Exploratory Data analysis report
Project phase: Data Engineering and Preparation
You investigate what your team's dataset actually contains before any modelling starts: its size, structure, and variable types, with descriptive statistics and well-chosen visualisations showing distributions, relationships, and outliers. You give an explicit account of missing data — where it occurs, how much, and whether it looks random or systematic — with each figure or table accompanied by a written interpretation, and end with concrete, substantiated conclusions on whether the data is ready for modelling.
Starting Points
Key Points
- I have started my analysis with a clear research question and a conscious choice for my approach.
- The report describes the dataset size, structure, and variable types clearly.
- I have used meaningful visualizations (charts/graphs) to show distributions, relationships, and outliers.
- I have included a clear analysis of missing data (where, how much, and why it's happening).
- I have provided well-substantiated arguments for my chosen strategies (like imputation or exclusion).
- Every figure and table is accompanied by a written interpretation (don't just show a graph, explain it!).
- I have concluded with concrete advice about whether the data is ready for the modeling phase.
- The report is written in a way that is easy to understand for the intended audience.
- The analysis is reproducible (the code in my notebooks/scripts matches the findings in the report).
- The report is a standalone markdown document, made available in GitLab.