Terminal-Based Data Analytics Pipeline (Python & Bash)

📌 Project Overview (For Recruiters)

This project demonstrates how I build a real-world, terminal-driven data analytics pipeline using Python and Bash.

The goal is to simulate an end-to-end analytics workflow where raw country-level socio‑economic data is:

• extracted,
• cleaned and transformed (ETL),
• analyzed using Python,
• and exported as analytics-ready outputs for BI tools such as Tableau or Power BI.

This project focuses on practical data analyst skills, not just theory.

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🧠 Problem Statement

Raw global development datasets often come in messy formats and are not directly usable for analysis or dashboards.

This project solves that problem by:

• automating data cleaning and transformation,
• generating summary statistics and visual insights,
• and producing clean CSV outputs ready for reporting and visualization.

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🛠 Skills Demonstrated

• Python (Pandas, NumPy, Matplotlib)
• Bash / Shell scripting
• ETL pipeline design (Extract → Transform → Load)
• Data cleaning & preprocessing
• Exploratory Data Analysis (EDA)
• Terminal-based automation
• Analytics-ready data preparation for Tableau

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📂 Project Structure

terminal_practice_project1/
│
├── run_analysis.sh        # One-command pipeline execution
├── etl_pipeline.py        # Data extraction, cleaning, transformation
├── analysis.py            # EDA and visualization logic
├── output/                # Cleaned datasets and plots
├── README.md
└── .gitignore

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▶️ How to Run the Project

Clone the repository and run the pipeline from the terminal:

git clone https://github.com/aswathappaswetha-tech/terminal_practice_project1.git
cd terminal_practice_project1
bash run_analysis.sh

This single command:

1. Loads the raw dataset
2. Cleans and transforms the data
3. Performs analysis
4. Saves cleaned CSV files and visualizations

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📊 Outputs / Results

• Cleaned, analytics-ready CSV files

• Summary statistics of socio-economic indicators

• Visualizations such as:

  • GDP vs Life Expectancy
  • Country-level comparisons

📈 Interactive Dashboard (Tableau)

An interactive Tableau dashboard was built using the cleaned output from this pipeline.

🔗 View the dashboard here:
Global Development & Health Insights Dashboard

Dashboard Highlights:

• GDP vs Life Expectancy analysis
• Child Mortality comparison across countries
• Health & economic development insights
• Interactive filtering by country and indicators

📌 The dashboard uses CSV outputs generated directly from this ETL pipeline.

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🚀 Why This Project Matters

This project reflects how data analysts work in real environments:

• using the terminal,
• automating workflows,
• and preparing data for decision-making tools.

It demonstrates my ability to move from raw data → insights → business-ready outputs.

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👩‍💻 Author

Swetha Gowribidanur Aswathappa MSc Data Analytics | Python | SQL | ETL | Data Visualization Berlin, Germany

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📎 Future Improvements

• Add logging and error handling
• Parameterize input datasets
• Extend analysis with clustering or regression models
• Connect pipeline directly to Tableau extracts