Here's a selection from Udacity's website. Initially, I intended to post questions from Google or Microsoft hiring managers and recruiters, but you can find these questions by doing a Google search, or reading the following book:
Anyway, here are some of these questions and topics to discuss during a job interview for a data scientist position. More can be found here, or in this book. Can anyone provide good answers?
- What do you think a data scientist is/does?
- What do you think are the most important skills for a data scientist to have?
- Which machine learning model (classification vs. regression, for example) to use given a particular problem.
- The tradeoffs between different types of classification models. Between different types of regression models.
- How to go about training, testing, and validating results. Different ways of controlling for model complexity.
- How to model a quantity that you can’t directly observe (using Bayesian approaches, for example, and when doing so, how to choose prior distributions).
- The various numerical optimization techniques (maximum likelihood, maximum a posteriori).
- What types of data are important for a particular set of business needs, how you would go about collecting that data.
- Dealing with correlated features in your data set, how to reduce the dimensionality of data.
- Linear/polynomial regression
- Decision trees
- Dimensionality reduction
- Why you chose the model you did, given the problem you were trying to solve.
- What the features of your data were.
- How you tested and validated the results.
- What you got out of the project.
- When you get a new data set, what do you do with it to see if it will suit your needs for a given project?
- How do you handle big data sets — how would you start work on a project with an associated data set that was many tens of GB or larger?
- What’s a project you would want to work on at our company?
- What data would you go after to start working on it?
- What unique skills do you think you’d bring to the team?
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