Data Wrangling

Context: FIT1043_MOC · the Wrangling stage of the value chain · turns raw data usable · precedes analysis · fixes Data Quality Problems found by auditing

Quick Revision

• 🎯 Objective: transform raw data into analysable tidy data ➔ valid, actionable results.
• ⚡ Key Constraint: raw data is messy (varied shapes/formats, entry mistakes) — wrangling is the necessary bridge, often the biggest time cost.

📝 Core

• Definition ➔ manipulating/transforming raw data into data that can be analysed for valid, actionable insight.
• Pipeline ➔ $\text{raw data}\Rightarrow \text{wrangling}\Rightarrow \text{tidy data}\Rightarrow \text{analysis}\Rightarrow \text{knowledge}$.
• Data product ➔ Data + Wrangling + Analysis = Data Product.
• Steps ➔ pre-processing, preparation, cleansing, transformation (among others).
• Why needed ➔ data comes in all shapes/sizes; different files format differently; data-entry mistakes happen.

⚠️ Common Mistakes

• 💡 Ideal data ≠ real data ➔ never assume clean input; audit first (Data Auditing in Pandas), then wrangle.
• 💡 Wrangling ≠ analysis ➔ it prepares data; the discovery/modelling happens afterwards in the Analysis stage.

🧠 Active Recall

State the wrangling pipeline and what "Data Product" means.

Answer

• Short answer: raw data → wrangling → tidy data → analysis → knowledge; a Data Product = Data + Wrangling + Analysis.
• Why: Bridge to analysis ➔ wrangling (pre-processing/cleansing/transformation) makes messy raw data valid and usable before any modelling.