Progress 1

In the initial phase, data loading, transformation, and cleaning were conducted to support Biogenesys' strategic expansion into Latin America.

1. Summary of Progress

The following tasks were completed:

• Analyzed Readme.txt to understand dataset column descriptions.
• Created a notebook (PIDA_M4_Nombre_Apellido.ipynb) and imported libraries (Pandas, Numpy, Matplotlib).
• Loaded data_latinoamerica.csv and verified data integrity.
• Selected key countries: Colombia, Argentina, Chile, Mexico, Peru, and Brazil.
• Filtered data to include entries post-January 1, 2021.
• Addressed missing values by country comparison.
• Conducted preliminary data cleaning.
• Saved filtered data to CSV for future use.
• Calculated descriptive statistics for each dataset column.

2. Metric Analysis

Implications: Calculated metrics reveal insights into the distribution and behavior of dataset variables, supporting understanding of central tendency, variability, dispersion, and data shape, which are essential for further analysis.

Conclusion

This initial phase laid the groundwork by preparing and cleaning data critical for Biogenesys' expansion strategy in Latin America.

Analysis of Measures 📉

• Median: Represents the central value in an ordered dataset, crucial for identifying central tendency without the influence of outliers.
• Variance and Range: High variance indicates data dispersion; a high range suggests significant variation among data values.

Interpretation: The combined analysis of median, variance, and range helps assess data consistency and variability.

Extra Credit: Higher-Order Functions for Efficient Data Manipulation

High-order functions enhance flexibility and efficiency in data processing, allowing for more concise, reusable, and efficient code.

Example: The calcular_tasa_incidencia(casos, poblacion) function calculates and visualizes COVID-19 incidence rates, helping assess relative pandemic impacts across countries.

Progress 2

Exploratory Data Analysis and Visualization

This phase involved data exploration and visualization related to COVID-19 incidence, vaccination rates, and healthcare infrastructure. Key tasks included:

• Data manipulation and statistical analysis with Pandas and Numpy.
• Creation of visualizations (density histograms, bar charts, heatmaps, scatter plots) with Matplotlib and Seaborn to highlight patterns, trends, and anomalies

• Median: Represents the central value in an ordered dataset, crucial for identifying central tendency without the influence of outliers.
• Variance and Range: High variance indicates data dispersion; a high range suggests significant variation among data values.

Progress 3

Detailed EDA with Numpy and Pandas

An advanced exploratory data analysis (EDA) was conducted to refine and prepare data for visualization, focusing on COVID-19 incidence patterns. Key steps included:

• Data pre-processing (handling missing values, date conversion).
• Temporal analysis (identifying active, recovered, confirmed case trends).
• Analysis of seasonality and autocorrelation in confirmed cases.

Results: EDA revealed patterns and correlations, essential for accurate forecasting and strategic site selection.

Recommendations: Continue advanced analysis, including spatial and temporal visualization, for strategic insights.

Progress 4

Practical Applications - Power BI Integration

The final phase focuses on integrating and presenting analytical findings in Power BI. Dashboards and interactive reports were created to facilitate decision-making for laboratory expansion and vaccination center placement.

Methodology:

• Imported pre-processed data into Power BI for visualization.
• Designed interactive dashboards to effectively communicate data insights.

Conclusions: This project provided a robust foundation for Biogenesys' strategic expansion by leveraging data-driven insights into COVID-19 impact, healthcare capacity, and population vulnerability.

Conlusion

Optimal Location Analysis for Pharmaceutical Laboratories in Latin America

Based on the analysis of the dataset, key factors were identified to determine the optimal location for establishing pharmaceutical laboratories in Latin America. These factors include:

• Confirmed COVID-19 cases
• Confirmed deaths
• Total population
• Population density
• Number of doctors and nurses per 1,000 inhabitants
• Mortality rate
• Vaccine doses administered

The findings highlight Brazil as the optimal country for constructing the laboratories, as it exhibits the highest values in the following indicators:

• Confirmed COVID-19 cases
• Vaccine doses administered
• Total population
• Confirmed deaths
• Nurses per 1,000 inhabitants
• Highest percentage of rural population

Additionally, Brazil shows relatively low values in:

• Doctors per 1,000 inhabitants

These results underscore Brazil's suitability as the ideal location for the laboratories, enabling a targeted approach to regions with high case density and pressing healthcare infrastructure needs.