DATA ANALYST BOOTCAMP

This project focuses on developing a system that evaluates and predicts employee performance using historical performance data, incorporating employee metrics through data analytics techniques.

The dataset consists of employee performance data, including work output, attendance, engagement levels, feedback, and managerial assessments. Preprocessing involves:

• Detecting patterns and outliers in employee performance data.
• Incorporating managerial views, including performance reviews and rating.
• Labeling employees as high performers, average performers, or underperformers based on both quantitative metrics and managerial feedback.

The objective is to create an interactive dashboard that comprehensively views inventory levels, turnover rates, and reordering triggers, and reordering triggers using Power BI practice projects

• The dataset includes inventory data with product details, stock quantities, reorder thresholds, and sales history.
•  Columns may include Product ID, Current Stock, Reorder Level, and Sales Quantity.
• Preprocessing involves calculating stock turnover rates and creating calculated columns for reorder suggestions.

The goal of this project is to develop a system that identifies fraudulent healthcare claims by analyzing historical claims data using data analytics techniques.

The dataset consists of healthcare claims data containing information about medical procedures, diagnoses, and billing amounts. The preprocessing steps include:

• Detecting anomalies within the data.
• Generating features to capture claim patterns.
• Labeling claims as either legitimate or suspicious.