A complete BU 351 Unit 2 data quality assessment: a field by field profile of missing, duplicated, inconsistent and out of range values, each with its downstream effect. Searches like "bu 351 unit 2 assignment example", "bu351 unit 2 sample" and "bu 351 unit 2 example" land here.
What a finished BU 351 Unit 2 data quality assessment looks like
The deliverable is a profile table plus a findings section, and the findings are where the credit sits. The profile runs one row per field: its type, how many records carry a value, how many distinct values appear, and the smallest and largest where that makes sense. Anything unexpected in that table becomes a finding written up underneath, with a count, an example record showing the problem, a judgment about likely cause, and a statement of which later stage it would damage. Duplicates are handled as a separate question, since a repeated row and a repeated identifier are different problems. A treatment section says what was done about each finding, whether that is removal, correction, recoding or leaving it alone, with the reasoning attached. Nothing is analyzed yet.
How a BU 351 Unit 2 example is structured
The profile comes before the findings because a problem stated without a count cannot be weighed, and an issue affecting a handful of records is not the same as one affecting a third of them. Findings carry an example record so a reader can see the defect rather than take it on trust. Cause is guessed at explicitly, since a field missing at random and a field missing only for one customer type require different treatment and the second is a far more serious finding. Each finding names the stage it would damage, which is what makes this a pipeline unit rather than a cleaning chore: a field with scattered gaps may be harmless for a descriptive total and fatal for a model. Treatment decisions are recorded with reasons because a cleaned dataset with no record of what was changed cannot be trusted by anybody downstream. No analysis appears.
One profile row per field
Type, coverage, distinct values and range are recorded for every column, which is what turns a hunch into a countable finding.
Findings carry counts and examples
Each problem is reported with how many records it touches and one record showing it, rather than described in general terms.
Random gaps separated from patterned ones
Gaps concentrated in one segment are treated as a different and more serious finding than gaps scattered across the whole file.
Every finding names the stage affected
A defect harmless for a descriptive total can be fatal for a model, so each one says where it would bite.
Treatment decisions written down with reasons
What was removed, corrected, recoded or deliberately left alone is recorded, because a cleaned file nobody documented cannot be trusted.
Where marks go in BU 351 Unit 2
This one loses points by describing data quality rather than assessing a dataset. Two pages on why clean data matters, followed by a list of quality dimensions from the reading, leave the supplied file unexamined. Findings stated without counts cannot be prioritized, and a marker cannot tell a trivial problem from a structural one. Cleaning performed silently, so that the analysis simply runs on a corrected file, removes the deliverable entirely. Duplicate rows and duplicate identifiers reported as one problem hide the more serious of the two. Outliers deleted because they look extreme discard the observations often worth the most. Assessments that stop at counting gaps never ask whether the gaps have a pattern. A production dataset or customer table pulled from work is the employer's, whatever is stripped from it.
Get a BU 351 Unit 2 example written to your instructions
Post us the Unit 2 instructions, the BU 351 rubric your classroom carries, and the dataset the assignment supplies. We write a custom example that profiles every field, reports findings with counts and example records, separates patterned gaps from random ones and documents each treatment decision. First custom sample free, returned in 24 to 48 hours.
BU 351 Unit 2 questions, answered
What if the supplied dataset is already clean?
Say so and show the evidence, since a clean file is a real result and not a dead end. Report the profile, note that coverage is complete and the coded fields hold no strays, then look harder at the things a clean file still hides: values that are present but implausible, categories that mean the same thing under two labels, and dates that fall outside the period the file claims to cover.
Should I remove records with missing values?
Only after saying what removing them costs. Dropping incomplete records is easy and quietly changes who is left in the file, which matters if the gaps concentrate in one group. Where the instructions leave the choice open, show the count you would lose, say whether the remainder still represents the population, and record the decision either way rather than doing it silently.
Which tool should the profile be built in?
Whatever your section requires, and where nothing is specified a spreadsheet handles a file of this size comfortably. Pivot counts, a distinct value list per field and a few conditional checks produce everything the profile needs. If you use a statistical package, paste the output into the document rather than referring to it, since the marker is reading a report and not opening your working file.