Guidelines for
Challenge and Interview

Challenge Problem

We ask fellows to work on a small challenge problem to assess problem solving and coding capabilities

Choose a challenge problem from a list provided to you when you login.
Perform your analysis in a well-commented Jupyter notebook and post on Github or Google Colab.
Share your notebook with us.

Some hints for hacking our challenge

Ask yourself why would they have selected this problem for the challenge? What are some gotchas in this domain I should know about?

What is the highest level of accuracy that others have achieved with this dataset or similar problems / datasets ?

What types of visualizations will help me grasp the nature of the problem / data?

What feature engineering might help improve the signal?

Which modeling techniques are good at capturing the types of relationships I see in this data?

Now that I have a model, how can I be sure that I didn't introduce a bug in the code? If results are too good to be true, they probably are!

What are some of the weaknesses of the model and and how can the model be improved with additional work.

Technical Interview

In the interview, you will be asked few questions that will help us evaluate your application discussing the following

Introduction - A short introduction to your background and skills, as well as your passion  for ML.
Motivation - Why do you want to participate in this program?
Technical Knowledge - You will be asked 3 technical questions to measure your conceptual knowledge in ML and data science.

Challenge Problem Frequently Asked Questions

What are you looking for in challenge submissions?
  1. Problem solving ability - did you understand the problem correctly, and did you take logical steps to solve it?
  2. Machine learning skills - what sort of models did you use? How rigorous was your exploratory analysis of the data, your choice and fine tuning of models, and your assessment of results.
  3. Coding skills - does your python look presentable or do you code like a scientist?
  4. Communication skills - is your solution readable and well explained? Messiness and raw code with no explanation does not reflect well on your potential for working well with our business partners during the fellowship.
What are some of the common mistakes I should avoid?
  1. Skipping exploratory analysis and feature engineering
    Do not jump straight into fitting models without demonstrating to us, in your Jupyter notebook, that you have understood and thought about the dataset.
  2. Choosing models with no explanation
    Please use the notebook to explain your thought process. We care about this as much as we care about your results.
  3. Unreadable notebooks
    Make sure to run your notebook before sharing so that we can see the results. We won't be running your code on our machines. On the other hand, please do not print out the entire dataset or endless rounds of epochs.
  4. Overly simplistic final results
    Your final results should consist of more than a single number or percentage printout. Explain why you chose the success metrics you chose, and analyze what your output means.
When are the challenges due?
All deadlines can be found on our apply page.