Marketing | Data Analytics | Web Analytics | Results Driven
Dynamic and highly organized Marketing and Data Analyst. Obtained my Bachelor's of Science - B.S. in Marketing from California State University, Northridge. Received my certificate of Data Analytics and Visualization from University of California, Berkeley to continue my life-long learning and professional development. Looking for an opportunity, in an environment, that offers a great challenge to further help companies and clients advance effectively and productively. Enjoy leveraging background and skill set to support detailed and efficient analysis from both stakeholders and consumers in any given industry. Strengths including analytical problem solving abilities combined with collaborating across diverse groups, makes me a valuable addition to any team.
2022
University of California, Berkeley
Excel - Pivot Tables, VBA Scripting, Fundamental Statistics - Modeling, Forecasting, Python Programming - Python, NumPy, Pandas, Matplotlib, API Interactions, Databases - PostgreSQL/pgAdmin, MongoDB, ETL, Front-End Web Visualization - HTML, CSS, Bootstrap, Dashboarding, JavaScript, D3.js, Geomapping with Leaflet.js, Business Intelligence Software - Tableau, Big Data Analytics with Hadoop, Machine Learning, and Deep Learning.
2021
California State University, Northridge
Acquired quantitative and qualitative research and analytical skills to help firms better understand consumers, competitors and markets, and thus create effective strategies to identify, serve and maintain excellent relationships with their customers and partners. Also gained an understanding of how to manage brand success with the coherent integration of products and services; their sale and distribution through multiple channels; marketing communications programs including advertising and promotions using traditional and digital media; and effective pricing policies.
The ultimate goal of this analysis is to determine the underlying factors that led to the recent phenomenon being dubbed “The Great Resignation". We believe that younger generations are placing more emphasis on work/life balance, compensation transparency, remote work, and company culture. Our goal for this project is to find, process, and use the data we can find to see what has caused this shift around working conditions and employment. We used time series forecasting models particularly on Scikit-learn regression models to perform forecasting on time series. Skforecast was implemented as well as frequency distribution to predict the selected industries of analysis.
Tools used : Python, Jupyter Notebook, Pandas, Supervised Machine Learning, Skforecast, Recursive autoregressive forecasting, ForecasterAutoreg
Category : Machine Learning, Time Series Forecasting
Year : November 2022
The imbalanced-learn and scikit-learn libraries were implemented to build and evaluate models using resampling. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company, we oversampled the data using the RandomOverSampler and SMOTE algorithms, and undersampled the data using the ClusterCentroids algorithm. We then used a combinatorial approach of over- and undersampling using the SMOTEENN algorithm. In the next phase of our project, we compared two new machine learning models that reduce bias, BalancedRandomForestClassifier and EasyEnsembleClassifier, to predict credit risk. Lastly, we evaluated the performance of these models and make a written recommendation on whether they should be used to predict credit risk.
Tools used : Python, Jupyter Notebook, Pandas, Supervised Machine Learning, Logistic Regression, Random Forest Regression
Category : Machine Learning
Year : October 2022
The overview of this project incorporated a business proposal and pitch for Citi Bike for a particular group of potential investors. The goal and methodology of this project was to analyze the Citi Bike data that was collected in New York City in the month of August and launch a bike sharing business in Des Moines. The month of August was chosen specifically to analyze how the business operates in the peak of summer where tourism is the highest.
Tools used : Python, Jupyter Notebook, Pandas, Tableau Desktop, Tableau Public
Category : Visualization, Business Intelligence, Mapping Functionality
Year : September 2022
The purpose of this project was to convert the earthquake GeoJSON files using JavaScript, the Data Driven Documents D3.js, and Leaflet while also plotting the data on a mapbox map to an API request from the mapbox API key. From this, we were able to display and visualize the differences between magnitudes of earthquakes worldwide of the last seven days in correlation to the Earth's tectonic plate's location.
Tools used : Javascript, D3, Leaflet.js, GeoJSON, HTML, CSS
Category : Statistics, Correlation, Data Visualization, EDA
Year : September 2022
Dynamic and interactive visualization dashboard and charts using Plotly.js with a human belly button diversity dataset. Interactive dashboard and charts display bacteria that live inside the human body. Upon selection of an id number in a pull down list, the id metadata will be displayed in a div element and the top ten bacterial samples will be displayed in a pie chart and bubble chart.
Tools used : Javascript, D3, Plotly, JSON, HTML, Bootstrap, CSS
Category : Statistics, Correlation, Data Visualization, EDA
Year : September 2022
This project builds a web application that scrapes various websites for data related to the Mission to Mars and displays the information in a single HTML page. MongoDB is used with Flask to deploy results to an HTML page.
Tools used : Beautiful Soup, Pandas, Splinter, MongoDB, Flask, Bootstrap, HTML/CSS
Category : Web Application, Deployment, Database, ETL
Year : August 2022