I graduated in December 2024 with a Bachelor of Science in Data Science, concentrating in Mathematics, from the University of Texas at Arlington. My academic journey has equipped me with a strong foundation in statistics, data modeling, and the ability to uncover meaningful insights from complex datasets. I am passionate about transforming data into actionable strategies that drive growth and innovation.

I am currently expanding my knowledge through Coursera, where I am exploring advanced techniques in data visualization, customer behavior analysis, and the latest industry tools. This commitment to continuous learning helps me stay at the forefront of trends and equips me with the skills to deliver impactful insights.

As a detail-oriented and goal-driven individual, I thrive in collaborative environments that value creativity and problem-solving.

"There is nothing noble in being superior to your fellow man; true nobility is being superior to your former self."
— Ernest Hemingway

This philosophy inspires me to embrace growth opportunities, continually improve, and seek innovative ways to make a difference through data-driven decision-making.

I am currently taking the Microsoft Power BI Data Analyst course to prepare for the Microsoft Certified: Power BI Data Analyst Associate (PL-300) certification. This course is enhancing my skills in data visualization, report building, and business intelligence to deliver impactful insights and actionable solutions.

I plan to pursue the Microsoft Generative AI for Data Analysis Professional Certificate. This comprehensive program is designed to unlock the transformative power of AI in data analysis. It will empower me to leverage AI across the entire data lifecycle, streamlining processes and uncovering deeper insights crucial for career success. This certification aligns with my goal of staying at the cutting edge of data science and analytics.

• Customer Segmentation
  • Customer segmentation project leveraging K-Means, K-Modes, and Agglomerative Clustering to group customers by shopping behaviors, income, and demographics, optimizing store offerings and marketing strategies for enhanced sales and satisfaction.
• Employee Churn
  • This project predicts employee churn using five machine learning models: K-Nearest Neighbors (KNN), Random Forest, Naive Bayes, Logistic Regression, and a simple MLP Neural Network. By analyzing employee data such as satisfaction level, work hours, promotion history, and salary, the models classify whether an employee is likely to leave the company. After preprocessing and cleaning the dataset, we evaluated model performance using metrics like accuracy, precision, recall, and F1 score, achieving a 99% test accuracy with the best-performing model.
  • This project was a collaborative effort by Alyssa Juarez and Madison Nguyen and, Zehra Erden. Contributions included data preprocessing, model implementation, performance evaluation, and documentation.