THE ANALYSTANALYTICAL WORK SIMULATIONS
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ASSIGNMENT REGISTER / 09 ACTIVE

Nine assignments. One company.

Each assignment places the learner in a defined role at Meridian, with its own business moment, decision pressure, source neighborhood, analytical purpose, and handoff. Work them in sequence or assign them by capability in a course, lab, training program, or independent study plan.

01CC-241202BRIEF

Customer Care / Customer Insights Analyst

The Monday Scorecard

Reconcile two conflicting satisfaction figures before the executive review.

INTENDED AUDIENCE
New analysts and learners building foundations
PREPARED LEARNER
3–4 hours
NEWER LEARNER
5–8 hours
PREREQUISITES
Tables and data types · Basic SELECT/GROUP BY · Pandas introduction
SQL2
Python2
Data complexity2
Statistical reasoning1
Decision ambiguity3
Deliverable load2
SQL

Profile grains, scales, cohorts, duplicate interactions, and response coverage.

PYTHON

Reproduce the profile in Pandas, visualize scale/cohort differences, and create the scorecard artifact.

OPEN ASSIGNMENT
02CM-240708INVESTIGATION

Commercial Data Transition / Commercial Data Transition Analyst

The Quarter That Moved

Certify Q2 orders, revenue, and fulfillment timing after an acquisition cutover.

INTENDED AUDIENCE
Learners comfortable with joins and dataframe work
PREPARED LEARNER
5–8 hours
NEWER LEARNER
8–12 hours
PREREQUISITES
Python functions · Pandas joins · SQL joins and aggregation
SQL3
Python3
Data complexity3
Statistical reasoning2
Decision ambiguity3
Deliverable load3
SQL

Construct a stable order fact across headers, lines, events, and shipments.

PYTHON

Profile collisions and clock errors, build reusable exception flags, test reconciliations, and certify an extract.

OPEN ASSIGNMENT
03GX-250505DECISION

Product Experimentation / Product Experimentation Analyst

The Navigation Vote

Determine whether a mobile-navigation experiment warrants rollout.

INTENDED AUDIENCE
Intermediate analysts ready for statistical judgment
PREPARED LEARNER
8–12 hours
NEWER LEARNER
12–18 hours
PREREQUISITES
Probability and sampling · Confidence intervals · SQL cohort construction
SQL3
Python4
Data complexity3
Statistical reasoning4
Decision ambiguity4
Deliverable load4
SQL

Build one row per assignment and reconcile exposure, sessions, events, and orders.

PYTHON

Run balance checks, estimate effects and uncertainty, inspect distributions, and separate confirmatory from exploratory slices.

OPEN ASSIGNMENT
04OP-250320DECISION

Connected Reliability / Connected Reliability Analyst

Rollback Before Dawn

Recommend global rollback, scoped containment, or monitored continuation during a storm.

INTENDED AUDIENCE
Intermediate analysts ready for statistical judgment
PREPARED LEARNER
6–9 hours
NEWER LEARNER
10–16 hours
PREREQUISITES
Missing-data mechanisms · Time-aware joins · Exploratory visualization
SQL4
Python4
Data complexity5
Statistical reasoning4
Decision ambiguity5
Deliverable load4
SQL

Build point-in-time asset, telemetry, alert, weather, and field-operation evidence lanes.

PYTHON

Diagnose missingness, compare regions and periods, quantify counterevidence, and visualize decision thresholds.

OPEN ASSIGNMENT
05FO-250320DECISION

Field Operations Planning / Service Capacity Analyst

The 7:30 Capacity Call

Build a morning risk view for appointments likely to miss their service window.

INTENDED AUDIENCE
Intermediate analysts ready for statistical judgment
PREPARED LEARNER
10–16 hours
NEWER LEARNER
18–26 hours
PREREQUISITES
Supervised learning · Feature pipelines · Temporal validation · Classification metrics
SQL4
Python5
Data complexity4
Statistical reasoning4
Decision ambiguity4
Deliverable load5
SQL

Create a point-in-time appointment feature mart at a stable entity and scoring time.

PYTHON

Build sklearn pipelines, compare a baseline, use forward validation, inspect calibration/errors, and design an intervention threshold.

OPEN ASSIGNMENT
06SP-251201PRACTICUM

Supply Planning / Supply Planning Data Scientist

Forty-Eight Hours of Stock

Forecast 21-day SKU/warehouse risk and recommend transfers, expedites, or substitutions.

INTENDED AUDIENCE
Advanced learners and professional development groups
PREPARED LEARNER
12–18 hours
NEWER LEARNER
20–30 hours
PREREQUISITES
Time-series validation · Forecast error · Simulation · Operational constraints
SQL4
Python5
Data complexity5
Statistical reasoning5
Decision ambiguity5
Deliverable load5
SQL

Reconcile movement, demand, receipt, transfer, and vendor facts into a complete daily spine.

PYTHON

Backtest honest baselines, forecast intermittent demand, simulate lead-time risk, and produce a constrained action file.

OPEN ASSIGNMENT
07PR-260119DECISION

Field Operations Strategy / Senior Operations Analyst

The Orion Renewal

Audit a claimed 12% optimizer gain and make a procurement recommendation.

INTENDED AUDIENCE
Intermediate analysts ready for statistical judgment
PREPARED LEARNER
10–16 hours
NEWER LEARNER
18–28 hours
PREREQUISITES
Metric design · Join reconciliation · Quasi-experimental reasoning
SQL5
Python4
Data complexity5
Statistical reasoning5
Decision ambiguity5
Deliverable load5
SQL

Define stable route, stop, work-order, visit, and workforce outcomes without fanout.

PYTHON

Plot trends and heterogeneity, run sensitivity/placebo analyses, and build the board-ready evidence package.

OPEN ASSIGNMENT
08NL-241203PRACTICUM

Support Operations / Applied ML Analyst

The Queue Nobody Owns

Design a safe shadow-routing system for uncategorized support work.

INTENDED AUDIENCE
Advanced learners and professional development groups
PREPARED LEARNER
12–18 hours
NEWER LEARNER
20–30 hours
PREREQUISITES
Text representation · Classification · Error analysis · Model packaging
SQL4
Python5
Data complexity4
Statistical reasoning4
Decision ambiguity5
Deliverable load5
SQL

Construct leak-resistant conversation-level labels and train/validation cohorts.

PYTHON

Build TF-IDF and comparison pipelines, inspect class errors, add abstention, package inference, and define monitoring.

OPEN ASSIGNMENT
09MR-260120PRACTICUM

ML Governance / Model Risk Analyst

Too Good to Ship

Audit an implausibly strong cancellation model and decide its smallest safe path forward.

INTENDED AUDIENCE
Advanced learners and professional development groups
PREPARED LEARNER
8–14 hours
NEWER LEARNER
16–24 hours
PREREQUISITES
Model validation · Temporal splits · Calibration · Data governance
SQL3
Python5
Data complexity5
Statistical reasoning5
Decision ambiguity5
Deliverable load5
SQL

Trace model lineage, snapshot grain, labels, and point-in-time availability.

PYTHON

Reproduce leakage, compare grouped temporal validation, build an allow-listed baseline, and audit calibration and segments.

OPEN ASSIGNMENT
PLANNING A SEQUENCE?

The instructor desk compares prerequisites, workload ranges, scaffolding options, and review boundaries across all nine assignments without placing solution material in the learner path.

OPEN INSTRUCTOR DESK