Portfolio

Education

• B.S Computer Information Systems

  University of Houston - Victoria (May 2022)

• A.S Liberal Arts

  LoneStar College System (Dec 2020)

Certifications

• IBM Data Analysts Professional Certification Sep 2024 - March 2025
• Microsoft Power BI Data Analysis Associate Certification: March 2024
• Coursea - IBM Data Analyst Capstone Project March 2025
• Coursea - Python for Data Science, AI & Development Oct 2024
• Coursea - Data Visualization & Dashboard with Excel and Cognos
• Coursea - Excel Basics For Data Analysis Sept 2024
• Coursea - Introduction to Data Analytics Sept 2024

Work Experience

Student Intern @ Alabama A&M sponsored by the Department of Defence ( May 2022- July 2022)

• Enhanced a Feed Forward Neural Network (FFNN) model using Google Colab, Natural Language Processing, and Python to accurately identify numbers and physical objects with TensorFlow, Matplotlib, and NumPy backed by the NIST database, demonstrating expertise in data analysis and model tuning.
  
• Presented findings analysis from an articulate report on Artificial Intelligence during a briefing to U.S. Department of Defense officials, and prepared client-facing presentations and materials that effectively using communication skills to breakdown complex data in an easy-to-understand format.
  
• Demonstrated leadership qualities by leading and organizing a group of ten undergraduate students during this summer program to build and maintain relationships with Director of Project, group members and all other group members.
  
• Independently and effectively managing tasks, priorities, and resources to achieve objectives efficiently, while learning machine learning concepts such as object detection and classification.
  
• Contributed to daily Scrum ceremonies by delivering insights, updating team progress and goals, and presenting data to enhance collaboration and performance.

Project

Machine Learning Project - Long Term Short Term Model [In Progress]
Machine Learning Project - Simple Pytorch Learning Model April 2025 Link

• Built a fully functional machine learning model from scratch using PyTorch’s nn.Linear layer, demonstrating an understanding of model architecture, tensors, and data flow in neural networks.
• Designed and implemented a custom training loop including forward propagation, loss calculation with nn.MSELoss, backpropagation, and weight updates with torch.optim.SGD.
• Applied Mean Squared Error (MSE) loss function to measure model performance and optimize model parameters over multiple epochs to minimize prediction errors.
• Demonstrated understanding of optimizers and gradient descent by manually updating model parameters based on calculated gradients to improve model accuracy over time.
• Reduced model loss by over 80% over the training period, showing successful learning and convergence through iterative optimization.
• Explained complex machine learning concepts (loss functions, optimizers, training loops) through a storytelling analogy (“Bob the Model and Coach Smith”) to make foundational AI/ML principles more accessible to beginners.
• Documented the project with a detailed README explaining the project goals, learning outcomes, model architecture, and future improvement plans, ensuring clarity for technical and non-technical audiences.

Machine Learning Project - Simple Learning Regression Model April 2025 Link

• Built a supervised machine learning model to forecast daily maximum temperatures using historical weather data from Kaggle.
• Applied time series feature engineering techniques, including lag features, rolling averages, and seasonality transformations.
• Trained and evaluated a Stochastic Gradient Descent(SGD) forecasting model, achieving an RMSE (Root Mean Squared Error) as low as 1.61 on test data.
• Conducted rigorous model validation using Time Series Cross-Validation, reporting an average RMSE of 3.13 across multiple folds.
• Engineered seasonal features (day-of-year sine/cosine cycles) to improve model accuracy and capture periodic patterns.
• Developed and visualized forecast vs actual temperature trends using matplotlib, ensuring clear model performance interpretation.
• Gained hands-on experience with key machine learning libraries such as scikit-learn, pandas, numpy, and matplotlib.
• Demonstrated strong understanding of time-series forecasting challenges, including data leakage prevention (no shuffling) and temporal dependencies.

Machine Learning Project - Simple Tensor Model April 2025 Link

• Designed and implemented a simple TensorFlow model to perform supervised learning tasks, demonstrating strong understanding of tensor operations and data flow within neural networks.
• Applied core mathematical principles to understand and manipulate tensors, activation functions, and gradient-based optimization techniques during model training.
• Built and trained a basic model architecture from scratch, including defining input/output layers, weights, and bias initialization without reliance on pre-built templates.
• Optimized model performance by analyzing training loss behavior and understanding gradient descent convergence through hands-on experimentation.
• Demonstrated deep understanding of foundational machine learning concepts by translating mathematical formulas into functional TensorFlow code.
• Improved technical problem-solving skills by debugging tensor operations, dimensionality errors, and learning rate tuning challenges.
• Documented project with clear explanations of the underlying mathematics, model structure, and training results for easy reproducibility and future development.
• Strengthened Python programming skills with a focus on TensorFlow library usage, object-oriented design, and data manipulation.

Data Analyst Project - United States Crude Oil Import & Export Report Sep 2023 - Oct 2023 Link

• Demonstrated reporting on research United States’ top crude oil imports and exports for fiscal year 2022 self-learning project, for reporting on U.S Top Exports of Oil and Gas, turning raw data to identifying meaningful information, for strategic planning
• Create professional, visually appealing market trends, including charts, graphs, and infographics, from multiple source systems into data visualization report (Power BI).
• Detail oriented with excellent organizational, reporting solution, analytical, and problem-solving skills using DAX calculations and data modeling.
• Assessed the report to identify significant trends or changes over time, contributing to a comprehensive understanding of market.

Data Analyst Project - U.S Sales of EVs & PHEV Link

Excel Project - Self Project (Sept 2022 - Oct 2022)

• Crafted drill down reports on weather data using Pivot tables, leveraging sorting, grouping, filtering, and slicing techniques to distill complex data.
  
• Designed accompanying charts and diagrams to visually communicate trends and patterns in data.
  
• Enhanced overall understanding and informing strategic decision-making.
  
• Showcasing proficiency in data analysis, visualization, and attention to detail
  

Senior Project: Sentio AI: Stocks Sentiment Analysis Project The University of Houston – Victoria (Augest 2022 - December 2022)

• Assisted the Lead Team member in developing an iOS app utilizing Apple’s ML Core and Twitter Elevated+ API.
  
• Provided guidance to peers on API implementation and functionality.
  
• Gained experience in programming with Apple’s Swift language.
  
• Developed proficiency in Git version control system.
  
• Collaborated with team members on assigned tasks and shared responsibilities.
  

Database System: Coffee House Database The University of Houston – Victoria (Augest 2020 - Dec 2020)

• Demonstrated effective leadership skills as the team lead and resolved team issues, guiding a group of 3 members towards successful project completion.
  
• Leveraged expertise in Android Studios, PHP, MySQL, and Java to develop and deploy an end-to-end 3-tier database system, ensuring seamless functionality throughout the entire stack.
  
• Designed a robust ER-Model for the database system, showcasing meticulous attention to detail and a strong understanding of database design principles.