Webbo3 Data Analytics · Cohort 2 · Excel Track · Pod Task, Week 5
Pod Task Week 5: Final Submission
The complete Verdant Foods workbook: from a raw, messy file to a dashboard a manager can act on, with every step recorded in between.
By the end of this class you can
- Submit all five tabs, complete and consistent
- Know exactly what is checked, so nothing is lost on a technicality
- Describe the project in a way an employer understands
This is the end-to-end project the whole Excel track was building towards. Everything in it should be something your pod can explain, defend and, if asked, rebuild.
The Brief
Pod Task Week 5: Final Submission
- Tab 1, Raw Data: the original file, untouched (470 rows).
- Tab 2, Cleaning Log: all four issues documented with the count and the method used.
- Tab 3, Cleaned Data: the clean data, with the Quantity_Flag and Revenue columns added.
- Tab 4, Analysis: the 8 business questions answered, with formulas visible and a one-sentence interpretation for each.
- Tab 5, Dashboard: a one-page dashboard with a KPI row, 3 charts, 1 slicer and 3 written insights.
- Every chart title states a finding, not a field name.
- No 3D charts. No default Excel rainbow palette.
- Your names and the date on the Dashboard tab.
Hand in: The complete Verdant Foods workbook (.xlsx) in your pod's shared folder, by the deadline your tutor gives. All five tabs must be present.
Skills tested: End-to-end Excel analytics: data cleaning, formula analysis, PivotTables, dashboard design and business interpretation
How the Work Is Checked
Your tutor opens the workbook and works through it the way a manager would, then the way an auditor would. These are the questions the review asks:
| Tab | What is checked |
|---|---|
| Raw Data | Is it exactly the downloaded file? |
| Cleaning Log | Are all four issues there, each with a correct count and a method that could be repeated? |
| Cleaned Data | 455 transactions; no duplicates; real dates; four categories; flags on the right rows; Revenue empty where Quantity is missing |
| Analysis | Correct answers from formulas; visible formulas; blocks that add up to N84,828,800; meaning sentences that interpret rather than repeat |
| Dashboard | KPIs that match the Analysis tab; the three required charts; finding titles; a connected slicer; one colour system; three real insights; names and date |
| The whole pod | Can any member explain any tab when asked? |
A missing tab. Typed numbers where formulas should be. A Count of Revenue PivotTable. A dashboard average that includes the Missing rows as zeros. An edited Raw Data tab. Each is easy to avoid with the checks from Week 5, Virtual 2.
What You Can Now Say You Have Done
This project belongs in your portfolio. Here is how to describe it in three lines, the way an employer reads it:
Every claim in that paragraph is something your workbook proves. Keep a copy of it.
Checkpoint
Q1. How many tabs must the workbook have, and in what order?
Five: Raw Data, Cleaning Log, Cleaned Data, Analysis, Dashboard.
Q2. What must be on the Dashboard besides the charts?
The KPI row, the slicer, three insights, and your names and the date.
Tick Before You Move On
- ☐ All five tabs present, in order
- ☐ Every check in Week 5, Virtual 2 passed
- ☐ Names and date on the Dashboard
- ☐ Saved on the Dashboard tab and reopened from the shared folder
- ☐ Every pod member can explain every tab
Quick Recap
Five tabs, from raw file to decision, every step recorded. Formulas everywhere, visible; titles that state findings; insights that recommend. You have done, end to end, what a junior data analyst does in a real job.
Final Scenario
A month from now, in an interview, someone asks: "Have you ever worked with messy data?"
You say yes, and describe it: a 470-row file with duplicates, four spellings of every category and dates in two formats; how your pod found each problem, fixed it, logged it, valued every transaction it honestly could, and turned it into a dashboard with three recommendations.
Then you open the workbook and show them the Cleaning Log.
Congratulations on finishing the Excel track. SQL is next.
What comes next: the SQL track of Cohort 2, where everything you learnt here runs at the scale of a real company database.