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Edward
's mentorship /

Delving into Data Science

Learning a new skill

Mentee Profile

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.

Mentee Goals

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

Mentor Match

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.

Custom Curriculum

Skill Development

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.

Program

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.

Final Project

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.

MENTEE FEEDBACK

Students self-reported large gains in:

Technical skills

Appreciation for the value of his own contributions

Clarification of which academic areas interest him

Vist project website

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Projects

Hugo has run hundreds of mentorships across dozens of fields.  Each of them are unique and designed around the students background, goals and interests.  Below are some of the final projects students developed during their mentorships.

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