Data Science Interview Questions: What The Experts Say

Guest Blog our consultant managing the role
Author: Guest Blog
Posting date: 8/22/2019 9:13 AM
Our friends at Data Science Dojo have compiled a list of 101 actual Data Science interview questions that have been asked between 2016-2019 at some of the largest recruiters in the Data Science industry – Amazon, Microsoft, Facebook, Google, Netflix, Expedia, etc. 

Data Science is an interdisciplinary field and sits at the intersection of computer science, statistics/mathematics, and domain knowledge. To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it reflects in the interview. They've divided the questions into six categories: 

  • Machine Learning
  • Data Analysis
  • Statistics, Probability, and Mathematics
  • Programming
  • SQL
  • Experiential/Behavioural Questions

Once you've gone through all the questions, you should have a good understanding of how well you're prepared for your next Data Science interview.

Machine Learning


As one will expect, Data Science interviews focus heavily on questions that help the company test your concepts, applications, and experience on machine learning. Each question included in this category has been recently asked in one or more actual Data Science interviews at companies such as Amazon, Google, Microsoft, etc. These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview. 

  • Explain Logistic Regression and its assumptions.
  • Explain Linear Regression and its assumptions.
  • How do you split your data between training and validation?
  • Describe Binary Classification.
  • Explain the working of decision trees.
  • What are different metrics to classify a dataset?
  • What's the role of a cost function?
  • What's the difference between convex and non-convex cost function?
  • Why is it important to know bias-variance trade off while modeling?
  • Why is regularisation used in machine learning models? What are the differences between L1 and L2 regularisation?
  • What's the problem of exploding gradients in machine learning?
  • Is it necessary to use activation functions in neural networks?
  • In what aspects is a box plot different from a histogram?
  • What is cross validation? Why is it used?
  • Can you explain the concept of false positive and false negative?
  • Explain how SVM works.
  • While working at Facebook, you're asked to implement some new features. What type of experiment would you run to implement these features?
  • What techniques can be used to evaluate a Machine Learning model?
  • Why is overfitting a problem in machine learning models? What steps can you take to avoid it?
  • Describe a way to detect anomalies in a given dataset.
  • What are the Naive Bayes fundamentals?
  • What is AUC - ROC Curve?
  • What is K-means?
  • How does the Gradient Boosting algorithm work?
  • Explain advantages and drawbacks of Support Vector Machines (SVM).
  • What is the difference between bagging and boosting?
  • Before building any model, why do we need the feature selection/engineering step?
  • How to deal with unbalanced binary classification?
  • What is the ROC curve and the meaning of sensitivity, specificity, confusion matrix?
  • Why is dimensionality reduction important?
  • What are hyperparameters, how to tune them, how to test and know if they worked for the particular problem?
  • How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession, and gender? Define a machine learning algorithm for this.
  • How will you inspect missing data and when are they important for your analysis?
  • How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?
  • What are time series forecasting techniques?
  • How does a logistic regression model know what the coefficients are?
  • Explain Principle Component Analysis (PCA) and it's assumptions.
  • Formulate Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) techniques.
  • What are neural networks used for?40. Why is gradient checking important?
  • Is random weight assignment better than assigning same weights to the units in the hidden layer?
  • How to find the F1 score after a model is trained?
  • How many topic modeling techniques do you know of? Explain them briefly.
  • How does a neural network with one layer and one input and output compare to a logistic regression?
  • Why Rectified Linear Unit/ReLU is a good activation function?
  • When using the Gaussian mixture model, how do you know it's applicable?
  • If a Product Manager says that they want to double the number of ads in Facebook's Newsfeed, how would you figure out if this is a good idea or not?What do you know about LSTM?
  • Explain the difference between generative and discriminative algorithms.
  • Can you explain what MapReduce is and how it works?
  • If the model isn't perfect, how would you like to select the threshold so that the model outputs 1 or 0 for label?
  • Are boosting algorithms better than decision trees? If yes, why?
  • What do you think are the important factors in the algorithm Uber uses to assign rides to drivers?
  • How does speech synthesis works?

Data Analysis


Machine Learning concepts are not the only area in which you'll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions. We're grouping all such questions under this category. Data Analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual Data Science interviews at the companies listed above.  

  • What are the core steps of the data analysis process?
  • How do you detect if a new observation is an outlier?
  • Facebook wants to analyse why the "likes per user and minutes spent on a platform are increasing, but total number of users are decreasing". How can they do that?
  • If you have a chance to add something to Facebook then how would you measure its success?
  • If you are working at Facebook and you want to detect bogus/fake accounts. How will you go about that?
  • What are anomaly detection methods?
  • How do you solve for multicollinearity?
  • How to optimise marketing spend between various marketing channels?
  • What metrics would you use to track whether Uber's strategy of using paid advertising to acquire customers works?
  • What are the core steps for data preprocessing before applying machine learning algorithms?
  • How do you inspect missing data?
  • How does caching work and how do you use it in Data Science?

Statistics, Probability and Mathematics


As we've already mentioned, Data Science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for Data Science, and these topics are always brought up in data science interviews. Here is a list of statistics and probability questions that have been asked in actual Data Science interviews.

  • How would you select a representative sample of search queries from 5 million queries?
  • Discuss how to randomly select a sample from a product user population.
  • What is the importance of Markov Chains in Data Science?
  • How do you prove that males are on average taller than females by knowing just gender or height.
  • What is the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP)?
  • What does P-Value mean?
  • Define Central Limit Theorem (CLT) and it's application?
  • There are six marbles in a bag, one is white. You reach in the bag 100 times. After drawing a marble, it is placed back in the bag. What is the probability of drawing the white marble at least once?
  • Explain Euclidean distance.
  • Define variance.
  • How will you cut a circular cake into eight equal pieces?
  • What is the law of large numbers?
  • How do you weigh nine marbles three times on a balance scale to select the heaviest one?
  • You call three random friends who live in Seattle and ask each independently if it's raining. Each of your friends has a 2/3 chance of telling you the truth and a 1/3 chance of lying. All three say "yes". What's the probability it's actually raining?
  • Explain a probability distribution that is not normal and how to apply that?
  • You have two dice. What is the probability of getting at least one four? Also find out the probability of getting at least one four if you have n dice.
  • Draw the curve log(x+10)

Programming


When you appear for a data science interview your interviewers are not expecting you to come up with a highly efficient code that takes the lowest resources on computer hardware and executes it quickly. However, they do expect you to be able to use R, Python, or SQL programming languages so that you can access the data sources and at least build prototypes for solutions.

You should expect a few programming/coding questions in your data science interviews. You interviewer might want you to write a short piece of code on a whiteboard to assess how comfortable you are with coding, as well as get a feel for how many lines of codes you typically write in a given week. 

Here are some programming and coding questions that companies like Amazon, Google, and Microsoft have asked in their Data Science interviews. 

  • Write a function to check whether a particular word is a palindrome or not.
  • Write a program to generate Fibonacci sequence.
  • Explain about string parsing in R language
  • Write a sorting algorithm for a numerical dataset in Python.
  • Coding test: moving average Input 10, 20, 30, 10, ... Output: 10, 15, 20, 17.5, ...
  • Write a Python code to return the count of words in a string
  • How do you find percentile? Write the code for it
  • What is the difference between - (i) Stack and Queue and (ii) Linked list and Array?

Structured Query Language (SQL)


Real-world data is stored in databases and it ‘travels’ via queries. If there's one language a Data Science professional must know, it's SQL - or “Structured Query Language”. SQL is widely used across all job roles in Data Science and is often a ‘deal-breaker’. SQL questions are placed early on in the hiring process and used for screening. Here are some SQL questions that top companies have asked in their Data Science interviews. 

  • How would you handle NULLs when querying a data set?
  • How will you explain JOIN function in SQL in the simplest possible way?
  • Select all customers who purchased at least two items on two separate days from Amazon.
  • What is the difference between DDL, DML, and DCL?96. Why is Database Normalisation Important?
  • What is the difference between clustered and non-clustered index?

Situational/Behavioural Questions


Capabilities don’t necessarily guarantee performance. It's for this reason employers ask you situational or behavioural questions in order to assess how you would perform in a given situation. In some cases, a situational or behavioural question would force you to reflect on how you behaved and performed in a past situation. A situational question can help interviewers in assessing your role in a project you might have included in your resume, can reveal whether or not you're a team player, or how you deal with pressure and failure. Situational questions are no less important than any of the technical questions, and it will always help to do some homework beforehand. Recall your experience and be prepared! 

Here are some situational/behavioural questions that large tech companies typically ask:   

  • What was the most challenging project you have worked on so far? Can you explain your learning outcomes?
  • According to your judgement, does Data Science differ from Machine Learning?
  • If you're faced with Selection Bias, how will you avoid it?
  • How would you describe Data Science to a Business Executive?

If you're looking for new Data Science role, you can find our latest opportunities here. 

This article was written by Tooba Mukhtar and Rahim Rasool for Data Science Jojo. It has been republished with permission. You can view the original article, which includes answers to the above questions here. 

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Data Science For Business Decision Making

All strong and successful businesses are built and run upon well-informed decision-making, which derive from a mix of leader experience, industry knowledge and, more recently, the regular implementation and use of advanced Data Science teams.  While the use of data has been around for many years, it’s hard to believe that it is only in the last five years or so that we have seen the adoption of such technology and skills really take off. Five years ago, the importance and demand for Data Scientists sat at a very meagre 17 per cent, whereas in 2019, we saw exponential growth of over 40 per cent – a number that is expected to continue growing as we move forward.  Within Data & Analytics, Data Science is a crucial arm within many businesses of all shapes and sizes. Through the collection and analysis of certain datasets, Data Science teams can delve into an organisation’s pain points, any potential obstacles and future predictions; crucial elements which, if looked at and planned for in advance, can be the making of a business.  So, how else can Data Science influence the decision-making process and make a positive impact on a business and its bottom line? The removal of bias and the increase of accuracy As humans we are innately susceptible to bias, conscious and unconscious, and this can be a hindrance on our ability to make informed yet impartial decisions. By relying solely on facts and figures instead of our own opinions, we are not only removing bias, but we are in turn making the decision-making process more accurate.  Accuracy within decision-making will remove the potential risk of mistakes and the need to re-do tasks, therefore saving precious time, resource and money, unequivocally a benefit for any business’s bottom line.  Efficiency There are elements of all businesses that require trial and error for example, hiring practices. People who look great on paper and perform exceptionally well in first interview may turn out to be utterly the wrong fit six months down the line. However,  collecting and recording data of those employees who do fit well into the business, compared to those who don’t, can help to reduce the chance of choosing the wrong candidate. This in turn improves staff retention rates, helps create a positive work culture and, of course, positively impacts profitability.  Considering the cost for hiring one person for a company is around £3,000, Data Science is of huge benefit to any company, large or small, in reducing the risk of high staff turnover.  Mitigating risk All businesses at some point in their lifetime will come up against potential obstacles and risks that, if not managed properly, can be potentially lethal. The implementation of Data Science will allow senior leaders to learn from past mistakes and create evidence-based plans to better tackle, or completely avoid, similar problems in the future.  This could be for either organisational risk or strategic risk, both of which can be extremely damaging if not prepared for. Organisational risk entails problems occurring within daily business tasks such as fraud, data loss, equipment and IT issues and staff resignations. Strategic risk relates to events that cannot be planned for in advance; those sudden and unforeseeable changes - a great example being the current COVID-19 pandemic.  However, with both risk groups, Data Scientists can help to mitigate these risks through learnings and observations made from reams of previous data, as well as real-time intelligence. This allows senior leaders to act fast where needed, and plan where possible.  Data & Analytics, and especially Data Science, has been, and will continue to be, a key driver in the evolution of many industries worldwide. As we move forward, we will undoubtedly see an even larger uptake of the available technologies as business leaders everywhere begin to see the influential value of data-driven decision-making. If you’re a Data Scientist looking to take a step up or are looking for the next member of your team, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

Is Computer Vision at the Core of the New Normal?

Computer Vision is one of the fastest growing markets in Data & Analytics. While it was on a trajectory prior to the pandemic, the needs we have now have amped up the role Computer Vision plays in our day-to-day lives and businesses who want to keep up or get ahead are paying attention.  Unexpected Businesses Using Computer Vision Some unusual players leaning on these technologies are grocery stores. While some have pivoted to pickup and delivery, others have remained stagnant with yesterday’s shopping habits changed only to individuals in store wearing masks. For those who made the leap to the "new normal", they’re using things like shelf sensors and Machine Learning to automate ordering and determine best placement of a product. Though retail stores are no stranger to video analytics, the rise of Deep Learning and AI offer a more rapid analysis of video for real-time threat assessment. Teaching the machine to watch for crowding, erratic movement, or potential conflict allows for quick reaction or proactive measures to stop a conflict in play. Yet, behind all this Machine Learning and Computer Vision elements are people. Real live humans. And it’s their new normal which is a strong part of the world’s new normal as most everyone shifts and remains online, working remotely. Behaviours are changing and many businesses have differentiated themselves from others by staying ahead of the game.        Five Ways Businesses Are Moving Forward in the New Normal Remote work is here to stay. A jump of 18% of remote working after the pandemic is expected to remain key to many businesses. And nearly three quarters of executives, plan to increase their remote workers. Key components of this new change will be to bring onboard those with strong digital collaboration skills, ability to manage virtually, and reassess how goals and objectives are to be decided. How will businesses keep remote employees engaged, enthused, and feel part of the team when they could be miles or countries apart?Gig Workers as Cost-Saving Measure. As employees move out of office and online, gig workers are a go-to for businesses hoping to move forward and keep costs low. Performance management systems will need to be re-evaluated. After all, if the idea is to keep costs low (read: overhead), then how does the debate about whether or not to offer benefits fit in to the mix?Definitions are Changing. Whether the definition includes ‘critical skills,’ ‘critical role,’ or something similar. What these meant once are changing. Now, the focus is on how to encourage, mentor, or coach employees in professional development skills which can open up a variety of opportunities versus one set path to one set role.Keeping Track Virtually. Though most businesses tend to follow the model of ‘productivity and performance’ over simply hours worked, some organisations passively track their remote workforce. This keeping track can include timeclock software virtually managed to computer usage to monitoring communications. Several benefits of data tracking in this manner could be a boon to HR Managers as it could help to understand employee engagement. But it’s a fine line to traverse.Organisational Redesign Done with Efficiency in Mind. As everything from products to people move online, it’s more important than ever to ensure things like logistics, supply chains, and workflows are designed with efficiency in mind. Computer Vision AI models can help take these systems to the next level as things like grocery shopping, retail, and legacy businesses find their business must go online or pivot in the new normal to survive. In our recently released 2020 Salary Guide we discuss each specialism. What’s working. What isn’t. And how businesses can hire and retain top talent to keep their projects on track and their businesses running smoothly.If you’re interested in Data & Technology, Risk or Digital Analytics, Life Sciences Analytics, Marketing & Insight, or Data Science, check out our current opportunities. Alternatively, you can contact one of our expert consultants if you’d like to learn more. 

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