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Analysis of Data Science Interview Questions

Breaking down data science interview questions by category

tl;dr: Unless the data science role is nuanced, most data science roles require fundamental knowledge about the basics of data science. (SQL, Coding, Probability and Stats, Data Analysis)

We analyzed hundreds of data science interview questions to find trends, patterns and topics that are the core of a data science interview.

At a high level, we divided these questions into different categories. We added a weight to each category. Weight of a category is simply the number of times we found a question occurring or repeating in the bucket from a random corpus of 100 questions.

From the pie chart above, categories like SQL, coding are non ambiguous. Machine learning basics consists of Linear/Logistic regression and related ML algorithms. Advanced ML consists of comparisons between multiple approaches, algorithms and nuanced techniques. Big Data technology includes big data concepts such as Hadoop, Spark and may include the infrastructure side/data engineering/deployment side of data science models. In essence, data science fundamentals are asked 70% of the time in a data science interview.

While SQL and coding based questions might be part of the initial online assessment, data analysis questions tend to be a take-home assessment. The remaining categories are usually covered during the phone/in-person interview and vary based on the role, company, years of experience and team composition.

Considering all this data, we designed a data science interview course to help people Ace Data Science Interviews. All the categories mentioned above will be covered in this course. The current cohort starts September 16, 2019. It will be a small group of 15 people. Sign up here!

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Analysis of Data Science Interview Questions was originally published in Acing AI on Medium, where people are continuing the conversation by highlighting and responding to this story.