Data Science FAQ
Intro
This section outlines the common questions I’m asked during the data science mock interview sessions and is based solely on my experience. Please, take everything I share below with a grain of salt, and don’t use this as a single source of truth, since it’s not.
First of all, there are many “flavors” of data science, and even within one company different teams might look for different skill sets. Depending on the team, the focus can be on computer vision, NLP, forecasting, causal inference, dashboarding, building data pipelines, etc. Furthermore, some companies can have a “data scientist” role, but your actual work will look more like a data analyst, businesses intelligence engineer, data engineer, ML engineer, or research scientist.
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However, there are some common areas that many companies are focusing on when interviewing for data science positions. These areas typically include applied statistics, core ML, SQL, and Python/R.
How do I prepare for an interview?
What resources should I use to study?
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Probability & Statistics:
www.khanacademy.org/math/statistics-probability -
SQL:
www.sqlzoo.net -
Python:
www.datacamp.com/courses/intro-to-python-for-data-science -
Core DS / ML concepts:
www.coursera.org/learn/machine-learning -
Interview Preparation:
www.dsinterviewdaily.com
What is the compensation of a data scientist?
The compensation of a data scientist varies depending on many factors like company, level, tenure, education, relevant experience, market conditions, etc. Furthermore, it changes over time. You can look up the compensation ranges at levels.fyi.