Results Driven | Customer Focused
Analytical, energetic, and result-driven analyst with an ever-broadening knowledge base in business and analytics. Experienced in SQL database management, ETL, Python, Tableau, business intelligence, data visualization, EDA, and machine learning. Passionate about improving processes and bringing a data driven mindset to all.
2022
Georgia Institute Of Technology, Atlanta, GA
Advanced Excel, Fundamental Statistics, Python Programming, API Interactions, Data Mining, Web scraping, Databases - Postgres, MongoDB, 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.
2019
Georgia State University, Atlanta, GA
Biotechnology & Bioinformatics, Bioethics, Biochemistry, Endocrinology, Microbial Pathogenesis, Advanced Genetics, Molecular Neuroscience, Cell Physiology, Immunology, Virology
2016
Clayton State University, Morrow, GA
Advanced Cellular Biology, Bioinformatics, Biochemistry, Pathophysiology, Microbiology, Organic Chemistry, Physical Chemsitry, Physics, and Genetic Transformation. Demonstrated proficiency in utilizing laboratory instruments for a wide array of experiments, including: gene cloning, DNA isolation, PCR, Southern Blotting, ELISA, SDS PAGE, Western Blotting, electrophoresis, restriction digestion, preparing and loading gels, and gene detection for sickle cell anemia.
This project utilizes neural networks and deep learning to predict whether applicants will be successful if funded by the nonprofit, Alphabet Soup, based on data from over 34,000 organizations.
Tools Used: : Neural Network, Tensorflow, Keras
Category : Deep Learning, Machine Learning
Year : August 2022
This project uses Javascript, Plotly and D3 to build an interactive dashboard exploring and analyzing belly button microbial diversity. The dashboard dynamically populates based on test subject ID selected.
Tools used : Javascript, D3, Plotly, JSON, HTML, Bootstrap, CSS
Category : Statistics, Correlation, Data Visualization, EDA
Year : June 2022
This project I examine and measure the entire dialogue of the show South Park in terms of offensiveness, the use of offensive language, to visualize if the amount of offensive language and content has decreased over the run of the series.
Tools used : Python, Pandas, NLTK Tokenizer, TextBlob Sentiment Analysis, Matplotlib, StopWords, WordCloud, and Punctuation Lists/p>
Category : Python, Pandas, Tokenizer, Sentiment Analysis
Year : June 2022
The United States Geological Survey (USGS) is responsible for providing scientific data about natural hazards, the health of our ecosystems and environment; and the impacts of climate and land-use change. Their scientists develop new methods and tools to supply timely, relevant, and useful information about the Earth and its processes. This project combines the USGS earthquake data into a new visualize tool their earthquake data which allows them to better educate the public and other government organizations on issues facing our planet and hopefully secure more funding.
Tools Used:D3, HTML, Leaflet.js
Category : : Data Visualizaton, JavaScript, API, Maps
Year : : July 2022
Considering recent events, we endeavored to take a closer look at gun violence in America. Our hope was to identify trends which if we are lucky might point to some root causes though a look at commonality among incidents. We hope our research can help others find a path that will lead to a solution. where multiple maps update from data that is stored in PostgreSQL. The project is powered by a Python Flask API.
Tools used : Python, SQLAlchemy, PostgreSQL, Javascript (D3.js, Chart.js, Leaflet.js), HTML/CSS/Bootstrap, Flask
Category : Web Application, Deployment, ETL, Full Stack Development, Teamwork
Year : August 2022
Generated a official dashboard by utilizing Tableau. By combining multiple years of data from Citi Bike's websites I was able to visualize multiple different phenomena and answer many questions city officials, public administrators, and heads of New York City municipal departments had regarding the program including: popular star/stop routes, age, gender, social status, duration, peak hours and rental times versus monthly subscribers. This project utilizes Tableau to display the advantages data visualizaitons has on telling a story.
Tools used : Tableau
Category : Data Visualization
Year : October 2021
Machine Learning automates model building, leading us to the creation of systems that can identify patterns, make decisions, and learn without much human intervention. It's an exciting realization in the growth of artificial intelligence as a computer learns from previous computations and produces reliable and repeatable results. I built a machine learning model that attempts to predict whether a loan from LendingClub is high risk or not. LendingClub is a peer-to-peer lending services company that allows individual investors to partially fund personal loans as well as buy and sell notes backing the loans on a secondary market. LendingClub offers their previous data through an API. I used this data to create machine learning models to classify the risk level of given loans. Specifically, I compared the Logistic Regression model and Random Forest Classifier.
Tools used : Python, Supervised Machine Learning, Logistic Regression, Random Forest Regression
Category : Machine Learning
Year : August 2022
This project used unsupervised machine learning to create a report where I prepared the data by removing columns, dropping null values, creating a scalar, and trained the scalar on the data. In part two, I applied the Dimensionality Reduction using PCA and then took it a step further and was able to reduce even more with t-SNE. Lastly, I did a Cluster Analysis with a K-Means model. It seems the best number of clusters is 3. I found that t-SNE wasnt helpful in finding clusters although after adjusting perplexity there was neglible differences. The dataset may be too small to make a precise prediction as noted after looking a patterns after the K-Means analysis. I recommend a larger set of data then splitting the data into testing and training data to these models for more accurate clustering and results.
Tools used : Python, Unsupervised Machine Learning, Dimensionality Reduction, KMeans, TSNE
Category : Machine Learning
Year : July 2022
This project builds a web application that scrapes NASA 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 : May 2022
- Data Science and Analytics Boot Camp, Georgia Institute of Technology 2022