Data Science Machine Learning

Shepherd Moonemalle

Conroe, TX *****

936-***-**** ***************@*****.*** www.linkedin.com/in/shepherd-m Summary

Versatile and results-driven professional with a strong foundation in computer science and data science. Proficient in Python, Java, machine learning, and software development, with experience in building intelligent systems, analyzing complex datasets, and developing full-stack solutions. Known for problem- solving, technical versatility, and effective communication in collaborative settings. Seeking entry-level opportunities in software development, data science, or related technical fields. Education

08/2023 - 12/2024 Sam Houston State University Huntsville, TX Master of Science: Computing and Data Science

GPA: 3.6/4.0

Master's Project: Developed a Network Intrusion Detection System using Matrix Factorization and Consensus Clustering techniques to improve anomaly detection in network traffic patterns. 08/2020 - 08/2023 Sam Houston State University Huntsville, TX Bachelor of Science: Computing Science

Dean's List (Spring 2023)

Skills

Programming Languages: Python, Java, Ada,

SQL, HTML, CSS, JavaScript

Machine Learning & AI: Supervised and

unsupervised learning (classification,

clustering), Feature engineering,

Dimensionality reduction (e.g., PCA, SVD,

NNMF, CUR), Model evaluation and

optimization, Scikit-learn, TensorFlow, Keras

Data Analysis & Visualization: Pandas,

NumPy, Matplotlib, Seaborn, Excel

Database Management: MySQL, PostgreSQL,

Database design and querying, SQL

optimization

Web Scraping & APIs: Beautiful Soup; REST

APIs

Version Control & Collaboration: Git,

GitHub

Tools & Environments: Jupyter Notebook,

Google Colab, VS Code

Projects

Invoice Scanner using YOLO Algorithm (Group Project) Collaborated with a team to design and implement an invoice scanner using YOLO-based object detection for extracting invoice details. Managed the database integration and ensured efficient data processing.

Technologies: Python, OpenCV, YOLO, SQL

Brain Tumor Segmentation based on MRI Scans

Developed a deep learning model using Fully Convolutional Neural Networks (FCNN) to segment and classify brain tumors from MRI scans, achieving an accuracy of over 90%. Technologies: Python, TensorFlow, Keras, OpenCV

Experience

01/2024 - 12/2024 SHSU Department of Computer Science Huntsville, TX Graduate Assistant

Provided technical support for undergraduate students, guiding them through concepts of network security, penetration testing, and vulnerability assessment.

Managed grading for two courses per semester, including weekly assignments for a class of 60 students, offering clear and constructive feedback on programming and security-related tasks.

Utilized tools such as Wireshark, Kali Linux, Nmap, Metasploit, VirtualBox, and Docker to support students in performing penetration testing, network traffic analysis, and security assessments.

Maintained a structured approach to tasks while managing multiple responsibilities under deadlines and helped organize events that fostered a collaborative and supportive learning environment.

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