Introduction
Are you aspiring to become a data scientist, but struggling to crack the interviews? Well – you’re not alone! Getting a break in the data science field can be difficult. Doubly so, if you’re coming from a non-data science background (which in all likelihood you are).
The stories you hear from other aspiring data scientists can make interviews feel more intimidating and daunting. So you better be prepared before facing the interviews.
What kind of questions can be asked? How can you prepare and what are the resources you should refer to? What is the structure of a typical data science interview? How should your body language be? These are just some of the questions you’ll have in mind.
Don’t worry – you’re in the right place!
I have been there as well. Believe me – the only way to crack data science interviews is through sheer practice and through the quality of your work. So, make sure you have a portfolio of projects you have worked on.
In addition, practice what a typical interview might look like. This will be the focus of this article.
In this article, we provide you with a comprehensive list of questions, case studies and guesstimates asked in data science and machine learning interviews. We have also listed additional resources including handy tips and tricks to guide you through your interview process and come out on the other side successfully.
This is the ultimate resource guide you can find. You should bookmark this page for every time you have to prepare for an interview.
Happy learning and all the best!
Table of Contents
- Data Science and Statistics Questions
- Machine Learning Questions
- Deep Learning Questions
- Case Studies
1. Data Science and Statistics Questions
This section is meant to test, enhance and improve your data science and statistics concepts. From probability to correlation, linear and regression to logistic regression, your concepts will be set in stone by the time you reach the last question!
1.1 40 Interview Questions asked at Startups in Machine Learning/Data Science
This is a list of 40 plausible & tricky questions which are likely to come across your way in interviews. If you can answer and understand these questions, rest assured, you will give a tough fight in your job interview. The key to answering these questions is to have a concrete practical understanding of ML and related statistical concepts.
1.2 40 Questions on Probability for Data Science
Probability is considered the backbone of quite a few data science concepts and techniques. You will need to have a good grasp on this subject in order to have a chance to land a data science role. These questions will test how well you know the probability.
1.3 7 Most Commonly Asked Questions on Correlation
Correlation is one of the core concepts in data science. It seems easy from the outside but it has its own tricky features. If you are learning statistical concepts, you are bound to face these questions which most people try to avoid. For folks who are well versed with statistics, this will be a good refresher.
1.4 41 Questions on Statistics for Data Scientists and Analysts
Your statistical concepts should be rock solid before you go for an interview in this field. To help you improve and test your knowledge of statistics, we have put together this list of questions. The article covers both descriptive and inferential statistics along with explanations for each question.
1.5 30 Questions to Test a Data Scientist on Linear Regression
Linear Regression is still one of the most prominently used statistical techniques in the data science industry and in academia to explain relationships between features. It is a technique you absolutely MUST KNOW inside out if you want to become a data scientist.
1.6 30 Questions to test your Understanding of Logistic Regression
Logistic Regression is likely the most commonly used algorithm for solving all classification problems. The questions in this article are specially designed for you to test your knowledge of logistic regression and its nuances.
2. Machine Learning Questions
Machine learning has become central to a lot of organizations strategies. If you want to carve a career for yourself in this field, you should be prepared to face the hard questions. This section will definitely test your ML techniques to the limit.
2.1 40 Questions to test a Data Scientist on Machine Learning
If you are a data scientist (or an aspiring one), then you need to be good at Machine Learning – no two ways about it. These questions have been designed to test your conceptual knowledge of machine learning and make you industry ready. Get ready to test yourself!
2.2 30 Questions to test a Data Scientist on Natural Language Processing
Natural Language Processing (NLP) is the science of teaching machines how to understand the language we humans speak and write. It is a very upcoming field in machine learning. Organizations are waking up to the power of how ML can be used to gain actionable insights from the text. Go through these questions and see how well versed you are with NLP.
2.3 30 Questions to test a Data Scientist on Tree-Based Models
Decision Trees are one of the most respected algorithms in machine learning and data science. They are transparent, easy to understand, robust in nature and widely applicable. You can actually see what the algorithm is doing and what steps does it perform to get to a solution. This trait is particularly important in a business context when it comes to explaining a decision to stakeholders, which makes an integral part of the interview process as well.
2.4 25 Questions to test a Data Scientist on Support Vector Machines
You can think of machine learning algorithms as an armoury full of axes, sword and blades. You have various tools, but you ought to learn to use them at the right time. ‘Support Vector Machines’ is like a sharp knife – it works on smaller datasets, but on them, it can be much stronger and powerful in building models. Test yourself with these 25 questions to enhance your knowledge of this wonderfully adept technique.
2.5 40 Questions to test a Data Scientist on Dimensionality Reduction Techniques
One of the most common questions in interviews is based on how you will deal with a massive dataset that consists of millions of rows and thousands of columns. Knowing dimensionality reduction techniques and when to use them comes in handy in these cases.
2.6 40 Questions to test a Data Scientist on Clustering Techniques
Clustering plays an important role to draw insights from unlabeled data. It classifies the data in similar groups which improves various business decisions by providing a meta-understanding. It is used in industries like marketing, finance and many others. It’s another must-know concept you should have a good grasp on.
3. Deep Learning Questions
Deep learning is the hottest research field in the industry right now. It has led to amazing innovations, incredible breakthroughs, and we are only just getting started! But jobs in this field are few and far between. If you manage to land an interview, you need to be completely prepared for the hard questions – there is no easy way out when you work in the deep learning domain. This section will tell you how prepared (or not) you are to apply and sit for these interviews.
3.1 45 Questions to Test a Data Scientist on the Basics of Deep Learning
This is a relatively easier set of questions that are MUST-KNOW if you wish to work in deep learning. Before you go further down this section, take this quiz first and then see where you stand. If you don’t understand a concept, the article has links to resources where you can learn them. Get going!
3.2 30 Questions to test a Data Scientist on Deep Learning
This is as good a place to start as any to test your deep learning knowledge. This contains basic as well as advanced questions. When we released this quiz, most people clearly took it without having an inherent knowledge of the subject. Can you do better? Go for it!
3.3 40 Questions to Test a Data Scientist on Deep Learning
This article carries on from the above one. It will test your conceptual knowledge of deep learning.
3.4 25 Questions to test a Data Scientist on Image Processing
When it comes to deep learning, image processing is the leading domain right now. With big players like Google and IBM launching automated platforms to build image classification models, the interest in this field is pretty high. The questions in this article are specially designed for you to test your knowledge on how to handle image data, with an emphasis on image processing.
3.5 12 Frequently Asked Questions on Deep Learning
While these are not specifically interview-based, you should have a comprehensive answer for each of these 12 questions. These are some of the most basic questions around deep learning and should be on your fingertips.
4. Case Studies
Case studies are an integral part of the data science interview process. The hiring manager will be sure to check how you structure your thinking when faced with a case study. Ensure you go through the below case studies in detail. Before you see the solutions, first solve the problem yourself and then check your answers.
4.1 Solve Interview Case Studies 10x Faster using Dynamic Programming
Dynamic Programming isn’t a trick or a mathematical formula which delivers correct answer just by providing the inputs. Rather, it’s a combination of structured thinking & analytical mindset which does the job. The concept is an old one, yet used by just a few of us. Learn this unique approach and your interviewer will be bowled over!
4.2 Case Study for Analytics Interviews – Dawn of Taxi Aggregators
Taxi aggregators have become a MASSIVE deal in certain parts of the country. In this article, we’ll solve a case study of taxi aggregators. Alongside this, we will also focus on the essentials required for solving a case study like a pro. Consulting firms like Bain, BCG and McKinsey prefer candidates who think like a pro when given any case study. Let’s make you one.
4.3 An Analytics Interview Case Study
This is a classic route optimization problem. You are given data about alternate roads and have to figure out possible routes that minimize the time taken to travel. As you answer each question, you are provided with more and more data to dive deeper into the case study. This is exactly how it happens in the interview room so strap in!
4.4 Case Study for Freshers: Call Center Optimization (Level: Medium)
In this article, we will look at a real-life case in the form of a call centre optimization problem. This case study will give you a good feel of how to simulate an entire environment in such an operation intensive function. The codes mentioned here are in R but even if you don’t know the tool, you can work out the problem in Excel.
4.5 Case Study: Optimize the Price of Products for an Online Vendor (Level: Hard)
This case study is a classic because of its applications in the real world. The objective of this case study is to optimize the price level of products for an online vendor. The calculations which you’ll need to perform are ones which often take place in real life. Therefore, it’s not just mathematical, but practical too. For experienced job roles, similar case studies often appear in job interviews. So, give your best attempt!
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