A data visualization and analysis project using Excel and Power BI to uncover trends and insights in key populations and services for Conavihsida's Departamento de Poblaciones Clave.
This project focuses on analyzing and visualizing data related to key populations and services offered by the Departamento de Poblaciones Clave del Conavihsida.
The goal was to extract actionable insights from daily referrals and interventions, streamline data for better reporting, and provide dashboards for informed decision-making.
- Provide clear visualizations of service trends over time.
- Identify the most utilized services and target demographics.
- Deliver actionable insights to optimize resource allocation and interventions.
The project utilizes departmental records documenting services provided to populations such as transgender individuals, sex workers, and drug users. Services analyzed include emotional support, medication assistance, and clinical referrals.
- Excel: Data cleaning, transformation, and validation.
- Power BI: Data modeling, advanced visualizations, and storytelling.
- Data Cleaning: Techniques like deduplication, column standardization, and error handling.
- Attention to Detail: Ensuring data accuracy and quality.
- Problem Solving: Designing intuitive and actionable dashboards.
- Communication: Translating complex data into accessible visual narratives.
The dataset originated from department logs and referral records related to interventions for key populations.
In Excel, the following transformations were applied:
- Removed duplicates.
- Standardized column names for consistency.
- Validated numerical values and addressed missing data.
- Restructured the dataset (e.g., unpivoting service metrics) for Power BI integration.
Goal: To create an interactive dashboard answering key analytical questions:
- What are the trends in service provision over time?
- Which population groups are the most attended?
- What is the gender breakdown of interventions?
- Which services are utilized the most?
- Which SAIs received the highest referrals?
- Data Entry & Cleaning: Preprocessed the dataset in Excel for structure and accuracy.
- Data Modeling & Visualization: Imported cleaned data into Power BI to design interactive dashboards.
- Insight Generation: Delivered actionable visualizations tailored for decision-making.
- Service Trends: A decline in some services over the years suggests changing population needs or could indicate positive developments, such as improved awareness among key populations, better management of HIV cases, or more consistent visits to SAIs (Servicios de Atención Integral).
- Gender Analysis: 70.59% of services were provided to males, while 29.41% were provided to females.
- Target Populations: Heterosexual individuals represented the most attended population.
- Top Service Areas: Identified the "Top 7 SAIs" with the highest referral counts.
The project culminated in a Power BI dashboard providing key insights:
- Trend Analysis: Visualized service trends from 2021 to 2024.
- Top Populations and Services: Highlighted the highest-demand populations and services.
- Gender Distribution: Displayed the proportion of males and females served.
- Resource Allocation: Pinpointed referral concentrations to optimize strategies.
- Excel: Data cleaning, transformation, and organization.
- Power BI: Data modeling, visualization, and dashboard creation.
This project demonstrates my ability to:
- Clean and structure complex datasets.
- Extract actionable insights using advanced visualizations.
- Communicate findings through storytelling dashboards.