📞 Phone: (818) -* | 📧 Email: [email protected] | 🌐 GitHub | LinkedIn | 📍 Northridge, CA

• Technologies: Python, Git, SQL, GoogleBigQuery
• Tools: Tableau, Jupyter Notebooks, Scikit-Learn, Pandas, Matplotlib, NumPy, Seaborn, Zoom, Microsoft Outlook, Microsoft Excel, Microsoft Word, Microsoft Powerpoint, Canva, Google Docs, Google Bigquery, Streamlit, APIs, Cerner, database, command line
• Techniques: Descriptive Analysis, Regression Analysis, Machine Learning Algorithms, K-Nearest Neighbors, Decision Trees, Logistic Regression, Linear Regression, Supervised Machine Learning, Preprocessing (StandardScaler), Confusion Matrix Display
• Certifications: Coding Temple - Full-Time Live Data Analytics Program [Link Certification]
• Soft skills: Customer Service, Adaptability, Time Management, Attention to Detail, Problem Solver, Collaboration, Creativity, Presentation skills, Hospitality, Leadership, Ethical

• Developed a web application using Streamlit and Python, integrating Spotify's API for real-time data access - https://songstats.streamlit.app
• Utilized Jupyter Notebook to ensure dataset quality and accuracy through data extraction, cleaning, and manipulation
• Employed Python libraries (Pandas, Scikit-Learn, Matplotlib) for efficient data processing and visualization
• Implemented interactive graphs within the application to illustrate song metrics and popularity
• Leveraged Big Data principles to optimize application performance with efficient handling of large datasets
• Created an intuitive user interface allowing users to view song metrics, popularity, and preview songs

• Led data acquisition from Kaggle.com using advanced web scraping techniques to obtain comprehensive video game sales datasets
• Utilized Python libraries (Pandas, NumPy, Matplotlib, Seaborn) in Jupyter Notebook for precise data manipulation and analysis
• Transformed raw data into actionable insights through meticulous preprocessing, ensuring data accuracy for strategic decision-making
• Developed captivating Tableau dashboard showcasing key performance indicators, enabling data-driven decisions
• Collaborated with stakeholders to tailor dashboard insights, empowering informed strategic decisions
• Demonstrated exceptional communication skills in presenting and training stakeholders for optimized engagement

• Created an interactive Tableau dashboard tailored to stakeholders' preferences, reflecting Netflix's brand colors
• Incorporated user-friendly features for selecting and viewing key information on movies and TV shows
• Implemented dynamic visualizations for Top 10 genres, yearly counts, and ratings distribution
• Ensured seamless navigation for stakeholders, facilitating quick access to insights
• Collaborated closely with stakeholders to refine dashboard design and functionality
• Demonstrated Tableau proficiency for efficient data visualization without a separate data warehouse

• Developed an interactive Streamlit web app integrating machine learning models to predict airline satisfaction based on diverse customer feedback parameters - https://airline-customer-satisfaction.streamlit.app
• Utilized Python to implement logistic regression and decision tree algorithms for accurate satisfaction level classification.
• Integrated data from customer surveys and airline databases to train and validate predictive models, ensuring robust performance
• Designed user-friendly interface and conducted rigorous testing to deliver a seamless user experience
• Presented web app to stakeholders, providing comprehensive training on functionalities a

• Conducted regression analysis to predict home prices based on key features
• Utilized Python and regression models for analyzing historical sales data
• Implemented data preprocessing techniques to ensure model accuracy
• Developed and optimized regression models for reliable predictions
• Integrated predictive model into a real-time application for users
• Collaborated with stakeholders to refine model features and demonstrate the value of predictive analytics for real estate

• Conducted thorough analysis of NYC Airbnb data using Python and data manipulation libraries for preprocessing
• Employed data visualization tools to create informative visuals and identify key trends
• Developed predictive models to forecast listing prices, contributing to strategic decision-making
• Collaborated with stakeholders to deliver actionable insights and recommendations, showcasing strong analytical and problem-solving skills in project execution

• Acquired hands-on experience with real-world datasets, applying statistical analysis and data visualization techniques
• Successfully deployed two Streamlit apps using real-world datasets, showcasing practical application of learned skills
• Demonstrated proficiency in data cleaning, exploratory data analysis (EDA), and model fitting during the bootcamp projects
• Utilized machine learning modeling techniques to create predictive models for song popularity and customer satisfaction in the deployed apps
• Utilized Python and pandas for data wrangling, cleaning, and preprocessing tasks in a Jupyter Notebook
• Conducted exploratory data analysis (EDA) on various datasets to extract meaningful insights using Scikit-Learn/Matplotlib/Seaborn
• Applied statistical techniques for hypothesis testing and trend analysis
• Created compelling data visualizations using tools such as Matplotlib, Seaborn, Streamlit, and Tableau
• Developed proficiency in SQL for querying and analyzing relational databases
• Participated in hands-on projects involving data interpretation and decision-making
• Collaborated with peers to solve real-world data challenges
• Familiarized with machine learning concepts and algorithms for predictive modeling
• Engaged in continuous learning and stayed updated on industry best practices

• Accurately inputted and verified data into electronic databases
• Proficient in maintaining records, conducting quality checks, and ensuring data accuracy
• Worked cooperatively and fully communicated verbally with Area/Regional Phlebotomy Management, Logging Area, Lab Dispatch, Customer Service, Call Center, Redraw department, and Courier services to ensure case completion throughout the entire shift
• Accommodated multiple facilities' special requests, honored high-priority facility laboratory orders, expedited urgent cases conducting constant follow-up
• Maintained a positive attitude to connect parties to ensure excellent customer service was being provided

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• College of the Canyons | General Studies | 08/2015