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    20 Common Data Science Interview Questions

    Interviewing for a data scientist role? Here are 20 common data science interview questions and tips to help you prepare.

    20 Common Data Science Interview Questions

    Written by  Rachel Pelta

    Edited by  Emily Courtney

    Updated on November 21, 2022

    By Rachel Pelta / November 21, 2022

    Getting ready for data science interview questions is, in some respects, no different than preparing for an interview in any other industry. You’ll research the company, prepare answers to common interview questions, and review your portfolio to use during the interview.

    However, preparing for a data science interview involves more than preparing for questions like “Why do you think you are qualified for this position?” Data scientist interviews include a lot of technical topics. And while you might be comfortable talking about your abilities, can you explain them in a way that makes sense to the hiring manager?

    Table of Contents

    Preparing for a Data Scientist Interview

    It’s not uncommon for a data scientist applicant to go through three to five interviews for the role. This can include a phone interview, Zoom interview, in-person interview, and panel interview.

    As you might expect, many of the interview questions will focus on your hard skills. However, you can also expect questions about your soft skills, as well as behavioral interview questions that assess both your hard and soft skills.

    >>MORE: BCG Data Science & Analytics

    Here’s how you can prepare for your data scientist interview.

    Go Back to Basics

    Start by brushing up on the fundamentals of data science. Review:

    Statistical analysis: collecting and analyzing large datasets to identify and uncover trends or cause and effect

    Data hygiene: cleaning and formatting raw data to ensure it’s accurate

    Coding: writing instructions in “computer speak.”

    Programming: creating the software or system that executes the coding

    Data modeling and visualization: presenting data visually to help establish the relationship between data points

    Jenna Bellassai, lead data reporter at Forage and former data scientist at Guru, advises applicants to “review fundamental programming and machine learning concepts. Be prepared to describe your contributions to previous projects.”

    While part of your interview prep likely involves researching the company, Bellassai says that to prepare for data science interview questions, you should also “think about what the company’s data may look like, what technical challenges they may face, and where machine learning models could play a role in their business. If you have experience with a niche technology or modeling approach that the company uses, be prepared to speak about it.”

    >>MORE: Data Science & AI at a Y Combinator Startup

    Review Possible Interview Topics

    Most interviews include questions about the specifics of the role, and a data scientist interview is no different. Bellassai says you can expect technical questions on these topics:

    Software engineering

    Data manipulation

    Statistical modeling (including machine learning topics)

    Architecture design

    Distributed computing

    Cloud architectures

    Working with specific types of data, like geospatial data

    Bellassai also notes that during the interview, you may have to solve a coding problem or draw an architecture diagram.

    10 Most Common Data Science Interview Questions

    While there are no guarantees, here are 10 interview questions you’ll likely encounter:

    What is the difference between supervised and unsupervised learning?

    What is the difference between data science and data analytics?

    Explain the steps in making a decision tree. How would you create a decision tree?

    You’re given a data set that’s missing more than 30% of the values. How do you deal with that?

    How do you/should you maintain a deployed model?

    How is data science different from other forms of programming?

    How often do you/should you update algorithms?

    What is the goal of A/B testing?

    What are the differences between overfitting and underfitting, and how do you combat them?

    What do you prefer using for text analysis?

    Bellassai also notes that while you should give your best answer to the question, there is no “right” answer. “Remember that there is no perfect solution. A particular approach isn’t necessarily the best just because you’ve used it before.”

    Bonus Round: 10 Common Behavioral Interview Questions for Data Scientists

    Technical skills aren’t the only kind of data science interview questions you’ll encounter. Like any interview, you’ll likely be asked behavioral questions. These questions help the hiring manager understand how you’ll use your skills on the job.

    While your answers will be specific to the role, use the STAR Method to tell a story about a time you put your skills to work and what the outcome was.

    Here are 10 behavioral questions you might encounter in a data scientist interview:

    Tell me about a time you used data to bring about change at a job.

    Have you ever had to explain the technical details of a project to a nontechnical person? How did you do it?

    What are your hobbies and interests outside of data science?

    Tell me about a time when you worked on a long-term data project. How did you approach collecting and analyzing data when different parts of the project had different deadlines?

    स्रोत : www.theforage.com

    SOLVED: A data scientist is writing a Machine Learning algorithm using a large data set. What is produced when the data scientist compiles the code?

    VIDEO ANSWER: Hello students- we are given a question here. A data scientist is writing a machine learning algorithm using a large data set. Okay, we need to say that calculate what we need to explain that what is produced when the data scientist

    स्रोत : www.numerade.com

    Christian Kästner :: CMU

    Christian Kästner

    [pronunciation and spelling]

    Associate Professor · Carnegie Mellon University · Institute for Software Research

    I am an associate professor in the School of Computer Science at Carnegie Mellon University. My current interests are in software engineering for software systems with ML components (or teaching software engineering to data scientists, "machine learning in production"), open-source sustainability, and software-supply-chain security. I am generally interested in understanding the` limits of modularity and complexity caused by variability in software systems, which naturally brings me to questions of quality assurance, interoperability, and feature interactions. My research combines rigorous empirical research with program analysis and tool building.

    I currently serve as the director of the CMU Software Engineering Ph.D. Program.

    Profiles: Curriculum vitae, Google Scholar, ACM, dblp.

    Software and Societal Systems Department (S3D)

    School of Computer Science

    Carnegie Mellon University

    Office: TCS 345

    Email: kaestner (at) cs.cmu.edu

    Mailing Address: C. Kaestner, S3D - TCS Hall 430, 4665 Forbes Avenue, Pittsburgh, PA 15213, USA

    News

    26 Oct. 2022

    Two postdoc positions available

    I'm looking for two postdocs, one each for (a) a project on performance analysis of configurable systems and (b) a project on software supply chain security. See our ICSE'22 paper "On debugging..." and our ICSE'21 paper "Containing malicious package updates..." as examples of the work in these two areas. Some research experience in either empirical software engineering or the specific field is useful. Email me if interested for more details.

    12 Oct. 2022

    Keynote: From Models to Systems: Rethinking the Role of Software Engineering for Machine Learning

    I was invited to give a keynote at MSR 2022 and used this to argue that we should invest in teaching software engineering to data scientists. This talk provides a good overview of how I think about teaching in this area and why I think that "software engineering for ML" is more of an education problem that a research problem. The remote version of the talk was recorded and is here on youtube:

    5 Oct. 2020

    Lecture Recordings: Software Engineering for AI-Enabled Systems

    All summer, I recorded all lectures of my class Software Engineering for AI-Enabled Systems. The students graciously consented in releasing those recordings, which can now all be found in a YouTube playlist under a creative commons license (like the rest of the course material):

    Also my annotated bibliography on the topic has seen some updates recently and I've written again about requirements engineering for production ML systems.

    Older News...

    Research Overview

    Software Engineering for AI-Enabled Systems

    We explore how different facets of software engineering change with the introduction of machine learning components in production systems, with an interest in interdisciplinary collaboration, quality assurance, system-level thinking, safety, and better data science tools: Capturing Software Engineering for AI-Enabled Systems · Interdisciplinary Collaboration in Engineering AI-Enabled Systems · Developer Tooling for Data Scientists

    Sustainability and Fairness in Open Source

    We study the dynamics of open source communities with a focus on unstanding and fostering fair and sustainable environments. Primarily with empirical research methods, we explore topics, such as open source culture, coordination, stress and disengagement, funding, and security: Sustainability and Fairness in Open Source · Collaboration and Coordination in Open Source · Adoption of Practices and Tooling

    Quality Assurance for Highly-Configurable Software Systems

    We explore approaches to scale quality assurance strategies, including parsing, type checking, data-flow analysis, and testing, to huge configuration spaces in order to find variability bugs and detect feature interactions: Variational Analysis · Analysis of Unpreprocessed C Code · Variational Type Checking and Data-Flow Analysis · Variational Execution (Testing) · Sampling · Feature Interactions · Variational Specifications · Assuring and Understanding Quality Attributes as Performance and Energy · Security

    Maintenance and Implementation of Highly-Configurable Systems

    We explore a wide range of different variability implementation mechanisms and their tradeoffs; in addition, we explore reverse engineering and refactoring mechanisms for variability and support developers with variability-related maintenance: Reverse Engineering Variability Implementations · Feature Flags · Feature-Oriented Programming · Assessing and Understanding Configuration-Related Complexity · Understanding Preprocessor Use · Tracking Load-Time Configuration Options · Build Systems · Modularity and Feature Interactions

    Working with Imperfect Modularity

    We explore mechanisms to support developers in scenarios in which traditional modularity mechanisms face challenges; among others, we explore strategies to complement modularity mechanisms with tooling: Virtual Separation of Concerns · Awareness for Evolution in Software Ecosystems · Conceptual Discussions

    स्रोत : www.cs.cmu.edu

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