data-analyst

Exploratory data analysis on a CSV file involves inspecting data quality, identifying patterns, and understanding distributions using Python libraries like pandas and numpy. This Skill performs EDA to inspect datasets, handle missing values, and report key statistical measures.

Yes, you can use pandas and matplotlib to clean data and generate visualizations. This Skill handles missing values, standardizes formats, removes duplicates, and creates informative charts like histograms and scatter plots to communicate findings.

Identifying customer segments from purchase history requires analyzing demographic and transactional data to uncover purchasing behaviors. This Skill analyzes datasets to derive actionable insights and identify key customer segments through statistical computations.

Yes, this data analysis approach supports hypothesis testing and statistical modeling. It performs statistical analysis by executing hypothesis testing and reporting key statistical measures to provide in-depth understanding of your dataset.

Handling missing values and removing duplicates involves data cleaning steps to standardize formats for reliable analysis. This Skill performs robust data manipulation to clean datasets, ensuring accurate statistical computations and visualizations.