Computer Lab Inventory with Data Science: A 2027 Project Guide

The starting point for this Data Science exercise is a lab inventory volunteer working with stock_entry records. Their question is specific: How many available items are recorded for each type? Building an exploratory modelling study around that question gives the project a manageable scope and makes the result possible to verify.

This is a 2027 study-edition project brief. All example records and operational situations are fictional. Start with basic Data Science knowledge, the ability to inspect structured data and a local practice environment. The complete first version should answer the stated question before adding accounts, real integrations or additional screens.

Define the project rule

Available stock excludes retired items. A stock-entry identifier is different from an item-type label, so two valid deliveries of the same type must remain distinct records. For this exercise, one row represents one stock_entry. The stable key is record_id; item_type is the grouping or filtering label. The numeric quantity field uses items. Accepted states are received, available, retired.

The summary selects status available and applies sum to the selected numeric values. Keep this definition visible in the interface, response or report so another learner can reproduce it. Use positive finite values in the starter fixture and define any future zero or missing-value policy explicitly.

Choose the Data Science approach

Separate descriptive analysis from a predictive experiment. Start by defining what could be known at decision time, then investigate whether a target is meaningful. Use a simple baseline and keep evaluation records separate from training. A three-row fixture can test processing logic, but it cannot support a credible model-performance claim.

The three design decisions to explain in your project notes are:

• Question and target definition. Apply this choice to stock_entry records and explain its effect on quantity.
• Leakage-aware data separation. Apply this choice to stock_entry records and explain its effect on quantity.
• Comparison with a simple baseline. Apply this choice to stock_entry records and explain its effect on quantity.

Work through a small sample

A separate predictive extension needs an adequately sized labelled synthetic dataset, a declared target and held-out evaluation. The example below only checks the processing contract.

[
  {
    "record_id": "lab-inventory-01",
    "item_type": "Keyboard",
    "quantity": 2,
    "status": "available",
    "recorded_on": "2027-01-04"
  },
  {
    "record_id": "lab-inventory-02",
    "item_type": "Display Cable",
    "quantity": 1,
    "status": "received",
    "recorded_on": "2027-01-05"
  },
  {
    "record_id": "lab-inventory-03",
    "item_type": "Headset",
    "quantity": 3,
    "status": "available",
    "recorded_on": "2027-01-06"
  }
]

Only the first and third records match available. Their quantity values are 2 and 3; the full-fixture result is 5 items, with 2 matching records. Selecting item_type = Keyboard leaves the first record, so the result becomes 2 items, with 1 match. This provides two independent checks for the filter and aggregation.

The full-fixture output contract is:

{
  "matching_count": 2,
  "operation": "sum",
  "value": 5,
  "unit": "items"
}

Check the result by item_type

The overall figure answers the all-records version of the question. Grouping by item_type produces the following separate expectations, using the same available rule. The middle group has a record in the source fixture but no qualifying observation.

Use this breakdown when answering the group-level question. Compare like-for-like units and keep the matching count visible, especially for averages. A different filter changes which records enter the calculation; it does not change the meaning of the metric.

The code blocks describe sample data and an expected result. Implement the surrounding feature using the installed version of your chosen tools, then save the actual run evidence separately from these expectations.

Build the feature in three passes

1. Establish a non-predictive baseline

First answer the descriptive question using the sum of quantity for available records. This creates a verified baseline and exposes schema problems. Three sample stock_entry records support processing tests, not a meaningful training experiment.

2. Define a defensible extension

Propose a synthetic target such as whether a future quantity exceeds a threshold chosen before training. State when the prediction would be made and which item_type and status information would be available then. Obtain enough labelled synthetic examples to study the question.

3. Evaluate without leaking the answer

Separate training and evaluation data before fitting preprocessing. Compare the model with a simple reference and inspect errors by item_type. Report the data construction and limitations. Keep measured evaluation results out of the article until the experiment has actually run.

Find the mistake before polishing the result

A useful defect to introduce deliberately is: Grouping by label removes one of two legitimate deliveries. First reproduce it against the fixture, then identify the smallest rule or state transition that explains the mismatch. Fix that boundary and rerun the full-fixture and selected-label examples. This creates a stronger explanation than changing several unrelated parts until the screen looks right.

The scenario-specific acceptance check is: Keep distinct entry identifiers and sum both valid deliveries. Preserve before-and-after evidence. Add a missing item_type, a negative quantity, an unrecognised status and a duplicate record_id as separate invalid cases. Define the rejection or duplicate-handling policy before testing, and verify that invalid input does not silently change valid existing records.

Check the Data Science concerns as well:

• Future information must not enter features.
• The baseline must use the same evaluation records.
• Tiny synthetic examples must not become accuracy claims.

For an empty filter match, the expected sum value is 0. Keep matching_count at zero. This distinction prevents an empty dataset from being interpreted as a measured observation.

Explain the finished work in a portfolio

Show the lab inventory volunteer question, the record definition and the expected calculation first. Then demonstrate a item_type filter and the acceptance check. Include the fixture, setup instructions, chosen tool versions, actual test output and one remaining limitation in the repository. If you show a screenshot, pair it with the input that produced it. Another learner should be able to repeat the same result from a clean checkout.

For a focused extension, add a second item_type selection and compare the two groups using the same rule. Specify how missing groups appear and keep the original fixture as a regression case. Expand scope only after the current calculation, state handling and recovery path remain understandable.

Questions learners often ask

Which stock_entry records belong in the result?

Select records whose status is exactly available and that satisfy the chosen item_type filter. Other states remain in the source data and can have separate summaries. Follow the domain rule above when a record changes state.

What should happen when no records match?

Return a zero total with a matching count of zero and a clear empty-result message. Keep the applied filter visible so the user can change it. Avoid using a loading indicator for a completed request that simply has no matching data.

How can I prove the quantity result is correct?

Reconcile the first and third sample records, then repeat the calculation for Keyboard alone. Save both expected and actual outputs, and rerun the scenario-specific acceptance check after making a change.

Continue learning with Groot Academy

Use this exercise as a concrete discussion point when choosing a learning path. Explore the related Groot Academy course and ask about the current syllabus, learning mode and project practice. Course availability and batch details should be confirmed directly with the academy.

For implementation details, consult the official Data Science documentation matching the tools you install. This project defines an original practice brief; it does not depend on a forecast about 2027 software releases.

Continue with Lab Maintenance Tickets using Data Science, or compare Computer Lab Inventory using Machine Learning. These companion practice guides are part of the 2027 learning collection.