Photo by National Cancer Institute on Unsplash

Domain — Healthcare

Company — Oncora Medical

Article —

Opportunity: to help physicians make informed treatment and patient care decisions


  1. Minimize risk of toxicity
  2. Maximize chance for cure
  3. Improve care for patients

Of the problem types introduced in the lecture, which one does this use case most closely align?

Prescriptive — recommend an optimal cancer treatment plan for each patient

Prediction — predicting the outcome of cancer treatment plans

How did the company/organization use data science to achieve the desired impact?

Using historical treatment data to build predictive models that help design an optimal treatment plan for cancer…

Photo by Yuyeung Lau on Unsplash

Week 3 and 4 at Metis Bootcamp was definitely increasingly onerous. This module combined two major topics — webscraping and linear regression. If you’re like me, with no math or stats background, some of the theories and concepts may seem a bit abstract by explaining with equations. Luckily, I found this channel on Youtube — StatQuest, that explains the concepts in graphs and made it so much easier to understand. Hope this is helpful to you as well!

Same as the last module, there’s a project due by the end of the second week. The second project at Metis is…

photo credit — Marian Carrasquero for The New York Times

As a Metis Data Science Bootcamp student, our first project is to perform an exploratory data analysis on NYC MTA turnstile data. We have to come up with a potential client who might find this data analysis useful and with a backstory for the analysis.

On top of all the things we’re learning in the first week, it seems like an impossible task. But it ties in with the curriculum pretty well and gives us the chance to put all the skills we just learned the past two weeks into practice.


My project’s backstory was built upon the recent proposal

Crystal Huang

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