Train and test yourself for data science interviews!

Photo by Annie Spratt on Unsplash

Since I started my data science journey, I have benefited from many open sources and blogs. I have learned a lot from these resources, now I have compiled the resources I used before for those preparing for the interviews. “I’m going to take interviews, but what am I supposed to study for?” as an answer to those who ask the question. These are the resources that I like on different subjects and prepare for the interview before I get a job. I will also suggest some methods for you to test yourself.

I have to say, these resources can vary depending on your strengths and the role you want to enter. Therefore, you can modify it as you wish and concentrate on a part of it. Remember that everyone’s strengths are different and data science is teamwork.

Statistic

Statistics is a very important topic in data science. If you don’t have a stats background you probably don’t know everything about it, that’s normal. Focus on the basics without worrying. You will learn the rest as you progress.

To review the main topics in theory:

Practice applying what you’ve learned. Solving statistics questions with hackerrank also improves your coding skills.

Python

If you want to work from scratch, this resource is for you:

If you have 1 month ahead of you, you can do a 30-day marathon.

My favorite is hackerrank. The questions are not very easy, but you can learn a lot with this method. So don’t worry, when there is a question you can’t solve, you can solve it by watching the video on YouTube. — It will always be useful to write the codes by looking at them without copy-pasting. — And you can look at different solutions in the discussion section.

SQL

Another important topic is SQL. You may need it for operations such as pulling and manipulating data. Therefore, they may be asked in interviews.

To see some sample questions:

For SQL you can practice with Kaggle.

ML & DL

The 30-day study found in this resource may be a good guide for you.

Another good guide is the excel file below. You can copy this file and add the source yourself. You can track your progress. You can rate the subjects you have difficulty with and study again later.

Behavioral & Approach

The way of approaching the problem and the behavioral part are as important as the technical knowledge.

My first suggestion is to think aloud. Even if you don’t know the answer, how you think is important. If you’re getting an answer, so is how you got it.

In addition, you can try to catch clues from the questions asked by listening well, and if you need detailed information, you can ask for detailed information. Here you can ask for hints or say “the answer might be x, I need to do some research for a more precise answer”.

There is more than one technical way of doing a job. That’s why the approach is very important in data science. You have to work on your way of going, your perspective, and the way you describe yourself. This topic is discussed in detail in the article below.

It is important in behavioral as well as technical issues. They may ask questions to find out what kind of person you are. It may also include issues such as going out to dinner/interviewing with the team, being invited to the office, asking for a personality inventory beforehand. It’s a difficult topic to foresee, but in the resource below, “What is your biggest regret at work?” 41 different questions are listed. Nice resource with possible answers.

Test Yourself

Photo by Jeremy Sheppard on Unsplash

To update our knowledge, it’s time to test ourselves.

For this, you can look at old interview questions from glassdoor.

You can set up a mock interview to practice. It is necessary to prepare for these with the seriousness of the real interview. The most important thing is to ask for feedback from the interviewer so that you can move forward with more solid steps to the next interview.

Now we come to workera.ai, my favorite resource for testing myself. I’m in love with it. You can prepare yourself for this test as suggested here.

The Skill Boost page explains how to prepare for workera.ai tests, ai interview types, and possible roles in a team. Sample questions and preparation methods are wonderfully explained below:

After taking the test, it gives you what stage you are at in different domains (Algorithmic Coding, Software Engineering, AI Literacy, Data Science, Deep Learning, Machine Learning, Mathematics, Business Analytics). In the Feedback tab, you can see what stage you are at, what is your score, are you improving, are you at the beginning, according to these fields.

In the Learning tab, there is a page that gives you a plan for which area you are weak in. For example, it offers you specific topics for system design and you can work on them over time.

A great resource for you to test yourself from different angles. It also creates a roadmap for learning and gives feedback. Simply great!

Photo by Franki Chamaki on Unsplash

I would also like to recommend the following blogs. These are also very useful resources. If you have any resource suggestions or suggestions you would like to add for those preparing for the interview, you can contribute by commenting. Also, if you want to talk to me in more detail, you can reach me on my superpeer account. Thank you!

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