euPOWERED: EU Renewable Energy Navigator

Strategic Analytics for Decarbonization, Economic Resilience, and Energy Security (2015–2024)

🔶 About Me

I am a Doctor of Neuroscience with strong experience in data analysis, statistical modelling and research design. I focus on translating complex data into actionable insights for business and policy. I enjoy data wrangling, visualization, and project management.

Skills & tools:

• advanced R, advanced Python (pandas, NumPy, matplotlib, seaborn, scipy),
• developing my skills in Power BI and Power Apps,
• developing my skills in SQL (ETL, PostgreSQL, pgAdmin4, DBeaver),
• comfortable managing AI-augmented workflow, leveraging Cursor IDE and Claude,
• technical documentation in Jupyter Notebook (Markdown syntax), version control in Git.

  Contact: dominika.a.drazyk@gmail.com
  LinkedIn: in/dominika-drazyk-otw95

🔶 Project Navigation

Select the path that best matches your interest:

1. Executive & Business Insight
For reviewers focused on storytelling, strategy, and end-results.

• ▶️ The euPOWERED Navigator: a ready-to-use interactive dashboard;

• PDF Presentation: a step-by-step walkthrough of the project’s assumptions, technical execution highlights, and business insights;

• Images: a repository of all screenshots from the dashboard used to present the capabilities of the Navigator.

2. Technical Deep-Dive & Audit
For reviewers interested in the full analytical process and data interpretation.

• Codes: production-ready data scraping, preprocessing and analysis scripts:
  • Scraper code: script for Eurostat datasets and metadata scraping;
  • Preprocessing code: script for preprocessing and a systematic missing-data audit;
  • Figures code: script generating styled, publication-ready figures;
  • Dashboard code: script rendering a three-page dashboard.

✳️ Dependency Management
This project uses Poetry to ensure a deterministic environment (locked versions) and 100% reproducibility. For basic users, a standard requirements.txt is also maintained.

• Option 1: Modern Workflow (Recommended) Use this if you have Poetry installed. This will automatically create a virtual environment and install the exact versions from poetry.lock.

# Bash
# Install dependencies and create virtual environment
poetry install
# Activate the environment
poetry shell

• Option 2: Standard Workflow (Pip) Use this for a traditional setup using the provided requirements.txt.

1. Initialize the Virtual Environment

# Linux / macOS
python3 -m venv .venv && source .venv/bin/activate
# Windows 
python -m venv .venv && .venv\Scripts\activate

2. Install Dependencies

# Bash
pip install --upgrade pip
pip install -r requirements.txt

🔶 Overview

An end-to-end data analytics project that scrapes, harmonizes, and visualizes a decade of EU energy statistics from Eurostat — then delivers the results through an interactive Streamlit dashboard built for lobbyists, policymakers, and energy analysts.

〽️ Practical business applications:

• Present the affordability argument, by correlating high renewable penetration with long-term price stability and lower net costs;
• Expose sectoral bottlenecks by identifying exactly where heating and transport systems fail to keep pace with the power grid;
• Optimize capital flow by pinpointing the “High Potential” markets where infrastructure investment will deliver the most significant economic and environmental ROI;
• Test national decarbonization performance, by direct, evidence-based comparisons between member states.

What this project delivers:

• A robust Python scraping and preprocessing pipeline ready for raw data imput and normalization.

• A set of well-documented codes for data visualization, ready for reporting.

• Ready-to-use and clear dashboard to support data-driven discussions and decisions.

• 〽️ Policy-relevant insights for lobbyists, policymakers and publicists interested in EU renewal energy sources transformation.

🔶 Objectives

1. Extract Eurostat datasets via the official JSON-stat API, scrape country-code mappings and dataset metadata, then harmonize and merge into a single wide CSV indexed by country and year.
   Code: scraper_code.py

2. Preproces the wide dataset, rename dimension codes to analyst-friendly labels, convert time formats, run a systematic missing-data audit with diagnostic visualizations, export the dashboard-ready CSV.
   Code: preproc_code.py

3. Return styled, publication-ready figures using a custom font, color palette, and shared matplotlib stylesheet.
   Code: figures.py

4. Drive a Streamlit application that renders six interactive analytical figures with per-figure country selection and metric toggles, a Data Model reference section with methodology and variable documentation, and an About page — all styled through a centralized CSS design system.
   Code: dashboard.py

AI-augumented Workflow

The project was built through an AI-augmented workflow, using the Cursor IDE and Claude. Data extraction, preprocessing, and analytical figure logic were written by the Author and optimized by Cursor. Presentation layer (Streamlit, CSS branding, HTML rendering, code refactoring) were implemented by Cursor under the following rules:

• Author-driven design decisions: All visual design choices (e.g, color palette, layout proportions, typography), content, and analytics (e.g, how to group RES Shares) originated from the Author;
• AI-assisted implementation: Cursor translated high-level instructions into working code, handling the CSS specificity with Streamlit's internal styles, HTML templating, and Matplotlib API details;
• Continuous review: The Author verified every change and provided feedback. The Author also made direct edits to the Streamlit code independently between Cursor sessions. Large structural improvements were first proposed by Cursor, then selectively approved or rejected by the Author before implementation.

Data & Source Metadata

External data sources (EUROSTAT):

• ten00124 Final energy consumption by sector: provides annual data on the energy end-use across industrial and residential segments;
• nrg_ind_ren Share of energy from renewable sources: official monitoring indicators for EU renewable energy targets, detailing penetration across specific economic sectors;
• nrg_pc_204 Electricity prices for household consumers (bi-annual): tracks the evolution of energy costs for the medium-sized consumer segment, including a breakdown of taxes and levies.

Key variables

Energy Consumption [ten00124]

• Consumption Industry nrg_bal: FC_IND_E: final energy consumption in the industrial sector;
• Consumption Transport nrg_bal: FC_TRA_E: final energy consumption in the transport sector;
• Consumption Households nrg_bal: FC_OTH_HH_E: final energy consumption by residential consumers.

Renewable Shares [nrg_ind_ren]

• REN Share nrg_bal: REN: overall share of energy from renewable sources;
• REN Share Transport nrg_bal: REN_TRA: share of renewables in the transport sector;
• REN Share Heat-Cool nrg_bal: REN_HEAT_CL: share of renewables in heating and cooling;
• REN Share Electricity nrg_bal: REN_ELC: share of renewables in the electricity sector.

Energy Pricing [nrg_pc_204]

• Price tax: X_TAX: electricity price excluding all taxes, levies, and VAT (net market price);
• Price+Tax tax: I_TAX: electricity price including all taxes, levies, and VAT (gross consumer price).

Tools & Methods

Programming & Analysis: Python {pandas, matplotlib, seaborn, pyjstat, BeautifulSoup, Selenium,Streamlit}, HTML/CSS;

Reporting: Streamlit interactive dashboard, custom Matplotlib stylesheet, branded CSS design system with custom properties;

Data Engineering: Eurostat JSON-stat REST API integration, web scraping, ETL pipeline;

Version control & sharing: Git & GitHub;

Analytics performed: systematic missing-data audit, trend analysis, correlation analysis, trailing 5-year boxplot with median thresholding, cross-national benchmarking.

🔶 Limitations & Challenges

• By design, the pipeline omits the Commercial and Public Services (nrg_bal: FC_OTH_CP_E). While the Industry, Transport, and Households cover the vast majority of consumption, the Total figures in this dashboard represent this specific subset, not the absolute national total;
• Pricing data is anchored to the DC Band from 2500 to 4999 kWh (nrg_cons: KWH2500-4999). While this represents the median EU household, it may not reflect the costs faced by low-income households or heavy industrial users who operate under different tariff structures;
• An “Outer Merge” strategy is used to accommodate countries with incomplete records for specific years. Consequently, some time-series or scatterplot clusters may appear fragmented for specific Member States.

❕ The insights provided are focused on the strategic application of energy metrics for policy advocacy and are not based on formal academic expertise in European energy economics or legislative drafting. My aim was to demonstrate the ability to architect data, design robust ETL processes, and extract actionable business intelligence from complex relational datasets.

🔶 Presented skills

Data Modelling & Engineering

• Developing custom web scrapers (BeautifulSoup, selenium) to automate the extraction of public datasets and metadata.
• Preparing and cleaning datasets using pandas and NumPy (synchronizing semi-annual series, mapping technical Eurostat codes to human-readable, policy-relevant labels, designing an "Outer Merge" data architecture).
• Executing systematic missing-data audits and diagnostic visualizations to maintain the structural integrity of the unified data model.
• Leveraging AI-augmented development (Cursor, Claude) to optimize the presentation layer while maintaining strict authorial oversight of analytical logic and visual proportions.

Data Visualization & Storytelling

• Imagining an interactive Streamlit dashboard with a custom-branded CSS design system and dedicated Matplotlib stylesheets for high-fidelity reporting.
• Translating abstract statistics into a strategic lobbyist narrative focused on economic affordability and decarbonization progress.
• Utilizing advanced visual analytics, including multidimensional bubble charts and percentile-based boxplots—to identify high-yield investment opportunities and sectoral bottlenecks.
• Preparing comprehensive documentation to effectively communicate methodology and limitations.

🔶 Portfolio Gallery

Project	Business Impact	Key Stack
euPOWERED Navigator	Policy-driven energy statistics & decarbonization tracking.	Python, Streamlit, Scraping
eCommerce Audit	Identifying financial risks and logistical bottlenecks.	PostgreSQL, ETL, Python
Consultant Dashboard	Market viability and competitive landscape engine.	Power BI, DAX, Star Schema
Efficiency and Diversity	Statistical research on human capital and spending.	Python, Scraping, Multivariate Stats

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