Internship Tasks – Data Analytics (Code Sentinel)

This repository contains my completed internship tasks under the Code Sentinel Internship Program. The goal of these tasks was to build hands-on skills in Python, Pandas, Data Visualization, and Business Intelligence tools by working with real-world datasets.

Tasks Completed

Task 1 – Load and Explore Data

• Loaded the Titanic dataset (titanic_data.csv) using Pandas.
• Explored dataset structure: number of rows, columns, data types, and summary statistics.
• Identified missing values and initial data quality issues.

Task 2 – Clean and Preprocess Data

• Handled missing values in key columns (Age, Embarked, Cabin).
• Removed duplicate entries where necessary.
• Converted categorical values into consistent formats.
• Saved a cleaned dataset (titanic_data_cleaned.csv) for reproducibility.

Task 3 – Visualize Key Insights

• Used Matplotlib and Seaborn to create visualizations:
  • Bar plots: Survival counts by gender and passenger class.
  • Histograms: Age distribution of passengers.
  • Pie charts: Survivors vs. non-survivors.
  • Boxplots: Relationship between fare and passenger class.
• Key insights: higher survival among females and 1st-class passengers.

Task 4 – Analyze Sales Data Using Grouping

• Used the Superstore dataset (superstore.csv) to perform grouping and aggregation:
  • Total Sales by Region
  • Average Profit by Category
  • Order Count by Ship Mode
  • Sales & Profit by Sub-Category
  • Sales by Region and Category
• Applied Pandas groupby + agg for summarization.
• Created multiple Seaborn bar plots for visualization:
  • Regional sales comparison
  • Average profit per category
  • Order count by shipping mode
  • Sales vs. profit across sub-categories
  • Regional sales breakdown by category

Tools Used: Pandas, Seaborn, Matplotlib

Task 5 – Create an Interactive Dashboard

• Imported data into Power BI.
• Designed an interactive dashboard featuring:
  • Slicers (Quarter, Region, Category)
  • KPIs (Total Sales, Avg Sales, Total Profit)
  • Dynamic charts (bar, line, and pie charts)
• Enabled real-time filtering for business-friendly analysis.

Tools Used: Power BI

Key Learnings

• Hands-on experience in data cleaning, preprocessing, visualization, and grouping using Python.
• Practical knowledge of business reporting through Power BI dashboards.
• Improved ability to make projects structured, reproducible, and professional.

📂 Repository Structure

├── Task_1_to_3/
│   ├── titanic_data.csv
│   ├── titanic_data_cleaned.csv
│   ├── task1_2_3.ipynb
│
├── Task_4/
│   ├── superstore.csv
│   ├── task4.ipynb
│
├── Task_5/
│   ├── sales_dashboard.pbix
│   └── screenshots/
│
└── README.md