Are you interviewing for a data scientist position? Do you feel ready? Data scientist interviews are known to be some of the toughest and most technical interviews in the industry. But don’t worry! You can prepare yourself for your interview by knowing what types of questions are typically asked.
It’s a good time to get into a career as a data scientist. With businesses across the world trying to get better at using data, there are plenty of companies willing to hire data scientists to help them. It’s also a great time for people who want to break into the data science industry. More than ever before, companies need professionals who can turn data into insights and drive value for their respective businesses. If you’re looking to learn more about what types of questions are asked at interviews for data science jobs, you’ve come to the right place
Describe your experience with data science
This is a pretty straightforward question. It would be good to start off with why you wanted to get into the data science field and how you learned data science.
Next, you could tell the interviewer how many years of professional experience that you have in data science and explain how you’ve used it in various projects. Make sure you’re specific about the type of projects you’ve worked on. For example, you can state that you’ve applied data science techniques to data visualization, predictive modeling, and recommendation engine projects. It would be good to have a digital portfolio to show the work you’ve done.
You could also discuss how you’ve used specific programming languages like Python, SQL, R, C++, Julia, Scheme, Haskell, Clojure, etc. If there’s a language that you know about but haven’t actually used it in any work projects, explain to them that you can get up to speed on other languages quickly. Additionally, you can enroll in a course where you could learn to apply the new language to real-world projects.
What is the difference between univariate, bivariate, and multivariate analysis?
This is a classic data science interview question. It’s also one that will get you thinking about the fundamentals of data science. Data science is about creating new data algorithms and models, as well as using statistical techniques to analyze data. There are many ways to analyze data and each of these ways gives you a different set of answers.
Univariate analysis involves looking at data on a single variable.
Bivariate analysis involves looking at the relationship between two variables.
Multivariate analysis involves looking at the relationship between multiple variables.
Most data science practitioners will spend the majority of their time performing univariate or bivariate analyses. It’s rare to find a data scientist who spends too much time performing multivariate analyses because they are extremely difficult to conduct properly.
What is root cause analysis?
A root cause analysis is a process that helps you figure out the underlying issue causing a particular problem. To do this, you have to find the root cause of the issue, instead of simply fixing the symptoms. For instance, if you’re trying to figure out why your website isn’t receiving enough traffic, a root cause analysis will help you figure out why people are leaving your site and not coming back. In other words, it seeks to identify the underlying reason for a problem.
What are resampling methods, and why they are useful?
Resampling methods allow you to perform random sampling with replacement. If you are asked this question, it’s likely that your interviewer expects you to understand the importance of statistical methods, and the value they bring to the data science field.
If you are asked this question, show that you know how to use resampling methods, and what use cases they are best suited for.
How do you evaluate the quality of a data set?
Data quality is one of the most important aspects of data science and data analysis. To get the most out of your data, you need to make sure it is of the highest possible quality.
You should be able to answer this question by explaining how you evaluate the quality of data. You should point out the importance of extracting data from multiple sources and performing quality checks.
Conclusion
Data Scientists are a valuable resource for an organization, and their expertise is in high demand. The data science field is changing so rapidly that it can be challenging to know what to prepare for during these interviews. These 5 data science interview questions should help you be more prepared for your interview, and help you land your next data scientist job.