🎓 M.S. in Data Science | Applied Statistics | Machine Learning | Data Visualization

Welcome to my GitHub profile! I’m currently pursuing my Master’s in Data Science at California State University, East Bay, with an expected graduation in May 2026. My work focuses on applied statistics, predictive modeling, and building data-driven solutions using R, Python, SQL, and modern visualization tools.

Here’s a snapshot of what I’ve been working on:

• ANOVA, regression modeling, bootstrapping, and resampling methods
• Predictive modeling and supervised learning techniques
• Hands-on academic projects across probability, statistical methods, and advanced R programming
• Building reproducible analyses with R, R Markdown, and Quarto

📁 Featured Coursework Repository:
👉 GradSchoolCoursework

• R (tidyverse, ggplot2, boot, caret)
• Python (pandas, NumPy, scikit-learn)
• SQL (MySQL, PostgreSQL)

• RStudio, Git/GitHub, Tableau, Power BI, Jupyter, Visual Studio Code

• Exploratory data analysis
• Statistical inference & hypothesis testing
• Regression, classification, time series
• Resampling (bootstrap, jackknife, permutation tests)
• Machine learning workflows

• STAT 620 — Probability & Statisical Theory
• STAT 630 — Statistical Methods
• STAT 650 — Advanced R Programming

• STAT 631 - ANOVA
• STAT 632 - Linear & Logistic Regression

• STAT 640 — Advanced Statistical Theory
• STAT 641 — Bootstrapping
• STAT 651 — Data Visualization

• STAT 652 - Statistical Learning
• STAT 653 - Statistical Natural Language Processing
• STAT 692 - Comprehensive Exam

Explore all coursework →
📁 GradSchoolCoursework

• Data engineering foundations
• Advanced SQL optimization
• Machine learning model tuning
• Tableau interactive dashboards

• 💼 LinkedIn: www.linkedin.com/in/brandon-keck-statistician

Thanks for stopping by! Feel free to explore my repositories or reach out if you'd like to collaborate, network, or chat about data science. 🚀