Top Data Science Interview Questions

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Data science is a rapidly growing field with a bright economic future. According to the Bureau of Labor Statistics (BLS), the median salary for a data scientist is close to $101,000, and the field is expanding at an astounding pace, with 36% growth expected by 2031. This guide to data science interview questions will help you prepare for a successful interview in this competitive field by covering key topics and emerging issues in DS, including basic data science, data analysis, statistics, machine learning, and deep learning.

Types of Data Science Questions

Other than general questions about your data science education or skills, you’ll also receive some technical questions that will assess your knowledge of various aspects of data science during your interview. Most of these questions will fall into one of four categories:

  1. Basic data science questions
  2. Data analysis questions
  3. Questions involving statistics
  4. Questions about machine learning and deep learning

Let’s look at a few common questions from each of these categories before going over some data science interview preparation techniques.

Basic Data Science Interview Questions

1. What Is a confusion matrix in predictive modeling?

Researchers use confusion matrices to gauge whether classification algorithms are performing effectively. The purpose of using a confusion matrix, which takes the form of a four-panel table, is to clearly visualize accuracy, recall, and other predictive analytics.

2. How do you use a confusion matrix to calculate the accuracy of a predictive model?

As we mentioned, a confusion matrix looks at four specific values. These are true and false positives and true and false negatives.

A true positive (TP) results when a positive outcome is predicted and occurs, while a true negative (TN) results when a negative outcome is predicted and occurs. By comparison, a false positive (FP) results when a positive outcome is predicted, but a negative outcome occurs. Likewise, a false negative (FN) results when a negative outcome is predicted, but a positive outcome occurs.

Using a confusion matrix, researchers can calculate accuracy using the formula: TP + TN / TP + TN + FP + FN.

3. Explain dimensionality reduction and its benefits.

Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of features or variables in a dataset while retaining as much information as possible. Dimensionality reduction makes it simpler for researchers to both interpret and visualize results while simultaneously minimizing the amount of storage space needed for the data. The overarching goal of dimensionality reduction is to simplify the data and make it easier to analyze, visualize, and interpret.

Data Analysis Interview Questions

1. Why is data cleaning important?

Incorrect data, corrupted data, duplicated figures, and lack of consistent formatting are just four examples of the numerous problems that can interfere with accuracy. Data cleaning is the process of removing these issues so that the data can be analyzed with greater ease, efficiency, and accuracy.

2. Why is Python used for data cleaning in data science?

Data cleaning is imperative for the reasons stated above, such as removing redundancies that could muddy the outcomes of an analysis. Python is commonly used for this purpose — along with Numpy, Pandas, SciPy, Matplotlib, and Keras — because of its versatility and flexibility as a general programming language.

3. Explain univariate, bivariate, and multivariate analyses.

Univariate analysis is the analysis of data with a single variable. Bivariate analysis is the analysis of data featuring two variables. Multivariate analysis is the analysis of data containing three or more variables.

4. What are Eigenvectors and eigenvalues?

In data science, eigenvectors and eigenvalues are used in linear algebra to analyze and transform data. Eigenvectors are the corresponding vectors that represent the direction of the variance in the dataset. Eigenvalues are scalar values that represent the amount of variance in a dataset along a particular axis or direction. Together, eigenvectors and eigenvalues can be used to perform principal component analysis (PCA), a technique used to reduce the dimensionality of a dataset by identifying the most important features or variables.

Statistics Interview Questions

1. What is selection bias?

Selection bias occurs when non-randomized population samples create a slant or “bias” in the data, leading to mistakes or omissions. For example, the National Cancer Institute defines selection bias as “an error in choosing the individuals or groups to take part in a study” and adds that the “subjects in a study should be very similar to one another and to the larger population from which they are drawn.”

2. What are the types of biases that can occur during sampling?

There are three basic types of sampling bias that researchers need to be aware of: selection bias, described above; undercoverage bias, which is when certain demographics or populations are excluded from research; and survivorship bias, which occurs when researchers focus exclusively on people who “survived” or passed a selection process.

3. What is A/B Testing? What is the goal of A/B Testing?

A/B testing compares the performance of two different versions of a website. Its purpose is to identify which details or variables drive the best business outcomes, such as testing two versions of a homepage to determine which one results in higher conversion or click-through rates.

4. What is a p-value? Why do we use p-value?

P-value measures the statistical significance of a given observation, enabling researchers to decide whether to accept or reject null hypotheses. The p-value is used to determine whether and to what extent the data accurately reflects the observed effect.

5. What is the ROC curve?

The ROC curve is widely used in machine learning, medical diagnosis, and other fields where binary classification is important. Referring to the receiver operating characteristic, the ROC curve is a graphical representation that is primarily used to evaluate certain predictive models.

6. What is a normal distribution?

Data can be presented visually to help researchers understand how it is distributed and whether there are any clusters or biases present in that distribution. Data can also be distributed around a median value, mean value, or other central value, which eliminates bias and results in the production of a bell curve. This is referred to as “normal distribution.”

Machine Learning Interview Questions

1. What is supervised learning?

Supervised learning refers to machine learning that occurs by inferring functions from training data, which features labeled training examples. Labeled data sets are an integral feature of supervised learning and teach the model to improve (such as classifying data more accurately) over time.

2. What is unsupervised learning?

In contrast to supervised learning, unsupervised learning does not use labeled data sets. Instead, this type of machine learning uses algorithms that “discover hidden patterns in data without the need for human intervention,” according to IBM. Unsupervised learning is chiefly used for three types of tasks: clustering, association, and dimensionality reduction.

3. What is an SVM in data science?

The acronym SVM refers to support vector machine. SVMs utilize hyperplanes — decision boundaries that separate classes — to complete classification or predictive tasks.

4. What is deep learning?

Deep learning is a subcategory of machine learning characterized by using neural networks to mimic human brain structure and function. The description of learning as “deep” refers to hidden, interconnected layers within the neural network.

5. What is an CNN (convolutional neural network)?

A CNN is a type of artificial neural network that is commonly used for image and video recognition and data classification. It is used extensively in deep learning and designed to automatically and adaptively learn spatial hierarchies of features from input data. CNNs have been widely used in various settings, such as object detection, facial recognition, and even self-driving cars.

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How to Prepare for a Data Science Interview

Knowing how to answer technical or industry-specific questions is only one aspect of preparing for a job interview. Here are six additional ways you can (and should) prepare, increasing your odds of a smooth and successful interview.

  1. Research the role and organization. Websites like LinkedIn, Glassdoor, Monster, and ZipRecruiter are four great resources for information about company culture, employee compensation, and other employees’ overall level of engagement and satisfaction at the company.
  2. Research the interviewer. If you know who will be interviewing you, it’s a good idea to familiarize yourself with their job title, specialization area(s), and past accomplishments so that you can ask more informed and relevant questions during your interview.
  3. Review your portfolio of past projects. This step is essential for ensuring you can discuss, defend, and explain each project you’ve worked on and in what capacity, in detail during your interview. Certain software companies, like GitHub, allow users to make separate changes to web pages at the same time. Employers will often search these web-based interfaces to review archives of an applicant’s data science projects.
  4. Prepare answers to common questions. We’ve reviewed some of the more technical data science interview questions in this guide, but preparing yourself for generic interview questions is equally important. For example, you should have robust answers to questions like, “Can you share a time when you overcame a challenge or solved a problem that you were facing in your last role?” or, “What would you say are some of your greatest weaknesses in the workplace?” You’re also likely to be asked about your hard and soft skills, your level of education, and previous standout accomplishments or achievements.
  5. Ask a friend or mentor to help you practice with a mock interview. Rehearsing mentally is one thing, but giving eloquent answers aloud is another — especially when you’re under the pressure of an important job interview. Practicing interviews with a trusted friend or mentor is a wise idea, which will help you gain confidence, refine the wording you use, and improve your overall delivery.
  6. Be ready to handle illegal and inappropriate questions. Unfortunately, you might encounter a situation where an interviewer asks you about a topic involving your age, gender, race or ethnicity, religious beliefs, sexual orientation, marital status, or whether you plan to become pregnant or have children. It is inappropriate — and in many scenarios, outright illegal — for employers to ask interviewees about these issues. A good response is to ask how the question is pertinent to your qualifications. Even if you are offered a position, you may want to think twice before joining an organization that asks illegal or overly personal questions as a part of its regular interview process.
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Successful Interview Tips for a Data Science Position

You’ve practiced and rehearsed your answers, taken the time to review your portfolio, and thoroughly researched the company and role you intend to apply to. You’ve even thought about your response strategy in the event that you’re asked an illegal or inappropriate question.

You’ve taken the necessary steps to ensure you’re well-prepared for your interview for your desired data science position. Follow these seven tips to impress your interviewer and receive the job offer you’ve been preparing for.

  • Bring physical copies of your resume to share. Bring at least two copies of your resume and any additional materials you’re requested to bring. It’s wise to use a folder to prevent any crumpling or wrinkling.
  • Dress professionally. Even if the company has a casual culture and dress code, it’s important to dress professionally for an interview to show that you’re serious about the position and have respect for the organization and interviewers.
  • Introduce yourself. Your introduction is the perfect time to explain why you’re a good fit for the role and the company.
  • Address resume gaps head-on. If there are any gaps in your resume, be confident and upfront about explaining the reason behind them. For instance, perhaps you were traveling abroad, taking time off to pursue a degree, working on a personal or creative pursuit, or providing care for a loved one.
  • Answer questions courteously. Interviewers aren’t just assessing your skills or reviewing your accomplishments — they’re also observing how you conduct yourself, whether you’ll represent the organization positively, and whether you’ll be a good “team player” with other employees. Therefore, maintaining a courteous, friendly, and professional demeanor throughout your interview is crucial.
  • Have questions to ask the interviewers. It’s natural to focus on saying the right things — but what you don’t say is just as impactful. In fact, one of the biggest job interview mistakes you can make is not having anything to ask when it’s your turn. Having thoughtful, pertinent questions shows that you took the time to research the role and have given serious thought to your career while also demonstrating that you are engaged and interested in learning.
  • Thank the interviewers for their time at the end of the interview. Be sure to thank your interviewer for sharing their valuable time and giving you the opportunity to chat. You should also follow up with a succinct thank-you email, ideally within 48 hours.

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