Xyrel VaughnFull Stack Engineer
Résumé
Credentials and applied proof
Data Analytics · Synthetic e-commerce dataIndependent applied exercise

Retail Operations Analysis

A transparent analysis of a small synthetic order dataset, connecting data preparation and descriptive statistics to fulfillment and return decisions.

Download the synthetic CSV Ask about the exercise
Evidence
Independent applied exercise
Supports
Data Analytics Level III
Data
Synthetic and privacy-safe
Focus
CSV · Data preparation · Descriptive statistics
Orders reviewed
24

Small synthetic sample across four sales channels.

Gross order value
₱57,830

Sum of order value before refunds, fees, or costs.

Average order value
₱2,410

Gross order value divided by 24 orders.

Return rate
16.7%

Four returned orders in the sample.

01

Scenario

A bounded problem with transparent evidence.

A fictional online retailer needs a quick operational view of order value, fulfillment time, channel mix, and returns before deciding where to investigate process friction.

  • Inspect and normalize a synthetic 24-order dataset
  • Summarize revenue, order value, fulfillment time, and returns
  • Compare channels and product categories
  • Translate observations into bounded operational recommendations
02

Method

How the exercise was approached.

01

Prepare

Validated dates, channel labels, numeric order values, fulfillment hours, and binary return flags.

02

Summarize

Calculated totals and simple averages, then grouped records by channel and category.

03

Interpret

Separated what the sample directly shows from questions that would require a larger dataset.

Observed

Findings from the available evidence.

  • Marketplace and social channels account for half of the sample orders, so channel-specific fulfillment checks deserve attention.
  • Returned orders appear across multiple categories; the sample does not justify attributing returns to one product group.
  • Long fulfillment times occur on more than one channel, suggesting the next analysis should include stock status and fulfillment-stage timestamps.

Recommended

Safe next actions.

  • Track order-created, packed, handed-to-carrier, and delivered timestamps separately.
  • Add return reasons before treating the return rate as a product-quality signal.
  • Compare gross order value with discounts, fees, refunds, and cost data before making profitability claims.

Inspectable artifact

Download the synthetic CSV

The downloadable file contains fictional records created for portfolio demonstration. It contains no customer or employer data.

Open artifact
Evidence and privacy boundary

The public summary omits the learner identifier, certificate and training numbers, QR code, signatures, and exact training location.

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