📊 CBS Open-Data Engineering & Visualization Pipeline

📌 Project Overview

This repository contains the official final graduation assignment (eindopdracht) completed for the Junior Data Engineering Certification Program at Bit Academy. Project Metadata: Developed in June 2022 as part of the graduation program. Uploaded to GitHub in 2026 for portfolio curation.

The project retrieves performing-arts data from CBS StatLine through the cbsodata Python package. The retrieved records are converted into a pandas DataFrame, after which selected columns are renamed, filtered, aggregated and visualized.

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🎹 Professional Context & Background

As a classical pianist (MMus) with a deep-rooted interest in analytical research, I built this end-to-end analysis workflow to bridge the gap between complex statistical databases and domain-specific insights.

This project allowed me to practise retrieving public data programmatically and using pandas, Matplotlib and Seaborn to explore and communicate the results.

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🛠️ Tools and Techniques

The notebook uses the following tools and techniques:

• Data retrieval: cbsodata for retrieving CBS StatLine data.
• Data preparation: pandas for converting the retrieved records into a DataFrame, renaming columns, removing duplicate IDs, filtering rows and selecting relevant columns.
• Calculations: pandas operations such as groupby, sum, mean and percentage calculations.
• Data visualization: seaborn and matplotlib.pyplot for creating line, scatter, pie, box and bar charts.

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🚀 Main Features

1. API-based data retrieval: Retrieves CBS table 70077NED through the cbsodata package whenever the notebook is run.
2. Data preparation: Renames and selects relevant columns, removes duplicate IDs and filters national and regional categories.
3. Aggregation and calculations: Uses sums, averages and percentages to compare performances and visitor figures.
4. Exploratory visualization: Presents the results through several chart types created with Matplotlib and Seaborn.

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📦 Getting Started & Local Installation

To spin up this Jupyter Notebook workspace on your local machine, follow these technical steps:

1. Environment Setup

Clone this repository to your target directory:

git clone https://github.com
cd data-engineering-project-1

2. Dependency Management

Open the workspace in Visual Studio Code and select your active Python kernel. Execute the deployment script in your notebook or run the following command in your terminal to host the required packages:

pip install jupyter cbsodata pandas matplotlib seaborn

3. Execution

Open your .ipynb work file inside VS Code and select "Run All" from the top controls. The notebook will retrieve the CBS table and generate the analyses and visualizations when the cells are run in order.

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📄 License

This architecture is open-source software licensed under the terms of the MIT License.