Edward was a junior in a private day school with a longstanding interest in computer science-- and freestyle rapping. He'd previously taken AP Computer Science and loved it, but since his new school lacked class options for expanding his knowledge in depth, he was learning Python independently and hoped to use his mentorship to learn more about the field in general.
To build his Python skills, particularly the standard data/ML field
To explore machine learning and different ways raw data sets can beaccessed and manipulated
Learning to use and analyze data using code
Edward's mentor was a PhD candidate in Data Science/Music Technology at NYU, who previously built algorithms for Amazon Music's recommender system for detecting cover songs in their ~10 million song catalog.
Edward learned python tools including matplotlib, numpy, pandas, and scikit, learned implementation considerations of classical machine learning algorithms and built a linear regressor and K-nearest neighbors classifier from scratch.
Edward and his mentor covered the basic statistics required for entry-level data analysis and worked in Google Colab to learn how to import and handle data and build state-of-the-art machine learning models.
Combining his curiosity for data science with his interest in music, Edward built a genre classifier using a Spotify song and genre dataset, which merged his knowledge of python for pre-processing its raw data and his skills with machine learning implementations from prior sessions. His model was able to successfully predict the high-level genre of a song with 50% accuracy.
I feel much more confident with coding in general, which will help with my advanced topics in computer science class.
Specifically building the linear regression and k-nearest neighbors models from scratch was most meaningful for me.
I think [my mentor] was extremely knowledgeable, to a point of awe. Yet even with all of that knowledge, he still was approachable and friendly. I felt I both learned fundamental computer science and data analysis principles but also how to gain more knowledge on my own. He was incredible!
Appreciation for the value of his own contributions
Clarification of which academic areas interest him
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