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How to Become a Data Scientist

Data Science Interview Questions

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Data science interview processes can vary depending on the company and industry. Typically, they will include an initial phone screening with the hiring manager followed by one or several onsite interviews.

You will have to answer technical and behavioral data science interview questions and will likely complete a skills-related project. Before every interview, you should review your resume and portfolio, as well as prepare for potential interview questions.

Data science interview questions will test your statistics, programming, mathematics, and data modeling knowledge and skills. Employers will be assessing your technical and soft skills and how well you would fit in with their company.

By preparing some common data science interview questions and answers, you can enter the interview with confidence. There are a few different types of Data Scientist questions that you can expect to encounter during your data science interview.

Employers are looking for candidates who have a strong knowledge of data science techniques and concepts. Data-related interview questions will vary depending on the position and skills required.

Here are some examples of sample data-related interview questions and answers:

What is the difference between supervised and unsupervised learning?

The biggest difference between supervised and unsupervised learning involves the use of labeled and unlabeled datasets. Supervised learning uses output and input data that is labeled, and unsupervised learning algorithms don't. Another difference is that supervised learning has a feedback mechanism while unsupervised learning does not. Finally, commonly used supervised learning algorithms include logistic regression, support vector machine, and decision trees, while unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm.

What is the difference between machine learning and deep learning?

This question can be difficult to answer clearly because there is obviously some overlap here. Start by explaining that deep learning is essentially a subfield of machine learning and that both fall under the umbrella of artificial intelligence. Where machine learning uses algorithms to analyze data and ultimately learn to make decisions based on what it brings out of the data, deep learning layers those algorithms to create artificial neural networks capable of learning and making informed decisions.

  • Explain Decision Tree algorithm in detail.
  • What is sampling? How many sampling methods do you know?
  • What is the difference between type I vs type II error?
  • What is linear regression? What do the terms p-value, coefficient, and r-squared value mean? What is the significance of each of these components?
  • What is a statistical interaction?
  • What is selection bias?
  • What is an example of a data set with a non-Gaussian distribution?
  • What is the Binomial Probability Formula?
  • How is k-NN different from k-means clustering?
  • How would you create a logistic regression model?
  • Explain the 80/20 rule, and tell me about its importance in model validation.
  • Explain what precision and recall are. How do they relate to the ROC curve?
  • Explain the difference between L1 and L2 regularization methods.
  • What is root cause analysis?
  • What are hash table collisions?
  • What are some of the steps for data wrangling and data cleaning before applying machine learning algorithms?
  • What is the difference between a box plot and a histogram?
  • What is cross-validation?
  • Explain what a false positive and a false negative are. Is it better to have too many false positives or too many false negatives?
  • In your opinion, which is more important when designing a machine learning model: model performance or model accuracy?
  • What are some situations where a general linear model fails?
  • Do you think 50 small decision trees are better than a large one? Why?

List of Data Science Interviews Questions: Technical Skills Questions

Technical skills questions in a data science interview are used to assess your data science knowledge, skills, and abilities. These questions will be related to the specific job responsibilities of the Data Scientist position.

Technical data science interview questions may have one correct answer or several possible solutions. You will want to show your thought process when solving problems and clearly explain how you arrived at an answer.

Examples of technical data science skill interview questions include:

What are the most important tools and technical skills for a Data Scientist?

Data science is a highly technical field and you will want to show the hiring manager that you're adept with all of the latest industry-standard tools, software, and programming languages. Out of the various statistical programming languages used in data science, R and Python are most commonly used by Data Scientists. Both can be used for statistical functions such as creating a nonlinear or linear model, regression analysis, statistical tests, data mining, and more. Another important data science tool is RStudio Server, while Jupyter Notebook is often used for statistical modeling, data visualizations, machine learning functions, etc. Of course, there are a number of dedicated data visualization tools used extensively by Data Scientists, including Tableau, PowerBI, Bokeh, Plotly, and Infogram. Data Scientists also need plenty of experience using SQL and Excel.

Your answer should also mention any specific tools or technical competencies demanded by the job you're interviewing for. Review the job description and if there are any tools or programs you haven't used, it might be worth becoming familiar with before your interview.

How can outlier values be treated?

Some types of outliers can be removed. Garbage values or values that you know cannot be true, can be dropped. Outliers with extreme values far outside the rest of the data points clustered in a set can be removed as well. If you cannot drop outliers, you could reconsider whether you chose the right model, you could use algorithms (like random forests) that won't be impacted as heavily by the outlier values, or you could try normalizing your data.

  • Tell me about an original algorithm you created.
  • What are some pros and cons of your favorite statistical software?
  • Describe a data science project in which you worked with a substantial programming component. What did you learn from that experience?
  • How would you effectively represent data with five dimensions?
  • Assume you need to generate a predictive model using multiple regression. Explain how you intend to validate this model.
  • When modifying an algorithm, how do you know that your changes are an improvement over not doing anything?
  • What is one way that you would handle an imbalanced data set that’s being used for prediction (i.e., vastly more negative classes than positive classes)?
  • How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression?
  • I have two models of comparable accuracy and computational performance. Which one should I choose for production and why?
  • You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them?

List of Data Science Interview Questions: Personal Questions

Along with testing your data science knowledge and skills, employers will likely also ask general questions to get to know you better. These questions will help them understand your work style, personality, and how you might fit into their company culture.

Personal Data Scientist interview questions may include:

What do you think makes a good Data Scientist?

Your answer to this question will tell a hiring manager a lot about how you see your role and the value you bring to an organization. In your answer, you could talk about how data science requires a rare combination of competencies and skills. A good Data Scientist needs to combine the technical skill needed to parse data and create models with the business sense necessary to understand the problems they're tackling as well as recognize actionable insights in their data. In your answer, you could also discuss a Data Scientist you look up to, whether it's a colleague you know personally or an insightful industry figure.

  • Tell me about yourself.
  • What are some of your strengths and weaknesses?
  • Which Data Scientist do you admire most?
  • How did you become interested in data science?
  • What unique skills do you think you can bring to the team?
  • Why did you leave your last job?
  • What kind of compensation are you looking for?
  • Give a few examples of best practices in data science.
  • What’s a data science project you would want to work on at our company?
  • Do you work better alone or as part of a team of Data Scientists?
  • Where do you see yourself in five years?
  • How do you handle stressful situations?
  • What motivates you?
  • How do you evaluate success?
  • What type of work environment do you prefer?
  • What are you passionate about outside of data science?

List of Data Science Interview Questions: Leadership and Communication

Leadership and communication are two valuable skills for Data Scientists. Employers value job candidates who can show initiative, share their expertise with team members, and communicate data science objectives and strategies.

Here are some examples of leadership and communication data science interview questions:

Tell me about an experience working on a multi-disciplinary team.

A Data Scientist collaborates with a wide variety of people in technical and non-technical roles. It is not uncommon for a Data Scientist to work with developers, designers, product specialists, data analysts, sales and marketing teams, and top-level executives, not to mention clients. So in your answer to this question, you need to illustrate that you're a team player who relishes the opportunity to meet and collaborate with people across an organization. Choose an example of a situation where you reported to the highest-level people in a company to show not only that you are comfortable communicating with anyone, but also to show how valuable your data-driven insights have been in the past.

  • Can you tell me about a time when you demonstrated leadership capabilities on the job?
  • How do you go about resolving conflict?
  • How do you prefer to build rapport with others?
  • Talk about a successful presentation you gave and why you think it went well.
  • How would you explain a complicated technical problem to a colleague/client with less technical understanding?
  • Describe a time when you had to be careful talking about sensitive information. How did you do it?
  • Rate your communication skills on a scale of 1 to 10. Give examples of experiences that demonstrate the rating is accurate.

List of Data Science Interview Questions: Behavioral

With behavioral interview questions, employers are looking for specific situations that showcase certain skills. The interviewer wants to understand how you dealt with situations in the past, what you learned, and what you are able to bring to their company.

Examples of behavioral questions in a data science interview include:

Tell me about a time when you had to clean and organize a big data set.

Studies have shown that Data Scientists spend most of their time on data preparation, as opposed to data mining or modeling. So if you have any experience as a Data Scientist, it is almost certain that you have experience cleaning and organizing a big data set. It is also true that this is a task that few people really enjoy. But data cleaning is also one of the most important steps for any company. So you should take the hiring manager through the process you follow in data preparation: removing duplicate observations, fixing structural errors, filtering outliers, tackling missing data, and data validation.

  • Tell me about a data project you have worked on where you encountered a challenging problem. How did you respond?
  • Have you gone above and beyond the call of duty? If so, how?
  • Tell me about a time you failed and what you have learned from it.
  • How have you used data to elevate the experience of a customer or stakeholder?
  • Provide an example of a goal you reached and tell me how you achieved it.
  • Provide an example of a goal you did not meet and how you handled it.
  • How did you handle meeting a tight deadline?
  • Tell me about a time when you resolved a conflict.

List of Data Science Interview Questions From Top Companies (Amazon, Google, Facebook, Microsoft)

To give you an idea of some other questions that may come up in an interview, we compiled a list of data science interview questions from some of the top tech companies.

  • What’s the difference between logistic regression and support vector machines? What's an example of a situation where you would use one over the other?
  • What is the interpretation of an ROC area under the curve as an integral?
  • A disc is spinning on a spindle and you don’t know the direction in which way the disc is spinning. You are provided with a set of pins. How will you use the pins to describe in which way the disc is spinning?
  • What would you do if removing missing values from a dataset causes bias?
  • What kind of metrics would you want to consider when solving questions around a product’s health, growth, or engagement?
  • What metrics would you assess when trying to solve business problems related to our product?
  • How would you tell if a product is performing well or not?
  • How do you detect if a new observation is an outlier? What is a bias-variance trade-off?
  • Discuss how to randomly select a sample from a product user population.
  • Explain the steps for data wrangling and cleaning before applying machine learning algorithms.
  • How would you deal with unbalanced binary classification?
  • What is the difference between good and bad data visualization?
  • How do you find percentiles? Write the code for it.
  • Create a function that checks if a word is a palindrome.
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