data-analyst-workflow

To analyze a CSV dataset, this workflow inspects the schema, cleans data while documenting assumptions, computes descriptive statistics, detects anomalies, and summarizes actionable takeaways in plain language.

The best way to clean raw data for reproducible analytics is to normalize values and document every meaningful assumption during the process, ensuring downstream statistical computation and interpretation remain transparent.

Exploratory data analysis detects outliers and seasonality by inspecting schema and data types first, then computing descriptive statistics and comparisons to identify segmentation effects before interpreting outcomes.

Yes, you can use this workflow for recurring KPI checks and exploratory data work because it is explicitly scoped for CSV analyses, automating data cleaning, anomaly detection, and statistical computation.

Descriptive statistics require schema inspection and missingness checks first because identifying data types, duplicates, and obvious quality issues ensures reliable, actionable insights rather than skewed statistical computations.

A limitation of automating a data-cleaning workflow for CSV analysis is that it requires documented assumptions for every meaningful change, meaning heavily unstructured data or non-CSV formats fall outside its intended scope.