A collection of resources to help you ace your Data Science interview

Data science interview


Preparing for an interview can be nerve-wracking, so having a collection of resources that can help you ace all aspects of the interview can be a great help. Especially if the interview you’re preparing for is a data science role one!

Data science is not just one field but a collection of fields used together to build something unique. Data science is simultaneously maths, statistics, problem-solving, pattern finding, communications, and business.

Because of how broad and interconnected the field of data science is, taking any step in this field may seem so complex and complicated, from trying to learn your way through to job-hunting, looking for the correct role, and finally acing the interviews, but, despite the complexity of the field, if you have clear steps you can follow, getting into and getting a job in data science will not be so puzzling.

Once you learn the basics, develop some projects, explore different datasets, and build a decent portfolio, a new challenge arises.

How to be fully prepared to ace any interview, stand up among the crowd, and land the job role you’re seeking?

This article will walk you through different resources you can use and hopefully help you be fully prepared to ace your following interview and get the job. 


Although some aspects of data science don’t require any coding, the core of any data science application must be programmed. Since data science has many applications across various fields, different programming languages can be used to build projects. But, regardless of what programming language you’re using, you need to strengthen your coding skills to get a job in data science.

The good news is, often, in interviews, the company will allow you to use whatever programming language you’re most comfortable with to solve algorithmic coding interview questions. To prepare for such questions, you can use resources like LeetCode or HackerRank. It will also be helpful if you understand time complexity, Python tricks and tips, and data structures & algorithms.


Data science is all about maths and statistics. From probability theory to linear algebra, maths magic allows us to understand data, find trends and patterns, and build algorithms to predict future data science. Math and statistics are crucial for data science; they are always asked about in data science interviews.

The complexity of statistical questions will vary depending on the role you’re applying for. An entry-level job position will have more basic statistics questions, but a senior position will have statistics questions in practical and real-life scenarios. You can practice and refresh your knowledge using William Chen’s probability cheatsheet, common probability distributions, and these 40 questions on probability theory for data science.


You can only spell data science with the data. Therefore, an essential skill that every data scientist should master is handling data. All skills are used daily in every data science project, from data collection to cleaning to exploration and analysis. As soon as the interviewer tests your ability to code and think about the different algorithmic problems, they will give you data science problems to test your data handling skills.

You often can choose Python, R, and SQL to clean, explore and analyze a given dataset. My favorite resources for practicing are SQL questions, how to write efficient queries, and these Pandas exercises.


Machine learning is the core of many data science applications. Although you may be writing machine learning algorithms only sometimes on the job, you need to be very comfortable with the basic machine learning algorithms. In addition, you need to be able to suggest a machine-learning algorithm based on a specific dataset or a specific problem. Statement.

Excellent resources, including 100 days of machine learning code infographics, and walking through a machine learning problem.


Validation is one of the main steps of any data science project. Ensuring that your model behaves correctly is crucial for your companies and clients because any error may cause the loss of money and resources.

Although this aspect of the data science project is only sometimes asked about in interviews, it is valuable knowledge just in case they ask about it. Resources to review validation include A/B testing interview questions, what to avoid when running an A/B Test, type I vs. type II errors, and guidelines for A/B tests.


In addition to the questions about the specific building blocks of the field, you will always be asked general data science questions to test your ability to put those building blocks together and develop a complete project.

For this interview section, you will be given a real-life data science problem and asked to walk through all the steps of solving the given situation. Some great resources to go through are 120 data science interview questions, and 3 types of data science interview questions.

The data science job-hunting process is one of the most challenging job-hunting processes out there. Looking for job roles in data science can be difficult; one of the main reasons is the vagueness of the role titles and descriptions. In addition, they all seem to overlap, making it hard for people to be happy with the perfect role for their abilities.

This vagueness only makes preparing for the interview even more of a hassle. After all, how can you prepare for a vague role? However, by practising the basic building blocks of the field and then some general questions about the different algorithms, you have a robust and potent combination guaranteed to land you the job.

Going through the job hunting journey would be best if you remember never to give up; keep working on yourself and your skills, and your hard work will definitely pay off.

Good Luck.




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