Data Analysis is the process of collecting, cleaning, organizing, analyzing, and interpreting data to identify trends, solve business problems, and support better decision-making using tools such as Excel, SQL, Power BI, and Python.

• Data Analysis focuses on examining historical data to identify patterns and insights.
• Business Analytics uses data analysis, statistical methods, and visualization to help organizations make strategic business decisions and predict future outcomes.

I have worked with Microsoft Excel, Advanced Excel, SQL, Power BI, Python (Pandas, NumPy, Matplotlib), Statistics, Data Visualization, and Dashboard Development to analyze data and create business reports.

Data Cleaning is the process of removing duplicate records, correcting errors, handling missing values, and formatting data properly before analysis. Clean data improves the accuracy and reliability of reports and business decisions.

SQL (Structured Query Language) is used to store, retrieve, filter, join, and analyze data from databases. It enables analysts to extract meaningful information quickly and efficiently from large datasets.

Power BI is a Business Intelligence (BI) tool developed by Microsoft that transforms raw data into interactive dashboards, charts, and reports. It helps businesses monitor KPIs and make data-driven decisions.

ETL stands for:

• Extract – Collect data from various sources.
• Transform – Clean, organize, and modify the data.
• Load – Store the processed data into a database or reporting system for analysis.

A KPI (Key Performance Indicator) Dashboard is a visual report that displays important business metrics such as sales, profit, revenue, customer growth, and performance trends, enabling management to make informed decisions.