DATA ANALYSIS

A practical analysis file for turning messy tables into a clearer story: what changed, what patterns matter, and what questions should be asked next.

Raw data rarely arrives ready for interpretation. Missing values, inconsistent categories, duplicated rows, and unclear business definitions can make even simple analysis feel unreliable.

I start by profiling the data, cleaning only what I can explain, and writing down assumptions before exploring patterns. The goal is not to force a dramatic insight, but to build a trustworthy path from raw inputs to a useful interpretation.

• Separate cleaning steps from exploration so the logic stays reviewable.
• Prefer simple grouped views before jumping into more advanced analysis.
• Keep notes on uncertainty instead of hiding weak spots in the data.

A documented analysis workflow with cleaned tables, exploratory summaries, and a short interpretation layer for decision-making.

Good analysis is as much about restraint as technique. The most useful work often comes from making the data easier to question.

Turn the workflow into a small repeatable notebook template with clearer data quality checks and exportable summary tables.