| Course | AC312 Fundamentals of Accounting Analytics |
|---|---|
| Unit | Unit 3 |
| Paper type | Data cleaning log |
| Length | About 580 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Accounting |
| Updated | October 2026 |
Free sample paper for AC312 Unit 3
28,977 Rows In, 28,640 Kept, Every Change Written Down: A Unit 3 Data Cleaning Log for a Grain Cooperative
[Student Name]
Purdue University Global
AC312: Fundamentals of Accounting Analytics
Unit 3 Data Cleaning Log
[Instructor Name]
[Date]
Plainsmark Cooperative, its extract and every figure are composite; the article and textbooks cited are real.
Standards for the Log
Every decision is written before it is applied, with the rule stated so another analyst could repeat it. Rows are removed only when they are not part of the population; everything else is corrected or flagged and kept. The goal is a tidy file in which each row is one settlement and each column one attribute (Wickham, 2014).
Log Entries
1. Exact duplicates. Issue: two monthly exports overlapped by three days. Rule: rows identical in every field are duplicates. Effect: 214 rows, $1,384,226.40, removed.
2. Out-of-period rows. Issue: the extract ran from August 25, 2024, to September 6, 2025. Rule: keep settlements dated September 1, 2024, through August 31, 2025. Effect: 63 rows, $402,915.00, removed.
3. Voided settlements. Issue: 30 settlements were voided and reissued, leaving each original and its reversal in the file. Rule: remove matched void pairs, which net to zero. Effect: 60 rows, $0.00, removed.
4. Date formats. Issue: 4,108 dates were stored as text in two layouts, month first and day first. Rule: convert both to a true date using the elevator code, since two elevators' systems write day first. Effect: 4,108 rows changed, no dollars affected.
5. Bushels keyed in pounds. Issue: 18 wheat settlements show bushels sixty times larger than the scale ticket. Rule: where bushels times 60 pounds matches the ticket's net weight, divide by 60. Effect: 18 rows changed; amounts were already right, so no dollars changed.
6. Commodity codes. Issue: 412 rows carry a trailing space, so "SB " and "SB" count as two crops. Rule: trim spaces. Effect: 412 rows changed.
7. Missing member numbers. Issue: 41 settlements carry a member number not in the member master. Rule: match by tax identifier. Effect: 35 matched and corrected; 6 rows, $38,204.10, flagged and kept for review.
8. Negative bushels. Issue: 728 rows carry negative bushels and amounts. Rule: these are discount corrections credited back to members, so keep them and label them as adjustments rather than removing them. Effect: 728 rows labeled.
Only three entries remove rows, and each removes rows that were never part of the year's settlements; the other five fix or label rows, because deleting a row the analyst does not understand would make the population quietly smaller.
Before and After
1. Rows: 28,977 before; 337 removed; 28,640 after.
2. Dollars: $188,189,259.95 before; $1,787,141.40 removed; $186,402,118.55 after.
3. Flagged for review: 6 rows, $38,204.10.
Control Total
General ledger, cost of grain purchased for the year: $186,951,268.55. Less the year-end accrual for deferred-payment contracts priced but not yet settled, which has no settlement row: $549,150.00. Ledger amount the settlement file should equal: $186,402,118.55. Cleaned settlement file: $186,402,118.55. Difference: $0.00.
The agreement shows that the cleaned file is complete for the year, so every later test is run over the whole population of settlements and not a subset (Richardson et al., 2021).
What the Log Is For
An auditor relying on this analysis will ask how the data got from the extract to the tests. The log answers that question entry by entry, and it lets anyone rerun the cleaning when next year's extract arrives (American Institute of Certified Public Accountants, 2017).
References
American Institute of Certified Public Accountants. (2017). Guide to audit data analytics. AICPA.
Richardson, V. J., Teeter, R. A., & Terrell, K. L. (2021). Data analytics for accounting (2nd ed.). McGraw Hill.
Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1-23. https://doi.org/10.18637/jss.v059.i10
Reading the AC 312 Unit 3 instructions
Unit 3 of AC312 gives students a raw extract and asks for a documented cleaning process. State the standards you will follow before listing entries: write each rule before applying it, remove only rows outside the population, and correct or flag everything else. For each entry, give the issue found, the rule applied, the rows and dollars affected, and whether rows were removed, changed or flagged. Number the entries so later units can refer to them. Watch for duplicates from overlapping exports, out-of-period dates, voids and reversals, inconsistent date formats, unit errors, inconsistent codes and missing keys. Summarize with a before-and-after panel. Finish with a control total that ties the cleaned file to an independent figure, explaining any reconciling item. A paragraph on how the log will be used shows its purpose. Cite the AICPA guide and Wickham's tidy data article in APA 7.
Inside the AC312 Unit 3 example
The sample opens with three standards for the log and cites Wickham's 2014 definition of tidy data. Eight numbered entries follow, each with the issue, rule and effect: 214 exact duplicates worth $1,384,226.40 removed, 63 out-of-period rows removed, 30 void pairs removed at no net dollar effect, 4,108 text dates converted using the elevator code, 18 wheat rows divided by 60 after a match to the scale ticket, 412 commodity codes trimmed, 41 missing member numbers with 35 matched and 6 flagged, and 728 negative rows labeled as adjustments. The highlighted sentence explains why only three entries remove rows. The before-and-after panel gives rows, dollars and flagged items. The control total subtracts a $549,150 deferred-contract accrual from the ledger and agrees to the cent. A closing section explains how an auditor and next year's analyst will use the log.
Where the points sit in the AC312 Unit 3 rubric
The AC312 grader checks first that every row removed or changed is accounted for, so the log's counts must add up from the extract to the cleaned file. Each entry needs its issue, rule and effect, and entries missing the rule lose credit because they cannot be repeated. Removing rows that belong in the population is the costliest mistake, so the reasoning for each removal is read closely. Flagging rather than deleting uncertain rows shows judgment. The control total is a separate scoring point, and a reconciling item must be explained, not just plugged. A before-and-after panel makes the result easy to verify. Explaining the log's purpose for auditors and future use adds credit. At Purdue Global, the log's standards should rest on the AICPA analytics guide and Wickham's tidy data article, both listed in APA 7.
AC312 Unit 3 help from the desk
Cleaning logs often list what was done without saying why, which makes the work impossible to repeat. Students delete rows they do not understand, such as negative amounts, which quietly shrinks the population. Others remove duplicates without checking whether they are real repeat transactions. Out-of-period rows are kept, or rows inside the period are cut by a wrong date format. Unit errors are fixed by guesswork rather than matched to a source. The control total is missing, or the cleaned file is forced to agree by an unexplained difference. Counts in the panel do not add. If your extract comes from a payroll, sales or expense system, a writer can build the log, the panel and the control total from your file's issues.
Get an AC312 Unit 3 example written to your instructions
Attach the extract supplied with the Unit 3 assignment, or describe its columns if the file cannot leave your section, plus the brief and rubric. The log, the before-and-after panel and the control total come back within 24-48h, alongside the cleaned file itself. A first custom sample is free, and the log is written so your instructor could rerun it. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
More AC312 papers and BS in Accounting samples
- AC312 Unit 1: What Data Can Answer Post
- AC312 Unit 2: Data Question Framing Exercise
- AC312 Unit 4: Descriptive Analysis Exercise
- AC312 Unit 5: Ledger Relationship Analysis
- AC312 Unit 6: Exception Testing Problem
- AC312 Unit 7: Visualization Set
- AC312 Unit 8: Seminar Reflection
- AC312 Unit 9: Dashboard Build
- AC312 Unit 10: Analytics Summary Report
- AC302 Unit 3: Indirect Operating Section
- AC301 Unit 3: Warranty Estimation Exercise
- AC300 Unit 3: Classified Balance Sheet
- AC256 Unit 3: Gross Income Problem
AC312 Unit 3 questions, answered
How detailed should each log entry be?
Detailed enough that another person could repeat it without asking. That usually means the issue, the rule, the count of rows and dollars affected, and whether the rows were removed, changed or flagged. A sentence or two per entry is typical. The sample's entries run to about forty words each, and the before-and-after panel carries the numbers so the prose does not have to.
Where can I find a free AC312 Unit 3 sample paper?
This page gives a complete AC312 Unit 3 data cleaning log for a composite grain cooperative, with eight entries, a before-and-after panel and a control total agreeing to the general ledger, and APA 7 references.
Why are negative rows kept in the AC312 Unit 3 log?
They are discount corrections credited back to members, real transactions that belong in the year's population. Deleting them would make the cleaned file disagree with the ledger and hide adjustments later tests may need.
How does the AC312 Unit 3 control total reconcile?
The ledger's grain purchases include a $549,150 accrual for deferred contracts priced but not yet settled. Removing it leaves $186,402,118.55, which the cleaned settlement file matches to the cent.
How were bushels keyed in pounds found in AC312 Unit 3?
Eighteen wheat rows showed bushels sixty times larger than the scale ticket's net weight implied. Wheat weighs 60 pounds a bushel, so the rows were divided by 60 after matching each to its ticket.