a Revenue column, then a PivotTable. Check: the PivotTable grand total should equal the sum of the Revenue column.
Task 2: flag late payments. Each invoice has a due date, a paid date and a status. Mark invoices paid after the due date. Hint: compare the two dates with IF. Check: count flagged rows with COUNTIF and spot-check five rows by hand.
Task 3: clean names. Names arrive in mixed case with extra spaces. Standardise them. Hint: TRIM and PROPER. Check: no cell should start or end with a space.
What are the SQL tasks?
Task 4: top customers. Find the three customers with the highest total order value. Hint: join, group and order with a limit. Check: the sum of all customers' totals should equal the total of the orders table.
Task 5: customers without orders. List customers who never ordered. Hint: a left join and a null test, or NOT EXISTS. Check: the count plus customers with orders equals total customers.
Task 6: month on month change. Show monthly revenue and its change from the previous month. Hint: a window function with LAG. Check: the first month should have no previous value.
What are the Python tasks?
Task 7: load and summarise. Load a CSV, show missing values per column and the mean of a numeric column. Hint: read_csv, isna and describe. Check: the row count equals the file's row count minus the header.
Task 8: group and plot. Find average sales per category and draw a bar chart. Hint: groupby and a bar plot. Check: the number of bars equals the number of categories.
Task 9: fix dates. A column has dates as text in two formats. Convert it to a real date column. Hint: to_datetime with a format or errors set to coerce. Check: count how many values failed to convert.
What are the statistics tasks?
Task 10: median or mean? Monthly incomes in a small sample include one very large value. Which average would you report and why? Hint: think about outliers. Check: compute both and compare.
Task 11: reading an A/B test. Variant B has a 4.2 percent conversion against 4.0 percent for A, with a small sample. What do you say? Hint: difference may be chance; consider sample size and a test. Check: write a sentence that includes the words "not enough evidence" or "evidence of a difference" and justify it.
What is the dashboard task?
Task 12: design a one-page dashboard. A manager wants to track weekly sales. List the four numbers, two charts and two filters you would include, and why. Hint: start from decisions, not data. Check: can every item answer a question the manager actually asks?
How do I track progress?
Make a table with the task number, date, time taken, whether you needed the hint and what you learned. Repeat tasks after a week. If you solve a task faster without a hint, the skill is sticking.
How do I check my own work without an answer key?
Use three checks. Reconcile: totals from two routes should match, such as a PivotTable and a SUMIFS. Sample: pick five rows and compute the result by hand. Reasonableness: ask whether the answer makes sense, for example whether a percentage is between 0 and 100. These checks catch most errors and are exactly what experienced analysts do.
How can I make the tasks harder?
Add a twist after solving each task. Use a larger file, add missing values, change the question slightly or ask for a chart as well. You can also swap tools: solve the same task in SQL and then in Python and compare the answers. Matching results in two tools is strong evidence that you are right.
How do I turn practice into portfolio work?
Pick two tasks that you enjoyed, expand them with a real dataset and write a short explanation of the question, method and result. Those two pieces, shown clearly, are more useful in an interview than a long list of completed exercises.
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