Sales Data Analysis

This project involves analyzing and visualizing a dataset to identify key insights and trends. The process includes:

Data Cleaning: Preparing raw data for analysis by handling missing values, correcting data types, removing duplicates, and standardizing formats.

Exploratory Data Analysis (EDA): Uncovering data patterns, distributions, relationships, and potential outliers to understand the dataset’s structure and components.

Power BI Visualization: Presenting findings in an interactive dashboard to convey insights effectively to stakeholders.

2. Data Cleaning

Use SQL (file: Data Cleaning.sql) for:

Removing duplicates and null values.

Standardizing data formats (dates, text, etc.).

Filtering out irrelevant data.

Preparing the dataset for further analysis.

3. Exploratory Data Analysis (EDA)

Execute an EDA SQL script (file: EDA.sql) to:

Identify data trends, patterns, and distributions.

Calculate key statistics such as averages, medians, and variances.

Perform segmentation analysis (e.g., customer segments, product categories).

Explore relationships among variables to inform visualization design.

4. Visualization with Power BI

After cleaning and analyzing the data:

Create Visuals: Use bar charts, line graphs, scatter plots, pie charts, and maps to represent the insights.

Interactive Dashboards: Design dashboards with filters, drill-down options, and tooltips for a dynamic experience.

Focus on KPIs: Highlight key performance indicators (KPIs) for easy access to critical metrics.

Storytelling: Guide the viewer through a narrative by arranging visuals logically