Beginning my career as an infrastructure engineer, I honed my skills in project management, complex construction oversight, and the meticulous examination of engineering designs for cost and quality efficiency. Intrigued by the potential of technology, I initially dipped my toes into coding through the creation of straightforward games and applications. This nascent curiosity rapidly grew into a profound passion for computer science, data science, and product management. In pursuit of my passion, I joined a Master's program in Data Science, mastering concepts such as data structures, algorithms, data processing, software engineering economics, data value, and digital marketing. One of the milestones in my career was launching my first startup, MusicMind. In this venture, I spearheaded product design, database creation, team recruitment, and raised $40,000 in funding. Further demonstrating my entrepreneurial spirit, I developed a React.JS web app, designed as a live cryptocurrency price calculator. A deep love for data has consistently been the backbone of my professional journey, leading me to accumulate significant experience in building web crawlers, implementing security measures, overseeing DevOps, managing database access, as well as mining and parsing data from public APIs.
While attending SMU's online Data Science Master's program, I worked with professionals from various industries and companies including Apple, IBM, and Microsoft. I did research into statistical analysis, machine learning algorithms, network engineering, and computer science principles.
Built a web crawler to capture images and metadata from Redfin.com’s newest listings, and developed a few-shot model for predicting property attributes like pool, view, kitchen island, high ceilings, hardwood floors, and fireplace.
View ProjectThis project takes a dataset of over 300,000 Kickstarter projects from 2009 to 2016. Data is cleaned, analyzed, visualized, and prepared for modeling using Multiple Linear Regression to predict the amount pledged for any particular kickstarter.
View ProjectAn exploratory data analysis project that aims to understand the highest volume streaming music on Spotify in 2017
View ProjectCryptoBox brings cryptocurrency to the cloud, and currently offers a Price Ticker & Currency Calculator that helps you manage your cryptocurrency. The calculator allows you to simulate trades & token exchanges.
View ProjectMusicMind is a Music Messenger App and Social Network that allows users to connect to their personal music collection or music streaming service, such as their Spotify account. They can then stream songs, and curate and share music video messages to the network, or to other social media platforms. MusicMind has a Database that holds and collects lyrics and other types of music metadata suchch as artist popularity, and audio-analysis features. We are using music metadata, and lyrics to transform songs into visual augmentation, creating a multi-media experience for users to engage more with music in real-time.
View ProjectThis report dives into the process of collecting and analyzing tweets from Twitter's public API. The project details the use of Spark, and nlp library to discover tweets, determine context, and filter topics. As a result of this project, my team was able to analyze thousands of tweets leading up to the 2016 NBA Finals to predict the winner, The Golden State Warriors.
View ProjectThis report was written with a team of peers from SMU in 2016. It covers data collection, analysis and reporting on how public images of the presidential candidates affected their polling numbers on a weekly basis.
View ProjectThis Jupyter notebook was developed by myself demonstrates the power of python, SK-learn, and Keras (runs on TensorFlow) to to get a 99.54% accuracy on kaggle for top 18% of submissions.
View ProjectThis report was written by myself for a Network Security Class in 2016. It provides a survey of blockchain technology, and it's potential impact on banking, finance, privacy and democracy.
View ProjectThis report was written for SMU statistical Analysis in 2016. It details the relationship and significance of student and instructor perceptions and use of wikipedia for academia.
View Project