For my final capstone project (M.S. Advanced Data Analytics), I analyzed **Fall 2023 U.S. college enrollment data** (115K records, 5,900+ institutions) to uncover demographic patterns and predict graduate enrollment.  - **Key Findings:** - Women consistently outnumber men in enrollment, with the gap widening at the graduate level (60.6% vs. 39.4%). - Hispanic student representation drops sharply from undergrad (25.6%) to graduate (15.2%). - Institutional size distribution is highly skewed (median 588 vs. mean 3,332). - **Models Utilized: Linear Regression, Decision Trees, and Random Forest. - Best performer: Random Forest (R² ≈ 0.78, MAE ~631). - Strongest predictors of graduate enrollment: female enrollment and Asian student representation. 🔗 [View Full Repository on GitHub](https://github.com/saulzmtz/Analyze-Enrollment-Trends-and-Predict-College-Enrollment)

Demographic Factors and College Completion Tools:Python (pandas, scikit-learn, matplotlib), Jupyter Notebook This project analyzed 2022–2023 college degree completions across 16,000+ U.S. institutions** to uncover the strongest demographic predictors of success. Key Findings: - Female completions were the most influential factor. - Non-traditional students (ages 25–39) play a critical role in completions. - Random Forest achieved **~99% accuracy**, outperforming logistic regression and decision trees. [View Full Repository on GitHub](https://github.com/saulzmtz/Analyze-Demographic-Factors-and-Predict-College-Completion)

After completing my first two Information Science courses along with a [Crash Course in Data Science](https://coursera.org/share/2bbd48af32b446202fa7a4135f3e2ec2) from John Hopkins University, I decided to take two data analytics related courses. Now, after completing these two courses--[Data Analysis and Knowledge Discovery](https://s3.amazonaws.com/mirror.facultyinfo.unt.edu/joh0019%2Fschteach%2FINFO5810-001005%20Syllabus-1.pdf) and [Data Visualization and Communication](https://s3.amazonaws.com/mirror.facultyinfo.unt.edu/jy0282%2Fschteach%2FINFO5709_Fall2020-1.pdf) I have decided to end my brief journey in the Information Science graduate program to pursue a Master of Science in Advanced Data Analytics. A new journey begins!

I recently completed the [Data Visualization and Communication](https://s3.amazonaws.com/mirror.facultyinfo.unt.edu/jy0282%2Fschteach%2FINFO5709_Fall2020-1.pdf) course and utilized spreadsheets, Python and Plotly to [explore and analyze the 2019 AustinISD TEA accountability ratings](https://sites.google.com/view/5709final-saulmtz/home?authuser=0) as my final project! An analysis of the AustinISD Texas Education Agency accountability statewide ratings for 2019.  Course Description: Introduces principles and techniques for data visualization for creating meaningful displays of quantitative and qualitative data to facilitate decision-making. Emphasis is placed on the identification of patterns, trends and differences among data sets. TOOLS: Excel, PowerBI, Tableau, Python (MatPlotLib, Seaborn, Plotly) SKILLS: Graphic design principles - Color, Text, Interaction, Perception, Exploratory Data Analysis, data visualization techniques from charts to dashboards