Cristiane Giacomazzi

Healthcare Data Analyst | Data Analyst

Bridging Clinical Insight & Analytics

Transforming data into better heathcare systems

R • Python • SQL • Power BI | Tableau

Health Data Analyst with expertise in statistics and data literacy, specializing in bridging technical insights with actionable business strategies.

Leveraging clinical experience as a licensed Physiotherapist in the healthcare industry, I excel in problem-solving in in high-pressure environments and driving efficiencies through data-driven decisions.

My core strengths include time management, prioritization, and the ability to communicate complex concepts clearly and concisely. Proficient in advanced data analysis techniques, I am eager to contribute meaningfully to business and data analysis roles.

Projects

PK/PD Analytics Plot & LOESS curve

In clinical development, translating complex longitudinal data into actionable insights is essential for assessing a drug's safety, efficacy, and therapeutic viability. This project focuses on the visual re-engineering and analysis of Pharmacokinetic (PK) and Pharmacodynamic (PD) Longitudinal Profiles to effectively communicate population trends, data variability, and target attainment over time. Using advanced data visualization techniques in R, this project provides a unified, scannable framework that bridges the gap between complex statistical outputs and non-technical stakeholder decision-making.

To see the complete analysis, use the following link [GitHub]

🧰 R - Statistics - Executive communication

Survival-Analysis-In-R

Clinical Data Re-Engineering for Executive Stakeholders. The goal of this project was to bridge the gap between complex survival analysis and business intelligence. Standard Kaplan-Meier plots in R are often cluttered with academic features—such as wide, overlapping confidence intervals, excessive time horizons, and statistical jargon—that hinder rapid decision-making in a business scenario. By stripping away visual noise, optimizing the axis bounds, and translating statistical parameters into clear business metrics (such as a headline-driven median survival takeaway), I transformed a raw scientific graphic into an executive-ready visual asset without sacrificing data integrity.

To see the complete analysis, use the following link [GitHub]

🧰 R - Statistics - Executive communication

Visual Re-Engineering of Indomethacin PK Profiles in R

➡️ The purpose of this project was to bridge the gap between technical data generation and business intelligence, transforming raw statistical output into a highly communicative visualization that enhances stakeholder data literacy and drives operational decisions.

To see the complete analysis, use the following link [Github]

🧰 R - Executive communication

Canadian Pharmaceutical Trends

➡️ Produced an exploratory analysis of pharmaceutical market behavior between 2024 and 2025 using data from the Government of Canada.

➡️ Applied data preprocessing, feature engineering, and exploratory time-series analysis techniques to prepare and analyze Canadian pharmaceutical trade data, including the creation of derived metrics and validation of data quality issues affecting statistical measures.

➡️ Developed a comprehensive analytical framework combining descriptive statistical modeling, time-series analysis, ranking methodologies, and geospatial visualization to evaluate pharmaceutical trade patterns, including year-over-year growth, market share dynamics, and distributional behavior.

➡️ This analysis supports strategic and operational decision-making by identifying high-growth pharmaceutical categories, evaluating trade partner performance, and benchmarking market behavior through time-series and distributional analysis, enabling stakeholders to better allocate resources, mitigate risks, and capitalize on emerging opportunities.

To see the complete analysis, send me an email or use the following link [Github]

🧰 R - Technical Report

Functional Outcomes in Patients with and without ECMO: a 24 Month Follow-up Study

➡️ Conducted a clinical outcomes study comparing 47 COVID-19 patients with and without high-cost treatment Extracorporeal Membrane Oxigenation (ECMO), analyzing functional recovery over 24 months using the Barthel Scale.

➡️ Applied statistical methods (Stuart-Maxwell Test) in R by DescTools, tidyr, ggplot2 (and others) to evaluate long-term functional limitations, with findings emphasizing persistent impairments in mobility and stair climbing among ECMO patients.

➡️ Recommended for patient journey.

➡️ Presented to stakeholders and recommended physiotherapy rehabilitation as a critical intervention for post-ECMO recovery, contributing to evidence-based strategies for improving patient care and informing future research (e.g., lung transplant cases).

🧰 R - Statistical Report - Clinical Studies - Tecnhical Report - Stakeholder Presentation

Sales Dashboard

Dashboard directly addresses key business questions, enabling stakeholders to

➡️ Understand the company's growth and performance trends over the years.

➡️ Identify and optimize the product portfolio by highlighting underperforming products and strategic focus areas.

➡️ Recognize top-performing institutions by sales importance.

➡️ Analyse gaps and investigate opportunities.

To see the complete analysis, use the following link [click here to read]

🧰 Python - Tableau - Looker Studio

Finding gaps and Opportunities

Evaluating the results of a Pilot Program can be an opportunity to investigate the "paint points" and discover opportunities of improvement.

➡️ Dataset created by research in the real-world data.

➡️ The complaints related with high-cost services should be investigated thoroughly.

To see the complete analysis, use the following link [Github] [Dashboard]

🧰 Python - Looker Studio

Market Analysis

In collaboration with Data Analyst Bruno de Fraga, I conducted a Market Analysis for a nonprofit organization in Canada, focusing on the health and social services sector. Here is the general market analysis, highlighting key economic indicators, opportunities, potential partners, and challenges within the industry. Our approach involved researching relevant information, gathering and analyzing data, and presenting insights to the client to showcase investment opportunities in this sector.

🧰 Infographic - Executive Communication

Effectiveness of a New Training Program

Assessing the Effectiveness of a New Training Program on Employee Performance: Conducted an A/B test (t-test) to evaluate the impact of a new training program on employee performance in a large dataset. The analysis prompting a recommendation to review the investment and conduct further investigation to identify underlying causes. Data visualization integrating Python and Power BI.

[click here to see] [Github]

🧰 Python - Power BI

Intensive Care Unit Analysis KPIs

Performed descriptive analysis and created Excel dashboards, allowing for improved operational decisions in an Intensive Care Unit, collaborating with stakeholders, particularly in public health setting.

[click here to see]

🧰 Excel - KPI

Time Series

Descriptive retrospective study of time series conducted from 2013 to 2023 in 30 health regions in Southern Brazil, using large databases from the Unified Health System in Python using Pandas, Matplotlib, and Seaborn libraries. Analyzed burn policy documents to relate with patient outcomes and accessibility to care in underserved communities. Demonstrating the most prevalent burn due to exposure to electric current radiation providing insights for data-driven decision making for health authorities.

[Github] [Scientific publication access]

🧰 Excel - Python - Data Visualization

Pharmaceutical Sentiment Analysis

Utilized NLP in Python to perform a sentiment analysis on 4143 reviews for 541 drug brands.

Identified 10 drugs with negative reviews (91% accuracy, precision 0.91 and recall 0.91).

Data visualization using Pandas to produce actionable insights to stakeholders.

Employing SWOT analysis which informed key decision-making in pharmaceutical processes.

🧰 ML - NLP - Pandas - SWOT Analysis

X-Ray Classification

Developed a deep learning CNN model in Matplotlib and Python for X-ray classification of three patterns, achieving 96% accuracy, surpassing the diagnostic accuracy of human radiologists, creating financial impact in radiology field.

[click here to see] [Github]

Customer Segmentation

Implemented K-means clustering in Power BI integrated with Python, improving customer targeting strategies, identifying the high-value customer groups and providing strategic tailor strategies for marketing departments.

[click here to see] [Github]

Handle datasets

Conducted exploratory data analysis and handled dataset imbalance using advanced sampling techniques (Oversampling, Undersampling, and Synthetic Minority Oversampling Technique - SMOTE) in Phyton. Data visualization using Matplotlib, Pandas, and Seaborn.

[click here to see] [Github]

LinkedIn metrics and key audience engagement indicators

Analyzed LinkedIn metrics and key audience engagement indicators (impressions and reach) using Excel to assess the impact of a project initiative. Presented findings creatively to stakeholders and the marketing team, delivering actionable insights and strategic recommendations. Leveraged SWOT analysis to support data-driven decision-making, mitigate risks, and optimize future initiatives for greater success.

[click here to see]

Churn Analysis

Performed an exploratory data analysis using Python to identify customers with a higher likelihood of churning.

Leveraged SWOT analysis to support data-driven decision-making and communicated the call-to-action graphically for stakeholders.

Data visualization using Tableau.

[Github] [click here to see]

Get in touch at cristianegiacomazzi5@hotmail.com

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