CodeSubmit Interview Series

The 10 Best Data Science Developer Interview Questions in 2023

Looking to hire your next data science developer? You’re in the right place. Today, you’ll get the best data science interview questions to ask at your next interview. Read on!

Interviewing data scientists 

Data scientists are in charge of compiling and analyzing big, structured and unstructured data sets. They use math, computer science skills, and statistics to understand large data sets. 

Data science experts are extremely sought-after. A data scientist includes data analytics, data mining, machine learning, Artificial Intelligence, Deep Learning, and so on in their work. They need to master many computer languages and statistical computations. What’s more, data scientists also need to have good interpersonal and communication skills. For instance, they need to be able to communicate statistical insights to make actionable suggestions.

Beginner data science interview questions

What are the best beginner data science interview questions? Here are the top questions you can use to interview junior data scientists. 

1. What are your favorite tools and devices in your role as a data scientist? 


You’ll need to understand what skill set your developer has. Some of the most common tools data scientists use are PythonSQL, and Tableau. While the specific tools data scientists use vary depending on their specialization, programs like MySQL, Excel, PowerBI, Bokeh, Plotly, and Infogram help them convert millions of data points into heat maps and chord diagrams. 

2. What is the difference between data science and data analytics?


Data science is the process of transforming data with different technical analysis methods to get insights for business scenarios. It focuses more on the future and new innovations. 

Data analysts, on the other hand, check existing hypotheses and information to answer questions for more effective decision-making. It focuses more on the present meaning of the existing historical context. 

3. What are the conditions for overfitting and underfitting?


Overfitting means that the model performs well for the sample training data, but new data doesn’t provide any results. These conditions happen because of low boars and high variance in the model. 

Underfitting, on the other hand, means that the model is so simple that it can’t identify the relationship in the data. As a result, it doesn’t perform well even with test data. Usually, this happens due to low variance and high bias. 

Senior data science interview questions

What are the best senior data science interview questions? Here are the top interview questions to help you assess your more senior candidates.

4. What is linear regression?


Linear regression is about understanding the linear relationship between dependent and independent variables. It’s a supervised learning algorithm and helps find the linear relationship between two variables. One is the independent variable or predictor and the other is the dependent variable or response. 

With Linear Regression, you try to understand how the dependent variable changes in terms of the independent variable. One independent variable is called a simple linear regression and more than one is known as multiple linear regression.

5. How do you avoid overfitting your model?


Overfitting means that a model is only set for a small amount of data and ignores the big picture. To avoid overfitting, keep the model simple, use cross-validation techniques, and use regularization techniques. 

To keep the model simple, take fewer variables into account and remove some of the noise in the training data. Cross-validation techniques refer to techniques such as k folds cross-validation and regularization techniques include techniques such as LASSO, which penalizes some model parameters if they are likely to cause overfitting.

6. Explain recommender systems.


Recommender systems predict how a user would rate a product based on their preferences. These systems can be divided into two areas:

Content-based filtering

For example, when a song is recommended to you with similar properties as another song you’ve listened to. 

Collaborative filtering

For example, you often see this on Amazon with the recommendation “Users who bought this also bought.”

7. What is Naive Bayes Classifier and how does it work?


Naive Bayes Classifier is an algorithm based on a probabilistic model, which works on the Bayes Theorem principle. Naive Bayes’ accuracy can be improved by combining it with other functions to make it a perfect Classifier. Bayes Theorem is a theorem that explains conditional probability. The scenario for this would be that you need to identify the probability of the occurrence of A if B has already happened.

8. What are the true-positive rate and the false-positive rate? 


True positive rate is a Machine Learning concept that means that true-positive rates are used to measure the percentage of actual positives that are correctly identified. It’s calculated by dividing True Positives with Positives. False positive rate is the probability of falsely rejecting the null hypothesis for a certain test. It’s calculated by deducting Positive Rate from False and then dividing this number by Negatives. 

9. Explain root cause analysis.


Root cause analysis was developed to analyze industrial accidents. Nowadays, it’s used in other areas. A factor is known as a root cause if its deduction from the problem-fault-sequence averts the undesirable event from occurring. Root cause analysis is a problem-solving technique that’s used for isolating the root causes of problems or faults. 

10. How regularly should an algorithm be updated? 


You should update an algorithm when you want the model to evolve as data streams through infrastructure, there is a case of non-stationarity, or the underlying data source is changing.

11. What are confounding variables?


Confounding variables are extraneous variables in a statistical model. They correlate directly or inversely with the dependent and the independent variable. The estimate can’t account for the confounding factor. 

Over to you!

There you have it. Now you know what data science interview questions to ask at your next interview.

While interviews help you assess a candidate, they’re just one part of your hiring process. You also need to assess developers’ skills. With CodeSubmit, you can do just that. Our assessment tests help you understand your candidates’ tech skills.

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