Node: A Data Science Course

Forge Academy is a non-profit school that provides students with the skills and opportunities to thrive in their early careers. They teach several different courses in relevant areas that is constantly updating based on the market’s needs.

I got introduced to Forge when I first did their Launch program, which pairs up hard-working students with companies for a summer internship. I took their Data Science course and landed an internship as a Data Analyst during my freshmen year of college. Afterwards, I became a teaching assistant for their Node course to help with teaching data science skills in Python. After one semester, I ended up taking over the teaching role and led a few teaching assistants for several semesters. In total, I taught data cleaning, data analysis, data visualization, and machine learning algorithms to over 60+ students while emphasizing the importance of data ethics throughout the course.

I hosted the whole course on Github and an online website for easy access to course materials, utilizing my skills in Git and domain hosting. The specific packages I focused on during the course were pandas, numpy, plotly express, and scikit-learn. Eventually, I trained a teaching assistant to take over my teaching role as I took up leadership roles in other extracurriculars I was involved in. Please feel free to explore the specific lessons I taught with the following link to the Github page: Node: A Data Science Course

Below are screenshots of some of the lessons:

Geographic Plot of Seattle Rentals (Week 4 Lesson)

This is a Plotly Express data visualization of different rentals in Seattle using AirBNB and Zillow. The color represents the neighborhood group and the size of each point represents the relative price of the rental. The plot is interactive within the lesson itself for easy searching and comparisons.

Decision Tree Metrics (Week 7 Lesson)

I introduced the decision tree machine learning algorithm with this simple example on whether or not Spikeball should be played on a given day. This shows what’s happening under the hood and how the machine learning algorithm makes its decisions.