Appendix: Interview: DS General Interview
Resources:
- https://www.quora.com/What-are-some-ways-to-prepare-for-a-data-challenge-with-a-Silicon-Valley-tech-company-How-do-I-make-sure-I-have-sufficient-fluency-with-data-munging-and-ML-in-Python
- https://www.thisismetis.com/blog/2018-rewind-most-read-blogs-of-the-year
- https://www.thisismetis.com/blog/cracking-the-take-home-coding-exercise
- https://www.springboard.com/blog/data-science-interviews-lessons/
- https://www.springboard.com/blog/data-science-interview-questions/
- https://medium.com/@AirbnbCandidateJourney/leveling-the-playing-field-an-overview-of-airbnb-s-data-science-interview-process-bd0660b77a17
- https://medium.freecodecamp.org/the-essential-guide-to-take-home-coding-challenges-a0e746220dd7
- https://www.fullstackinterviewing.com/2018/02/02/the-ultimate-guide-to-kicking-ass-on-take-home-coding-challenges.html
- https://towardsdatascience.com/how-to-land-a-data-scientist-job-at-your-dream-company-my-journey-to-airbnb-f6a1e99892e8
- http://thedataist.com/adventures-and-misadventures-in-data-science-interviews/
- https://ryanpmccaffrey.github.io/2017-04-02-Uber-Data-Challenge/
- https://towardsdatascience.com/the-big-list-of-ds-ml-interview-resources-2db4f651bd63
- https://datamasked.com/
- https://www.quora.com/How-do-I-get-a-job-as-a-data-scientist-if-I-have-no-prior-experience-It-seems-like-Kaggle-is-either-meaningless-or-an-ineffective-route-to-becoming-a-data-scientist-I-was-also-told-there-is-no-point-in-putting-up-my-code-on-GitHub
- https://www.quora.com/What-are-the-most-common-mistakes-made-by-aspiring-data-scientists
- https://www.quora.com/What-are-the-golden-rules-of-interviewing-for-a-data-science-job
- https://www.quora.com/What-are-tips-for-data-science-interviews-1
- https://www.quora.com/What-kind-of-portfolio-should-an-entry-level-data-scientist-have
- https://towardsdatascience.com/building-a-recommendation-system-for-fragrance-5b00de3829da
My checklist:
- create functions that do the following:
- A function that plots all the variables against the label (this will already give you some insights to talk about!)
- A function that bins continuous variables into classes.
- A function to extract info from dates.
- A function building the ROC curve and optimizing the cutoff point.
- A function to cross-validate.
- A function that returns partial dependence plots for the top random forest variables.
- A function that builds a decision tree and automatically extracts the top 3/4 splits.
- Basic feature engineering tasks
- Functions for parameter tuning
- be able to:
- Do cleaning without standard packages
- Cleaning & muning with numpy, pandas, regex, etc
- SQL joins
- Know:
- Different types of machine learning tasks
- Common algorithms for tasks
- Key measures of performance
- Model interpretability
