The Top 4 Online Tutorial Platforms for Data Science Interviews


The top four online platforms for preparing for data science interviews

For the past ten years, I’ve worked in the area of data science. In various roles, such as business analytics, growth analytics, marketing analytics, and marketing science, I’ve worked as a data scientist. I’m more aligned to a product these days, but I still interview data scientists on a regular basis.

I’ve gathered four online tools that have always helped me prepare for data science interviews over the years. I’d like to present them to you.

If you choose to watch rather than read, here is a link to the video.

Tips for Data Science Interviews
Let’s start with an overview of the data science interview style.

Data Science Interview Format
Understanding the various types of subjects that will be discussed during the interview is the first thing to do and one of the data science interview tips.

During a data science interview, you can expect to be asked about a variety of subjects. The recruiter should inform you of the subjects that will be discussed during the interview as well as the structure of the interview.
Your first technical interview, for example, could be conducted via video conference. As a result, you’ll be sharing a computer with the interviewer where you’ll be able to write and show them your code. Then there’s the possibility that your second interview will be in person, where you’ll meet multiple data scientists and be interviewed by four to five different interviewers on four to five different topics. It’s important to comprehend all of those subjects so that you can adequately prepare for those interviews.

During the data science interview, four topics were discussed.
The data science interview, for the most part, covers four subjects.

  1. Coding
  2. Product Sense / Business Cases
  3. Statistics and Probability
  4. Modeling Techniques

The very first is coding. And there’s the meaning of the commodity or the market scenarios. The third category is mathematics, which includes probability and the base theorem. The final subject may be modeling, which would include models such as Random Forest, Gradient boosting, K-means clustering, and other similar models.

To reinforce all of the topics that will be discussed in an interview, I want to practice as many problems as possible. The second item is preparation issues and interview questions that are specific to that particular interview and organization. That’s what I’ll be on the lookout for. That’s what I’m looking for: real questions that have been asked at previous interviews.

Prepare for your data science interview with these four online resources.

  1. Lots of Practice Problems
  2. Real Questions from Real Interviews

I want to practice as many problems as I can to reinforce all the concepts that will be covered in an interview. And the second thing is practice problems and interview questions that are very relevant for that interview and for that company. That’s what I’ll be looking for. Real questions that have been asked at interviews in the past, that’s what I’m aiming to get.

Prepare for your data science interview with these four online resources.
Here are four online tools I’ve used to train for data science interviews in the past. Finally, I’ll introduce you to a fifth one as a treat.

As a Learning Platform, Glassdoor
Glassdoor is the first online resource. When I’m planning for a data science interview, it’s one of my favorite platforms to use.

Glassdoor is depicted in the picture above. I typed in Facebook and chose interviews from the drop-down menu. We can also find interview questions for various positions at Facebook if we scroll down to the middle of the list. We see roles like data scientist, front end engineer, iOS developer, software engineer, and so on as we scroll down. However, there are numerous data science interview questions available. And what I like about this is that these are actual questions that were posed during the interview, actually during a Facebook data science interview. They’re as genuine as it gets.

The second thing I like about this is that it covers all of the issues we covered previously. In addition to typing in Facebook and looking at all of the questions there, if I’m preparing for a data science interview at Facebook, I’ll check for other companies in the same industry as Facebook. I could type in Snapchat or TikTok if I’m more interested in social media companies. If I want to broaden my search, I might type in other tech companies. Another example will be Google, and another might be LinkedIn. So Glassdoor will provide you with a large number of interview questions and answers. is a non-profit organisation dedicated to For Probability and Statistics

Let’s talk about statistics and probability now. is a website that I’ve used. It is, in reality, a math website. They also cover a variety of other subjects, such as computer programming, computer science, and quantitative finance, to name a few. However, I use this to review my statistics and probability. This was also a website that Facebook recruiters suggested for more statistics and probability practice.
I use the above menu to navigate to the practice section of this website. There are several options available. But it’s probability range, as seen in the image below, that I’m most interested in.

The fundamentals and casino probability are also available in the probability section. Basically, I want to give it my everything. And after I’ve delved into a few of these practise issues, I’ll be able to tell whether or not they’ll be included in the data science interview.
I would suggest a few pages on the Brilliant website: probability, random variables, statistical research, and distributions.

Going on Glassdoor first is a safe way to double-check whether or not you should be answering these questions. Read through some of the statistical issues to see if those concepts are covered on Brilliant.
This is what I would advise you to do. This is the place to go if you want to develop your statistics and probability skills.

Modeling Techniques Can Be Learned From a Variety of Sources
Now we’ll look for modeling tools, specifically machine learning models, on the internet. I don’t have a single go-to resource for learning about machine learning models or brushing up on those concepts. Modeling questions have come up in two forms in interviews:

  1. Theory
  2. Application of Models on Projects

The Use of Models in Projects
The theoretical questions are the first method. The second method is to discuss your designs. They inquire about the models you used and implemented on those projects, either implicitly or explicitly.

You may be asked about particular models, such as random forest, gradient boosting, and k-means clustering, as well as the models themselves.
You may be asked, for example, why would you want to use this model?
Why wouldn’t you want to use this strategy?
What is the best way to code this model?
How should the effects of this model be interpreted?
These are the most common theoretical questions you’ll encounter when it comes to modeling.

The Use of Models in Projects

You may be discussing a project that used one of these models. An interviewer will delve deeper and deeper into the project and the model, asking you questions about why you chose that model or what assumptions you made when creating the model.
The argument is that you must be familiar with machine learning theory. You just need to be familiar with the most popular machine learning models.

A few online resources that I use:

I read a lot of journals, so that’s one of the online tools I use. And one of my favorite websites is Towards Data Science. They have a lot of machine learning posts on their blog.
I go and YouTube to watch as many videos as I can in addition to reading blog posts to catch up on my machine learning theory and understanding. I would suggest a few outlets, like ‘Simplilearn’ and a new guy called ‘Data Professor.’ They talk a lot about machine learning models, both theory and implementation, which is important.
I check machine learning models and get as many videos as I can, in addition to following some of these channels.

LeetCode for the Interview Coding Section

The online tool that I used for the coding portion of the interview was called ‘ LeetCode.’ And, in essence, this is a forum designed to help computer scientists and software developers prepare for their interviews. They do, however, have a fun little database section with questions to help you practise your SQL.
If you click on any topic, you will be taken to a practice question where you can type in SQL code and then execute it to see what happens. It’s a full-featured workspace with an integrated development environment (IDE) for practising MySQL queries.

The fact that there are hundreds of practice SQL problems is something I like about this site. For the interview, I can get really good at just improving my SQL and coding skills.
LeetCode’s only drawback, in my opinion, is that it is and was designed for software developers. Many of the questions on this forum are designed to help you improve your SQL skills, but they don’t ask data science or data-related questions. So, I’d go to Glassdoor and try to find some coding questions for the organization I’m interviewing with, and then figure out what data science topics are addressed in those coding questions. Then, if I just wanted to do some SQL practice, I’d go to LeetCode and try to answer as many questions as possible.

BONUS — StrataScratch for Data Science Interview Preparation

This is ‘StrataScratch,’ the fifth online resource I promised you at the beginning. And it’s a forum that was created with one goal in mind: to assist data scientists in preparing for their interviews.
This is a framework designed with data scientists in mind. And it incorporates the best features of the four online services I listed earlier into a single platform.

We have coding questions on StrataScratch, and you can choose the questions based on the organisation. You can also choose which questions you want to answer depending on whether you’re more comfortable with SQL or Python. Then, if you want to click into one of them, you will be presented with the questions, a hint, and the ability to see answers as well as those of other users.
These are real-life questions gleaned from data science interviews. You should be certain that anything you’re doing corresponds to a data science interview.

Now, if you go to non-coding questions, you’ll find a variety of technical questions.
During a data science interview, you’ll be checked on probability, business cases, product sense questions, modelling questions, statistics, miscellaneous technical questions, system architecture, and a variety of other topics.
When you click on one of these questions, you’ll see the issue, an editor where you can comment and provide answers, and then other users’ solutions.

There are over a thousand interview questions on StrataScratch that are taken from real businesses for you to work on, both coding and non-coding questions.
These are the five online resources that will assist you in preparing for your data science interview. I hope you found this information useful.