Real consulting deliverables — an exploratory data analysis report and a full machine learning pipeline — showing exactly what a client receives when they work with STEM-azing Scientific Consulting. Every analysis below was designed, coded, and written personally by Dr. Delva.
The independent practice of Nella C. Delva, PhD — a Fulbright Research Fellow and biomedical scientist based in Berlin, Germany. The practice specializes in the intersection of rigorous life science expertise and applied data analytics, offering consulting, analysis, and writing services to academic researchers, clinical teams, and biotech organizations worldwide. Every deliverable is produced personally by Dr. Delva — not delegated, not templated.
Full descriptions and pricing live on the Services page — here's the shorthand.
EDA, statistical testing, publication-quality figures, Python/R pipelines.
Classification & regression, feature engineering, model validation, biological ML pipelines.
Manuscript development, grant proposals (DFG, ERC, NIH), research reports.
Experimental design strategy, hypothesis development, methods selection.
Doctoral & postdoc coaching, fellowship application prep, career transition support.
BI dashboards, GA4/GTM analytics reporting, SQL/BigQuery pipelines, data storytelling.
Entry-level engagement
Mid-tier engagement
Full-scope engagement
Sleep Quality & Stress Biomarker Dataset · N = 180 Participants
An exploratory analysis of a participant health dataset examining the relationship between sleep duration, stress levels, and intervention group assignment. The client required a rigorous statistical summary, publication-quality visualizations, and an interpretation grounded in current sleep science literature.
Research Questions
| Service Tier | ★ Starter |
| Dataset Size | N = 180 participants |
| Variables | Sleep duration, Stress score, Group |
| Analysis Type | Exploratory / Descriptive |
| Language | Python (Jupyter) |
| Deliverables | Report + Code + 3 Figures |
Tools & Methods
| Variable | Value | p-value | Interpretation |
|---|---|---|---|
| Mean Sleep Duration | 6.8 hrs | — | Below NSF recommended 7–9 hrs for adults |
| Mean Stress Score | 5.2 / 10 | — | Moderate stress level across full sample |
| Sleep–Stress r | r = −0.43 | < 0.001 | Significant negative correlation |
| Intervention vs. Control Sleep | +0.8 hrs | < 0.05 | Intervention group shows modestly higher sleep |
| Cohen's d (group effect) | d = 0.38 | — | Small to moderate effect size |
| % < 6 hrs sleep/night | 38% | — | Below minimum recommended threshold |
The data provides clear evidence of a stress-sleep relationship in this sample. The intervention appears to improve sleep duration modestly, but the small effect size and heterogeneous response suggest that not all participants are equally responsive as designed. The 38% prevalence of severely restricted sleep indicates a meaningful public health burden within this cohort.
Sleep Disorder Risk Classification · N = 300 Participants · 7-Feature Biomarker Dataset
Building a full machine learning pipeline to predict sleep disorder risk from a multi-modal biomarker and lifestyle dataset. Three classification models were developed, compared, and validated. Feature importance and partial dependence plots connected statistical findings back to the underlying neuroscience and physiology.
Research Questions
| Service Tier | ★★★ Advanced |
| Dataset Size | N = 300 participants |
| Features | Sleep, Stress, Age, Cortisol, BDNF, CRP, IL-6 |
| Target | Sleep Disorder Risk (High vs. Low) |
| Models Built | 3 (Logistic Regression, Gradient Boosting, Random Forest) |
| Validation | 5-fold CV + held-out test set |
| Deliverables | Report + Code + 6 Figures + CSVs + 2 Revisions |
| Model | AUC-ROC | Accuracy | CV Score | Recommendation |
|---|---|---|---|---|
| Logistic Regression (baseline) | 0.791 | 72.3% | 0.784 ± 0.04 | Good baseline; interpretable coefficients |
| Gradient Boosting | 0.834 | 76.7% | 0.826 ± 0.03 | Strong performance; handles non-linearity |
| ★ Random Forest (selected) | 0.847 | 78.0% | 0.839 ± 0.03 | Best performer; selected as final model |
Sleep duration is the primary protective factor — chronic restriction is a leading risk factor for sleep disorders. Stress score reflects HPA axis activation disrupting sleep architecture via cortisol elevation; cortisol, BDNF, CRP, and IL-6 add independent biological signal linking inflammation and neuroprotection to sleep outcomes.
Whether you need a rapid exploratory report or a full predictive modeling pipeline, engagements are scoped to match your data, your timeline, and your budget.