Data science interview jitters?

We’ve got you covered!

So your data science resume did well, and you got the interview call. First off, congratulations! and second, you might want to spend some time on data science interview preparation.

While narrowing it down to a set list of questions is impossible, as each company has different requirements, we have a general idea of what you need to look out for.

Learn how you can ace your data science questions with prepared answers so that you can get through them with minimum stutters!

Here is the summary of this guide to give you an outline of what we will be discussing:

  • Common data science interview questions include getting to know your personality and soft skills as an employee
  • Technical data science interview questions are subject-specific and require deep knowledge
  • Recruiters could also question your acquaintance with tools like programming languages or databases
  • Always be honest during interviews, even if it involves explaining your shortcomings
  • To pass the truth test by hiring managers, make sure you explain your previous work functions and certifications thoroughly when asked

If you are starting afresh with your data scientist interview research, here are a few questions that can help you prepare yourself:

You can check out our Interview Preparation tool to find the answers to all of your queries on data science questions!

Meanwhile, you can go over similar interview preparation guides to give you a well-rounded explanation of the process:

Data Science Interview Questions and Answers

The interview is where the recruiter does a thorough assessment of judging your suitability for the job that you are applying for. Before you educate yourself on data scientist interview questions, ensure that you are well prepared with everything you listed in your application.

In a job environment wherein resume frauds are more common than normal, recruiters will take the extra steps to verify that you are not one of them. They will try to cross-check the information that you gave in your data scientist resume.

For this reason, make sure that you maintain a conversational tone and answer their questions with honesty. Recruiters appreciate honesty but shroud them in positivity so that you still carry a positive first impression.

There are the categories of data scientist interview questions:

  • Common data science interview questions
  • Technical data science interview questions

You need to be familiar with questions in both categories so that you can answer data science questions easily!

Also read: How to Become a Data Scientist

Common Data Science Interview Questions and Answers

Aside from the more subject-specific questions, there are a few common ones that are bound to come up during data science job interview questions.

Such common questions are usually asked to assess your soft skills or interest in the role you are applying for. Here are some of those data science job interview questions:

  • Why do you want to work here?
    For this question, we would advise you to do research on the organization and find out what they cater to. Instead of winging it, make sure that you have enough knowledge about what they expect from you and how that organization aligns with your data science career requirements.

Refrain from answering generically and do adequate research to personalize it for each company. If you send out applications based on the company, then you could delve into how you had been following their work.

However, if you send out applications based on the role, you need to find out more about the company and know a bit about the kind of work they do. Of all the applications, you could stand out if the recruiter understands that you are passionate about what you do.

  • Why should we hire you?
    Recruiters mainly ask this question to understand how your skills would benefit their company. The research you do will come handy in here too.

Since you know what the company does, you can set a hypothetical situation wherein you use your skills and expertise to achieve company goals. Along with that, you can also talk about soft skills such as leadership, communication, discipline, and other skills that are requisite to a functional workplace.

  • What are your greatest strengths?
    Once you hear this question, create a mental checklist of all your data scientist skills and achievements. Aside from that, you could also add soft skills like innovating processes, meeting deadlines, bringing fresh ideas to the table, etc.

Even for this question, you need to have a basic understanding of what the company does. Assess the data scientist job description thoroughly to ensure that you are hitting at least some of the points that they mentioned.

  • What are your weaknesses?
    Undoubtedly, the trickiest question is when recruiters ask you to describe your shortcomings. However, they are doing so to assess your self-awareness and evaluate the kind of employee you would be at the same time.

You might be hesitant to answer this question, worrying that it might be a deal-breaker for your application. On the contrary, recruiters want you to be honest and address your minor faults as an employee and what you do to fix them.

Having faults as an employee does not make you an unworthy applicant but a self-aware one. Make sure that your answer ends on a positive note and does not leave a negative impression on the employer.

Technical Data Science Interview Questions and Answers

Another part of data science questions is the technical side of your work. In this category, recruiters will assess your ability to perform your work accurately.

Here are a few data science job interview questions:

  • How would you describe the differences between supervised and unsupervised learning?
    Supervised learning uses labeled data as input and has a feedback mechanism. The most commonly used algorithms are logistic regression, decision trees, and support vector machines.

On the other hand, unsupervised learning uses unlabeled data as input and has no feedback mechanism. The popular algorithms for unsupervised learning are the k-means clustering, apriori algorithm, and hierarchical clustering.

  • How is logistic regression done?
    Logistic regression is a statistical model that uses the logit function to give either 0 or 1. It is the Binary Classification that evaluates the relationship between the dependent variable, that is our predictions, and one or more independent variables by calculating the probability.

  • What is a decision tree, and explain the steps in making one
    It is a decision support tool that uses a tree-like structure of options and possible outcomes, including resource costs, utility, and chance event outcomes. Decision trees are the primary way to display an algorithm that exclusively contains conditional control statements.

The steps involved in making a decision tree are:

  • Take the input to be the whole data set

  • Measure entropy of target variable and predictor attributes

  • Calculate information gain of all attributes by gathering information on categorizing different objects

  • Choose one with the highest information gain as the root node

  • Repeat the procedure on every branch of the decision tree until the decision node is finalized on each branch

  • Define terms KPI, lift, model fitting, robustness and DOE
    KPI stands for Key Performance Indicator, that is measuring attributes of business success and how it achieves its objectives
    Lift is a performance indicator of the target model compared to a random choice model. It indicates how effectively the model is at predicting in contrast with the absence of one
    Model fitting evaluates how the model fits the given objectives and observations
    Robustness stands for a systems ability to handle variances and differences efficiently
    DOE refers to the design of experiments, which is the task that aims to describe and explain variation in information under certain premises to reflect any variables

  • What is Natural Language Processing?
    It is a branch of Artificial Intelligence that converts human language to machine-understandable language to be processed by ML models. Some of its examples are chatbots, google translate, and other real-time products like Google Home or Alexa.

Text suggestions, sentence corrections, and text completion are a few other examples of NLP.

  • What is Imbalanced Data, and what are the ways to manage it?
    Imbalance data is the error or imbalance in data distribution across different categories. Such data sets cause inconsistencies in model performance by prioritizing categories with large values and making it an inaccurate model.

To combat this, the number of samples for minority classes should be increased and the ones with high numbers of data points should be decreased. A cluster-based mechanism to increase data points for every category is ideal.

  • Define correlation and covariance in terms of their differences.
    Both correlation and covariance and used to establish dependency between random variables. However, there are a few differences that set them apart:

Covariance: It shows how much variables can change together in a cycle, thereby explaining the inherent relationship between two variables wherein a change in one affects the other.

Correlation: It measures how strong two variables are related by calculating the quantitative relationship between them.

Here is a graphical explanation of both the techniques and their differences:



  • Explain the basic steps of completing projects based on data analytics.
    After understanding the business requirements through coordination with stakeholders, the primary step is to analyze the given data and gather all necessities from the business.

Immediately after, the data is cleaned and prepared for data modeling, wherein the variables are transformed, and missing values are gleaned. The data models are then run against the data to gather meaningful conclusions to support decision-making.

After releasing the model implementation, the results are tracked, and the usefulness is analyzed by tracking the results and performance over specified periods. The final step is to conduct cross-validation of the data model.

  • Why is data cleaning important? Describe the step involved in data cleaning.
    It is extremely vital to clean data to glean valuable insights while running algorithms. Otherwise, it disturbs the process and leads the analyst to irrelevant or low-quality conclusions, which can lead to a huge waste of resources, including time.

Since the insights gathered from data analysts play a huge role in business decision-making, it is important to follow a tight error-free process that includes data cleaning. It also helps in fixing any structural issues in data and enhances the performance and speed of the process. In short, skipping this step can do more damage than good.

  • How do you fill in missing values while analyzing data?
    While some missing values can be more damaging than others, it is important to identify what kind of variables have these missing values.

If the missing values make meaningful patterns, then a few valuable insights can be drawn from them. In the alternate case where there are no patterns, then the values can be entirely ignored or filled in with default values like maximum, minimum, median, or mean values.

Another factor where you can use default values is if the missing values are part of categorical variables. Additionally, if the data analyzed have a standard distribution, then the missing values can be replaced with the mean value of the data.

However, in the case that 80% or more values are missing, then the data analyst can either drop the variables completely or replace them all with default values.

  • How do you create the ROC Curve?
    The Receiver Operating Characteristic or ROC curve is a graphical depiction of the difference between false-positive and true-positive rates at contrasting thresholds. It is used as a proxy for a switch between specificity and sensitivity.

The ROC curve is built by noting values of the true-positive rates against the false-positive rates. What it represents is the proportion of conclusions accurately predicted as positive out of the final positive conclusions. It also represents the degree of incorrectly predicted conclusions out of the gross negative conclusions

Data Science Interview Questions on Technical Tools

As data scientists, you may question your familiarity with tools that are crucial to data science. Acquaintance with technical tools means that you get better opportunities, and consequentially, better data scientist salary.


Source: Interview Bit

Here are a few tools in the list of data science job interview questions:

Python Data Science Interview Questions
Familiarity with programming languages like Python or R is a revered skill when it comes to data science. If you are acquainted with Python, you should ideally have basic knowledge of libraries such as Scikit-learn and Pandas.

You can create data science projects on Kaggle or GitHub and present that during the interview or explain your projects in detail. You can use Interview Query to practice Python lessons.

SQL Data Science Interview Questions
Even though your recruiter would not expect you to be proficient in entry-level or junior positions, it is a worthwhile skill for all roles. Especially if you want to submit applications at big companies with a lot of data, you would have to explore those data to identify and solve issues.

With knowledge of SQL, you would be able to write pivotal queries and know how to manipulate that data by using SQL queries.

Here are some SQL data science interview questions:

  • What are ACLs in MySQL?
    An ACL is a comprehensive list of permissions of a system source. MySQL makes use of security based on these lists for connections and other operations.

  • How to test for NULL values in a database?
    It is usually used in a database to mark an unknown value. MySQL comprehends the NULL value differently from other data types. To compare other fields with these values, we can use expressions like “IS NULL” or “IS NOT NULL”.

  • What is the difference between CHAR and VARCHAR in MySQL?
    While CHAR is the fixed length, VARCHAR is the variable length. They differ in maximum length as well and are different in the way they are stored and recovered.

Data Science Internship Interview Questions

To become a skilled data scientist, you need to have equal theoretical and practical experience. Data science internships are the best way to exercise your skills, even if you have not completed your education yet.

However, you cannot bag data science internships without being knowledgeable. Here are some data science intern interview questions that could help you impress recruiters:

  • What does the term Data Science mean?
    Data science is an interdisciplinary field comprised of scientific methods, algorithms, tools, and machine learning techniques to derive meaningful conclusions from raw data.

By analyzing business requirements and performing various methods like data cleaning, warehousing, staging, and architecture, data scientists maintain the raw data for processing.

To process it, data is subjected to algorithms like predictive analysis, regression, text mining, recognition patterns, etc. depending on the requirements of the company. The processed data is then brought to stakeholders through data visualization and reporting.

Also read: What is Data Science

  • What is the difference between data analytics and data science?
    Data science is mainly about transforming raw data into meaningful insights through technical analysis and applying complex algorithms.

On the other hand, data analytics deals with analyzing current business scenarios and assessing ways to facilitate better decision-making.

While data science uses predictive modeling, data analysts gain conclusions through existing contexts. Both professions focus on company growth and better decision-making but perform different tasks as a powerful duo.

Find out more: Data Analyst vs Data Scientist

  • Explain some methods used for sampling and its advantages.
    Data scientists deal with huge datasets to facilitate better outcomes. However, they do not process all of the data at once. Sampling is a technique that extracts smaller samples that encapsulate the essence of the entire data set and perform analysis on them.

There are two major categories of sampling methods:
Probability sampling techniques: clustered sampling, stratified sampling, simple random sampling.
Non-probability sampling techniques: convenience sampling, quota sampling, snowball sampling, etc.

  • What are the conditions for overfitting and underfitting?
    Overfitting only works well for sample training data. If new data is given to the model as input, it will not provide any results. The condition occurs because of high variance and low bias in the model.

Underfitting is caused due to over-simplicity. Since the model is simple, it will not be able to identify the correct relationship in the data. So, it will not perform properly even on test data.

Overfitting is mostly seen among decision trees, whereas linear regression is more subject to underfitting.

  • Name some key differences between long and wide format data.
    In long format data, each row represents a specific piece of information about the subject, and the subject would have its data in multiple rows. However, in wide-format data, the responses by a subject are fed in separate columns.

Long format data considered rows as groups, whereas wide-format data considers columns as groups.

While long format data is used in R analyses and writing into log files after trials, the wide-format data is commonly used in stats packages for repeated measures ANOVAs, and rarely in R analyses.

With these data science internship interview questions, you can make sure that you get the job and gain all the experience you need to move further in your career!

Interview Preparation by Hiration

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Key Takeaways

A data science interview can seem interrogatory if you do not know the answers. Prepare for your data science interview by educating yourself on theoretical and practical knowledge.

Here is what you can take away from this guide:

  • Perform research on the company to understand the kind of work they do and assess how you can fit in to contribute positively
  • Answer common data science interview questions by analyzing your soft skills and performance as an employee
  • Learn basic concepts of data science as recruiters will test your knowledge on the subject by asking you technical questions
  • Show your acquaintance with technical tools needed for data science by preparing data science projects or models beforehand

Do not let your interview opportunity pass, and prepare thoroughly for your data science interview with these tips!

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