This repository contains all the tasks I completed during my Elevoo Internship in Data Analytics. Each task focuses on applying analytical, statistical, and visualization techniques using different tools such as Excel, Python, SQL, and BI platforms.

• Built an interactive dashboard in Excel to track sales performance.
• Used pivot tables, charts, and slicers for dynamic reporting.
• Highlighted KPIs like revenue growth, top products, and regional sales distribution.

• Performed EDA on the Titanic dataset using Python.
• Applied data cleaning, feature analysis, and visualization with Pandas, Matplotlib, and Seaborn.
• Extracted insights on survival rates by gender, age, and passenger class.

• Implemented Recency, Frequency, Monetary (RFM) Analysis on customer purchase data.
• Segmented customers into categories like loyal, at-risk, and high-value.
• Visualized customer clusters to support targeted marketing strategies.

• Cleaned raw survey data by handling missing values and duplicates.
• Generated 5 key insights using concise visualizations.
• Focused on presenting results in a simple, readable dashboard.

• Wrote SQL queries to analyze product sales trends.
• Generated insights on top-selling products, revenue contribution, and customer purchase behavior.
• Improved querying efficiency with joins, group by, and aggregate functions.

• Scraped job postings using Python (BeautifulSoup/Requests).
• Collected data on job roles, skills, and industries.
• Analyzed demand trends in the job market with Pandas and Matplotlib.

• Conducted time series analysis on retail sales data.
• Identified seasonal trends, monthly breakdowns, and anomalies.
• Created line and area plots for visual storytelling.

• Built a Power BI Dashboard to summarize retail performance.
• Included KPIs such as sales growth, profit margins, and customer demographics.
• Enabled drill-down analysis for interactive exploration.

• Analyzed the Brazilian E-Commerce (Olist) dataset.
• Combined multiple datasets (orders, payments, products, reviews, sellers).
• Delivered a business insights report highlighting customer satisfaction, delivery times, and payment preferences.
• Presented findings in a structured, executive-style report.

• Excel – Dashboards, Pivot Tables, KPI Tracking
• Python – Pandas, NumPy, Matplotlib, Seaborn, BeautifulSoup
• SQL – Data querying and sales analysis
• Power BI – Interactive dashboards
• Jupyter Notebook – EDA, analysis, and reporting

This internship allowed me to gain hands-on experience in real-world data analysis workflows:

• Cleaning and preparing datasets
• Performing statistical and exploratory analysis
• Creating meaningful dashboards and reports
• Using multiple tools across the analytics stack

Each task reflects a different aspect of the data analytics lifecycle, from raw data processing to business decision-making support.