This folder contains works samples of data science and analytics with:

SQL: The SQL scripts I've provided are a testament to my advanced skills in the language, employing techniques like Common Table Expressions (CTEs), conditional logic, and data aggregation. These abilities are pivotal in my methodical analysis and reporting on pharmacy claims data, reflecting a deep understanding of both SQL command language and the nuances of healthcare data.

Python: My repository includes Python projects ranging from the automation of file zipping processes to advanced applications such as sentiment analysis using Google API, illustrating my capacity to employ Python for both practical automation and complex data analysis. Additionally, my code samples for data preprocessing to detect outliers in a dataset highlight my meticulous approach to data integrity and analysis.

SAS: In my SAS Enterprise 9.4 projects, I have developed a dynamic chart chaselist for medical providers, showcasing my adaptability with ad hoc analyses. I have also demonstrated my technical expertise in creating robust macros and custom formats within base SAS. Furthermore, my ability to streamline processes is evident in my sample project that exports a proc report directly into Excel, underscoring my proficiency in bridging SAS data analysis with accessible reporting formats.

SAS Viya: Through SAS Visual Analytics and SAS Model Studio in SAS Viya, I've engineered and improved a neural network model tailored to predict employee job satisfaction. My focus was on fine-tuning the model's accuracy by minimizing the Average Squared Error (ASE), which was a significant contribution to an HR consultancy's efforts in predicting job satisfaction trends in a global employee dataset.

Excel: I utilize Excel to its full potential by applying the Solver add-in for prescriptive analytics across various scenarios, including assignment variables, supply chain, and travel models. My proficiency with PivotTables and PivotCharts further enables me to transform complex datasets into insightful and visually appealing reports.

KNIME: A significant project in my portfolio is based on the KNIME platform, where I utilized four distinct predictive models to analyze and identify the key factors that influence customer churn. This project not only demonstrates my analytical skills but also my ability to leverage data mining tools to inform business strategy.

This summary encapsulates my diverse skill set across multiple platforms and programming languages, emphasizing a strong foundation in data manipulation, analysis, and predictive modeling, which are critical in making data-driven decisions and strategic insights.