6 Common Data Science Interview Questions

6 Common Data Science Interview Questions was originally published on Vault.

Data science is one of the fastest-growing career paths. And if you're looking to land a job in data science engineering this year, you'll need to be able to answer the following six interview questions—which cover a wide range of topics, including machine learning, statistics, and database design.

1. What’s the difference between data analytics and data science?

This question tests your understanding of these two areas and two related roles. To answer this question, you should first explain that data analysts and data scientists both analyze data. Then you want to discuss the differences, namely that data science is a more interdisciplinary field than data analytics. Data analysts provide decision support to business by compiling and displaying data in visual charts, while data scientists go one step further—developing and designing new processes and predictive models based on their analyses.

2. Can you explain root cause analysis?

First, you want to explain that root cause analysis is a method used to identify the underlying causes of problems or issues. For example, root cause analysis can be used in data science to determine the factors that contribute to a particular outcome. Ideally, when answering this question, you should also give a real-world example of how root cause analysis can be applied—for example, in medical mistakes, manufacturing mistakes, etc.

3. What’s the difference between data mining and machine learning?

To answer this question, you should explain that machine learning is a more advanced form of data mining. Machine learning algorithms can learn from data and make predictions, while data mining algorithms are only able to find patterns in data.

4. What’s your understanding of logistic regression?

Logistic regression is a statistical model used to predict the probability of a binary outcome. For example, it can predict the likelihood of a customer making a purchase. Another example is a doctor or nurse using it to indicate whether or not a patient will respond to a particular treatment. Note that an interviewer may ask you further questions about logistic regression to see if you know how to apply it in real-world settings.

5. Give me an example of when you would use neural networks.

Neural networks are machine learning algorithms designed to mimic the workings of the human brain. They are composed of a series of interconnected “nodes” that can learn to recognize input data patterns. This question will likely come up in an interview because neural networks are a hot topic in data science. However, they’re not always the best solution to every problem. Therefore, if an interviewer asks you this question, they want to know if you understand when neural networks are appropriate and when they’re not.

6. How would you handle imbalanced data?

For data scientists, “imbalanced data” refers to datasets where one class is significantly more represented than the other. For example, imagine you were trying to build a model to predict whether or not a patient has cancer. The dataset might be imbalanced if there are significantly more patients without cancer than with cancer. This issue can pose a challenge because most machine learning algorithms are designed to work best when the classes are balanced. They tend to learn from patterns in the data—if one type is much more represented than the other, the patterns may be skewed.

If you’re asked this question in an interview, the interviewer wants to know how you would deal with a situation like this. There are a few different ways to handle imbalanced data, so it's essential to have a solid understanding of the pros and cons of each approach.

George Tsagas is the owner of eMathZone. He is a software engineer with a computer science degree and deep love for math.