Free FIU Data Science One Hour at Bootcamp: Intro Naive Bayes workshop -  Register Here

Made at Metis: Deep Learning to Detect Pneumonia & Predicting Spotify Track Skips

By Carlos Russo • August 31, 2020

This post features two projects from recent graduates of our data science bootcamp. Take a look at what's possible to create in just 12 weeks. 

Pneumonia Detection: Pushing the Boundaries of Human Ability with Deep Learning
Jenny Wang, Strategy and Operations Analyst at Google

Getting pneumonia may be more common than you think. According to bootcamp graduate Jenny Wang, it's the most common reason for U.S. children to be hospitalized and it's the most common cause of hospital admissions for U.S. adults other than women giving birth. For her final project, she chose to build a deep learning model to detect pneumonia from chest X-Ray images. In a blog post summarizing the project, she writes: 

"It turns out that chest X-rays have long been considered the best tool to detect any form of pneumonia. However, studies have shown that even experienced radiologists often have a hard time correctly identifying whether something is an infiltrate (a substance denser than air, such as pus or fluid) on the X-Ray. This causes delays in diagnosis, which increases the disease severity and associated mortality rate. If humans aren’t able to correctly diagnose the disease early, maybe…deep learning can help!"

In her post, she goes into detail about her approach to the project, the data collection, the analysis, image processing, modeling, and more. It's a great read, and you can check it out here.

Predicting Spotify Track Skips
Austin Poor, Metis Bootcamp Graduate

For his third of five projects during the bootcamp, graduate Austin Poor worked on what he calls "a slightly simplified version of the Spotify Sequential Skip Prediction Challenge." As he details in a blog post about the project, Spotify supplied two main sets of information for this competition.

The first consisted of data about user listening sessions, including details like track ID, position in the session, if the track was skipped or not, and more. The second dataset consisted of information about the tracks themselves, including duration, popularity in the U.S., and release year. 

In his post, read how he took a subset of this data and used feature engineering and more to get results and plans for future work. Read it here.


    See more examples of Metis student projects here

    Similar Posts

    data science
    Two Metis Team Members Featured in New Book, 'Mothers of Data Science'

    By Emily Wilson • July 23, 2020

    In a book published last week, read interviews with two Metis team members – Chief Data Scientist Debbie Berebichez and Data Scientist Alice Zhao – about their experiences as mothers and data scientists.

    data science
    Python Guide: Tutorial For Beginners

    By Adam Wearne • July 28, 2021

    Welcome to a brief introduction to Python. In this article, we'll provide an overview of the Python language, some of its many use cases, how to install Python on your computer, and how to use Python.

    data science
    Learn Machine Learning in 6 Months

    By Zachariah Miller • May 24, 2021

    I came across a question on Quora that boiled down to: "How can I learn machine learning in six months?" I started to write up a short answer, but it quickly snowballed into a huge discussion of the pedagogical approach I used and how I made the transition from physics nerd to physics-nerd-with-machine-learning-in-his-toolbelt to data scientist. Here's a roadmap highlighting major points along the way.