data-analyst

Exploratory data analysis on a CSV dataset uses pandas and numpy to import data, calculate descriptive statistics, and identify correlations. This process generates actionable insights, distributions, and trend analyses from raw files.

Automating data cleaning and visualization for sales reports requires a Python data stack utilizing pandas, matplotlib, and seaborn. It transforms raw datasets into cleaned dataframes, charts, and summarized KPI dashboards for stakeholders.

Yes, you can analyze data directly from SQL databases using pandas by establishing a database connection. This allows you to query relational data, perform data cleaning, and execute exploratory data analysis within your Python environment.

Yes, this data analysis approach works with Excel files and API sources alongside CSV and SQL. It imports these varied formats into pandas dataframes to perform uniform data preparation, EDA, and reporting workflows.

You need a standard data science environment configured with the Python data stack including pandas, numpy, matplotlib, and seaborn. These libraries provide the foundational functions required to execute data cleaning, EDA, and visualization tasks.