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Broken Aggregates and Messy Media Information

7 Inputs
1 Hour 30 Minutes
Intermediate
scenario poster
Industry
retail-and-cpg
Skills
approach
data-understanding
data-wrangling
problem-understanding
data-quality
Tools
databricks
azure
python

Learning Objectives

Align data from different granularities and temporal formats
Transform media data from long to wide format for model readiness
Handle missing values while preserving business meaning
Ensure proper joins between media and sales data to avoid leakage or misalignment

Overview

You're part of GlobalMart’s analytics team building a Market Mix Modeling (MMM) pipeline to analyze how media investments drive sales. However, the preprocessing steps taken by the team have led to misaligned data, wrong aggregations, and an unpivoted mess — severely affecting model results.

Your job: Debug these upstream issues before modeling begins.

Prerequisites

  • Time series granularity (daily, weekly, monthly)
  • Experience with Pandas operations
  • Understanding of MMM data structures