AZAJ PATEL

MSc Data Science graduate (Distinction) with 2+ years of experience across data analysis and database administration. Skilled in analyzing, cleaning, and transforming large-scale structured and unstructured datasets. Proficient in Python, R, and SQL for exploratory analysis, statistical modeling, and scenario evaluation. Experienced in managing databases, writing optimized queries, and ensuring data integrity. Strong grasp of statistical probability concepts including bias, sampling, and error distributions. Adept at writing technical documentation, collaborating across Agile teams, and ensuring accuracy in data pipelines. Passionate about ethical AI, privacy-aware data science, and turning complex data into actionable insights. Open to roles in data science, analytics, or ML engineering within impact-driven teams.

• Programming & Tools: Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), R, SQL, Git, Excel, Jupyter, VS Code.
• Statistical Modeling & Analysis: Probability Distributions, Sampling & Resampling, Statistical Bias/Error, Hypothesis Testing, Confidence Intervals
• Visualization & Reporting: Power BI, Google Data Studio, Looker Studio, Excel Dashboards
• Data Analysis & Pipelines: Data Wrangling, Data Cleaning, API Integration, Automation (Python, SQL), Data Validation
• Database Admin: Database Management and Maintenance, SQL Query Optimization, Backup and Recovery Strategies, Data Security and Access Control
• Cloud & Databases: Oracle, Google Cloud Platform (Big Query), AWS, MySQL, MongoDB
• Data Analysis & ML: A/B Testing, EDA, Predictive Modeling, Customer Segmentation, Classification, Regression, Clustering, Statistical Analysis, Experimentation Design, Scenario Analysis
• NLP & Deep Learning: NLP (SpaCy, NLTK, VADER), LSTM, CNNs, Transformers (intro),
• Soft Skills: Technical Documentation, Stakeholder Communication, Cross-functional Collaboration, Agile & Scrum Methodology, Data Storytelling, Executive Reporting, Continuous Improvement

Dissertation: Building an Image Recognition System with Contextual Understanding Using Deep Learning

• Gained hands-on experience in deep learning for computer vision, working with object detection, visual relationships, and scene understanding.
• Utilized the Open Images dataset, performing data preprocessing, exploratory data analysis, and model evaluation using metrics like mAP.
• Developed a context-aware image recognition model integrating bounding boxes, segmentation masks, and narrative annotations.
• Designed and deployed a user-friendly interface for image upload and result visualization, applying Python and key ML libraries.

Predictive Modeling for Breast Tumor Classification (11/2023-02/2024)

• Developed a machine learning model to classify breast tumors as malignant or benign, improving early detection rates with 98.24% accuracy.
• Conducted feature engineering using 10 key tumor characteristics, achieving a recall score of 89.66%.
• Implemented deep learning techniques to enhance model performance and classification accuracy.

Data-Driven Analysis of Amazon Book Reviews Using NLP and R (12/2023-02/2024)

• Performed sentiment analysis and topic modeling on Amazon book reviews using R and NLP techniques to extract key customer insights.
• Implemented LDA for topic modeling and sentiment analysis to classify reviews and identify emerging themes.
• Developed visualizations and data-driven recommendations to assist publishers in improving book content and marketing strategies.