The Data Science juggernaut has brought more information, more applications, more software to utilize than ever seen before. For candidates conducting a job search, defining yourself and your skills is critical in the data science interview. You can’t know every technique, application or software but those you do know you should be able to talk about intelligently and with good examples.
Paul Chatlos, Executive Recruiter in Data Science and Analytics, offers you ten interview questions he finds most commonly asked by his clients. Take time to actually write down your answers to these questions and prepare an example of your work associated with each question. You may not get asked these specific questions in your data science interview, but just the process of thinking through what you know and don’t know through these questions will make you more articulate for other questions.
You may ask, Why a machine learning section? It is just the most marketable, talked about skill right now and commonly comes up in the data science interview. If you don’t know it, try to get a project or consulting assignment in it.
What is the Central Limit Theorem?
What is Linear Regression? Provide a clear, concise example of a time you developed a regression model and how it impacted the business.
What programming languages and environments are you most comfortable working with?
How would you clean a data set?
Tell me about the coding you did on your last work project.
How does data cleaning play an important role in analysis?
In your opinion, what is more important when designing a machine learning model? Model performance or model accuracy?
What is the difference between supervised and unsupervised learning? Also, be able to provide a clear concise example of a project using both.
What are recommender systems?
Before an interview write down examples of work experience related to these topics: teamwork, leadership, conflict management, problem solving and failure.
Paul also offers up the following resources that have additional questions, and answers to those questions, for you to review. A word of caution, don’t use the answers given verbatim. Make sure any answer you give is YOUR answer. It should make sense coming out of your mouth and out of your experience. Don’t use an answer if you haven’t done it or don’t understand it. A savvy interviewer in your data science interview will know!
Springboard is a bootcamp educator with some great advice on their website. They have broken down their questions by statistics, programming, modeling, past behavior, culture fit and problem solving.
Edureka is another data science educator and their list of questions and answers is very up-to-date, last updated on 6/19/19, with a focus on some of the “hot” techniques. Question breakdown is basic data science, statistics, data analysis, machine learning, probability and deep learning.
DeZyre gives free “recipes” and training through a live interactive online platform with other data science professionals. Their questions and answers have three categories: programming, statistics and practical experience.
Towards Data Science is a $5/month membership community. Members submit blogs and “smart voices and original ideas take center stage – with no ads in sight.” This blog included some topics we didn’t see on the other three.