Efficiency and Diversity of R&D
in Knowledge-Intensive Services (2005–2023)

🔶 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.

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

• Figures: a repository of all programmatically generated visualizations used to drive the data narrative.

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

• Full HTML Reports: a comprehensive, rendered versions of the analysis, including all code, statistical interpretations, and granular findings;

• Interactive Notebooks: the original Jupyter environment used for iterative development and data exploration;

• Clean Code: production-ready, concise .py versions of the analytical logic, stripped of notebook metadata for better readability.

✳️ 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

Strategic evaluation of how efficiently selected European countries convert R&D spending into researcher human capital within the knowledge-intensive services sector (NACE G–N). I inspect the available data to assess whether increasing female participation in researcher roles correlates with improved spending efficiency and labour dynamics.

〽️ Practical business question: Does investing in a more gender-diverse R&D workforce help or hinder the efficient use of R&D budgets in knowledge-intensive services?

What this project delivers:

• A reproducible, well-documented merged datasets ready for dashboards or further analyses.

• Re-usable scraper and analysis code that can refresh results when source datasets are updated.

• Clear visualizations of time trends, CAGRs, and female-share vs efficiency correlations.

• 〽️ Business-relevant insights for stakeholders interested in R&D investment, workforce planning, and diversity strategies.

Objectives

• Prepare a clean, merged panel dataset from Eurostat/DBnomics sources (2005–2023) for NACE G–N.
  Code: scraper_code.ipynb / scraper_code.py

• Load prepared datasets, filter and rename variables, review NaNs and missing data.
  Code: analysis_code.ipynb / analysis_code.py

• Analyse sector's spending efficiency and labour intensity across selected countries.
  Code: analysis_code.ipynb / analysis_code.py

• Analyse participation of female researchers and its relationship to the sector's growth rates.
  Code: analysis_code.ipynb / analysis_code.py

• Compare trends, CAGRs and correlations across countries to understand how female inclusion relates to spending efficiency and labour intensity.
  Code: analysis_code.ipynb / analysis_code.py

Data & Source Metadata

External data sources (Eurostat / DBnomics):

• htec_sti_exp2 dataset — R&D expenditure in high-tech sectors (Eurostat)
• htec_sti_pers2 dataset — R&D personnel in high-tech sectors (Eurostat)
• rd_p_bempoccr2 dataset — R&D personnel and researchers by activity and sex (DBnomics / Eurostat)

Key variables

• Year: 2005–2023
• GDE Euro: R&D expenditure (millions €) in NACE G–N sector
• FTE All: number of full-time positions in NACE G–N sector
• FTE Researcher: number of full-time researcher positions in NACE G–N sector
• FTE Researcher Fem: number of full-time researcher positions held by women in NACE G–N sector
• Countries (final selection): Bulgaria, Czechia, Estonia, Croatia, Hungary, Italy, Poland, Portugal, Slovakia
  ❕ Each of those countries shows >80% data entry completeness for the chosen metrics.

Tools & Methods

Programming & Analysis: Python {pandas, NumPy, matplotlib, seaborn, scipy}

Web scraping & metadata extraction: Python {BeautifulSoup, selenium}

Documentation & Reporting: Markdown, HTML syntax, Jupyter Notebooks.

Version control & sharing: Git & GitHub

Analytics performed: time-series plots, scatterplots, barplots, CAGR calculations, correlation analysis.

🔶 Examples of programming solutions

Data cleaning: clarify variable names and units.

df['time'] = df['time'].astype('int32')
df['Year'] = pd.to_datetime(df['time'], format = '%Y', errors = 'coerce')
print(f"Year: {df.time.isnull().sum()*100/len(df.time):.0f}% of entries were coerced to NaT.")

Diagnosing missing data: compute descriptive statistics, visualize coverage and gaps.

nan_count = df.isna().sum()
all_count = df.iloc[:,0].count()
prc = (nan_count * 100)/all_count
print('The percentage of NaN values per column:')
print(prc.apply(lambda x: x).apply("{:,.0f} %".format))

Computing additional metrics: calculate spending efficiency.

efficiency_calc = df.groupby(['Country', 'Year'])[['GDE Euro','FTE Researcher']].apply(
    lambda x: pd.Series({
        'SpendEff': x['GDE Euro'].sum() / x['FTE Researcher'].sum() 
            if not (x['GDE Euro'].isna().any() or x['FTE Researcher'].isna().any()) 
            else float('nan')})).reset_index()
df = df.merge(efficiency_calc, on=['Country', 'Year'], how = 'left')
print(f"SpendEff: {df['SpendEff'].isnull().sum()*100/len(df['SpendEff']):.0f}% of entries were coerced to NaN.")

Visualising results: ploting spending efficiency per country and across years.

g = sns.relplot(kind = 'line', x = 'Year', y = 'SpendEff', data = df, hue = 'Country', errorbar = None)
sns.move_legend(g, "upper right", bbox_to_anchor = (0.975, 0.95), ncol = 1)
g.set(title = 'Annual Spending Efficiency per a Researcher (FTE)', 
      xlabel = "Calendar year", ylabel = "Spending Efficiency [MIO € / 1 Reearcher FTE]")
g.fig.set_size_inches(10,4)
plt.savefig('../figures/Fig2.1 Annual Spending Efficiency per a Researcher FTE.png')

Documenting: providing merged dataset for future updates.

data = pd.merge(data_pers2_wide, data_exp2_wide, on = ['nace_r2', 'geo', 'time'], how = 'left') 
data = pd.merge(data, data_fem2, on = ['nace_r2', 'geo', 'time'], how = 'left')
data.to_csv('../data/scraper_data.csv', encoding='utf-8', index = False)
print('Merged dataset was saved into \'../data/scraper_data.csv\'.')

🔶 Limitations & Challenges

• Data limits: substantial missing data before 2008 and after 2022; metric comparability is best between 2009 and 2021.

• Country selection: only 9 countries (with >80% completeness) were analysed — results are not a full EU/EFTA comparison.

• Sector heterogeneity: NACE G–N covers multiple service subsectors that can influence spending and hiring patterns.

• Correlation: correlations do not imply causation. Observed links between female share and efficiency may reflect confounders (economic structure, policy, funding cycles).
  

❕ Offered insights are not based on a structured academic knowledge about EU policy or geo-political structure of discussed countries. My aim was to simply demonstrate my ability to understand complex datasets, data characteristics and measures that are outside my primary academic expertise.

🔶 Key findings

General trends: Most countries showed increasing R&D spending efficiency and growing researcher workforces (2005–2023). Full-time research positions held by women increased faster than overall researcher growth in all analysed countries.
〽️ Business Insight: countries can be categorised as balanced, inclusion-phase, or efficiency-first.

• Balanced growth: Poland and Slovakia increased spending efficiency, female share, and workforce size concurrently.
  〽️ Business Insight: diversity initiatives combined with targeted funding and organizational change could result in a stable growth.

• Initial investment: Italy, Portugal, and Croatia show rising female participation together with stagnant or declining spending efficiency — consistent with potential initial integration costs (recruitment, training).
  〽️ Business Insight: gender-diversity investments may increase short-term costs but can bring long-term efficiency benefits.

• Efficiency prioritization: Bulgaria, Czechia, and Estonia improved spending efficiency but presented declines in female share.
  〽️ Business Insight: without corresponding diversity policies, improved spending efficiency may have favored male-dominated recruitment or sub-sectors.

🔶 Presented skills

Data Wrangling & Engineering

• Developing custom web scrapers (BeautifulSoup, selenium) to automate the extraction of public datasets and metadata.
• Preparing and cleaning panel datasets using pandas and NumPy (handling missing values, datetime coercion, standardizing units).
• Executing complex data merging and restructuring across multiple external sources (Eurostat, DBnomics) to create a unified analytical foundation.

Statistical Analysis & Modelling

• Performing time-series analysis to track macro-level sector growth and workforce dynamics from 2005 to 2023.
• Calculating advanced performance metrics, including CAGR (Compound Annual Growth Rate) and custom spending efficiency indices.
• Conducting correlation analysis to evaluate the statistical relationship between demographic changes (female workforce participation) and financial efficiency.

Data Visualization & Storytelling

• Designing programmatic visualizations (matplotlib, seaborn) such as multi-variable time-series plots, scatterplots, and barplots.
• Translating abstract statistical findings into actionable business insights (e.g., categorizing country-level behaviors into "Balanced growth" or "Initial investment" phases).
• Documenting strict methodological limitations (e.g., distinguishing correlation from causation, addressing missing data gaps) to ensure data integrity and transparent reporting.

🔶 Portfolio Gallery

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