Consulting data scientist with broad experiences in machine learning, data engineering, applied statistics, dashboard design, strategy consulting, market research, and instructional design for clients in multiple industries capable of quickly learning and adapting to new technical domains and business environments as well as collaborating with a spectrum of professionals.
In February of 2022, I have decided to make the jump that I had long been considering from civil engineer to data science.
Having harnessed an API for a popular structural engineering program for simplifying and enhancing the design of a marine wharf in my past position, I fell in love with coding and utilizing large data sets towards solving complex problems in engineering and beyond.
From April 2022 to June 2022, I attended a 12-week data science bootcamp (with machine learning) and produced the following projects:
- MACHINE LEARNING INDIVIDUAL CAPSTONE PROJECT
How Fast Can You CitiBike?
Blog article here.
GitHub repo here.
Researched NYC CitiBike rider behavior for predicting trip durations with 77% accuracy using Ridge regression and geographically visualizing city-wide travel patterns by borough and neighborhood using geopandas.
- MACHINE LEARNING GROUP PROJECT
A New Hybrid Approach to Data-Driven House Price Predictions
Dashboard here.
Blog article here.
GitHub repo here (for web-based dashboard).
GitHub repo here (for machine learning model).
Built an interactive dashboard using streamlit on a scored ElasticNet model with 93% accuracy predicting the price of homes and the value gained by their proposed improvements in Ames, IA.
- DATA VISUALIZATION INDIVIDUAL PROJECT (R/PYTHON)
To eBike or Not to eBike?
Blog article here.
GitHub repo here.
Analyzed the feasibility of riding the electric bikes of NYC CitiBike compared to other transit modes using R and Python.
- DATA VISUALIZATION INDIVIDUAL PROJECT (PYTHON)
Winter Olympics Performance by Country and Region (1998 - 2022)
Blog article here.
GitHub repo here.
Visualized the performance of countries in the Winter Olympics as a function of their size and economy using Python.
During this experience, I completed over 500 hours of hands-on experience with Python, MySQL, R, Git/GitHub, and Machine Learning. The topics included statistics, simple linear regression, multiple linear regression, generalized linear models, decision trees, gradient boosting, Ridge and Lasso regression, support vector machines, neural networks, clustering, imputing missing values, and other machine learning algorithms.
In addition to this bootcamp, I took the initiative to obtain the following certifications afterwards:
- Databricks Certified Associate Developer for Apache Spark 3.0
(2022-11-23) - Astronomer Certification for Apache Airflow Fundamentals
(2022-12-17) - Google Analytics Certification
(2023-06-07)
I am presently seeking my first opportunity in data science, data engineering, or software engineering. My combination of engineering technical expertise as well as experience in business development and field leadership make me ideally suited for a client-facing technical consulting role, but I am also open to positions in internal development and business analysis.