Top data science interview questions and answers for 2022

So, you’re ready to be a data scientist. You’ve done the work. You’ve got the skills. But first, you need to face that dreaded job interview. But how does one prepare for this? Ace your interview and score a job in this technical industry by following these tips.

What do data scientists do?

Data scientists play a really important role for many companies. It is highly technical and involves critical thinking as well as creative problem solving. You need to not only know how to correctly operate the tools used to extract data, but also be able to later interpret that data. 

A few key tasks that data scientists may do on a daily basis include:

1. Identifying issues with data analytics and making recommendations for better ways to organise information.
2. Collecting data from a variety of sources and organising it in a straightforward manner. The data is usually presented to others in the company with less technical knowledge. Data scientists should have the soft skills, such as communication, to break down information, so it is understandable for fellow co-workers and managers. 
3. Creating optimal models and algorithms to gain and store data. 
4. Analysing data and reporting on patterns and trends.

You may be asked about the tasks you foresee undertaking during your job interview. So, it’s important that you have a good understanding of the day-to-day tasks involved as a data scientist.

Is data science a good career?

Data science is a great career for those who love learning and want to keep growing in throughout their entire career. It is an industry that has seen tremendous growth over the last few years, and as technology progresses, will only continue to develop. Here are some benefits of making a career out of data science.

1. Great salary prospects: Data scientists are in-demand. Because of this, you should be renumerated well with the current median salary sitting at $100k.
2. Opportunities worldwide: Data scientists are needed all over the world, not just Australia. So, if you have itchy feet and want a change of scene, data science can be a great career to see the world and experience different cultures.
3. No day is ever the same: You won’t be doing the same work every day. Data science is a varied role. You could be doing anything from presenting data to stakeholders, to making strategies to find the best ways to collect data.

How to prepare yourself for a job in data science?

To find success in a data science career, you need to have the technical skills required for the role. You also need a good understanding of professional etiquette and mannerisms to flourish in a workplace environment. Here are some things to consider when looking for a new job. 

1. Make sure you understand what the job entails

You can find out what you will do in a job by reading the position description. You can address how you have experience in each task by providing examples in your cover letter.

2. Know what you’re going to say in an interview

You will find yourself being asked general questions that are often brought up in an interview. Some examples include:

• What are your strengths and weaknesses?
• Where do you see yourself in 5 years?
• Tell us about a time you solved a problem?

It’s always good to plan out how you will answer these common questions. They are absolutely bound to come up at any interview. But of course, you are also very likely to be asked questions specifically related to data science. These can be about any topic like coding, probability, or statistics. Here are some common questions and how to best answer them:

Why is data cleaning important for analysing data?

Data cleaning is important as data can come from multiple sources, making it difficult to work with when ‘raw’. Cleaning data means that you will discover a more accurate result after analysis.
 

What is A/B testing and why do we use it?

A/B Testing is a technique that allows an organisation to project user experiences. This kind of testing is done to strategise the best way to understand user preferences.  
 

Is randomisation necessary when doing experimental design?

Randomisation is important when doing experimental design. It eliminates bias, creates a clearer causal relationship between explanatory variables and response variables, and automatically controls all lurking variables. 

3.  If you are finding yourself struggling, identify your pain points and get help.

If you find yourself nervous or feeling unprepared for the interview, you can ask a family or friend to do practice interviews with you. If you are struggling to answer data-related interview questions, you may need a refresher in data science - you can regain skills by completing a short course in Information Technology. 

Short courses and bootcamps in data science can build industry experience and help propel you into a career in data science. They look great on your CV and can help you better communicate your skills at job interviews.

Give your data science career a boost today – enroll in a data science or data science with python course at Upskilled.   

 

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