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Overview
Skill path
1
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ScenarioBroken Aggregates and Messy Media Information
retail-and-cpg
approach
data-understanding
data-wrangling
problem-understanding
data-quality
databricks
azure
python
01h 30m
2
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ScenarioUncovering Misleading Insights: Debugging EDA Visualizations
retail-and-cpg
approach
data-understanding
data-quality
data-visualization
problem-understanding
data-wrangling
python
azure
01h
3
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ScenarioFeature Engineering for Media Spend Analysis: Identifying and Correcting Common Pitfalls
retail-and-cpg
data-understanding
data-visualization
problem-understanding
data-wrangling
python
azure
01h
4
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ScenarioOptimizing Model Performance: Baseline Selection and Tuning
retail-and-cpg
approach
data-wrangling
ml-modelling
databricks
azure
python
02h 30m
5
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ScenarioStreamlining Experiment Tracking: Messy Logs
retail-and-cpg
approach
databricks
python
azure
mlflow
01h
6
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ScenarioCI/CD Pipeline Troubleshooting
retail-and-cpg
approach
programming
model-retraining
databricks
python
azure
02h 30m
7
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ScenarioTroubleshooting REST API Deployment Failures
retail-and-cpg
approach
databricks
azure
mlflow
model-deployment
python
02h