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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