๐ Phone: (818) -* | ๐ง Email: [email protected] | ๐ GitHub | LinkedIn | ๐ Northridge, CA
Accomplished and outcome-focused Data Analyst with a robust analytical and problem-solving acumen. Proficient in data manipulation, statistical analysis, and data visualization, with expertise in predictive modeling using machine learning algorithms. Experienced in deriving actionable insights from intricate datasets to drive value for innovative and forward-looking enterprises. ๐
- 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
- Western Governors University | Nursing | 05/2019
- Los Angeles Mission College | General Studies | 08/2017
- College of the Canyons | General Studies | 08/2015