Top Data Science Interview Questions and Answers
Preparing for a job interview in any field can be intimidating. If you’re looking for a job in data science and want to ace the interview, you need to be able to speak confidently on the key areas of the topic.
This comprehensive guide to the most common data science interview questions will give you a concise primer on Python, EDA (Exploratory Data Analysis), statistics, and machine learning concepts, along with expert answers and tips.
Basic Data Science Interview Questions
These foundational questions test your understanding of core data science concepts.
What Is Data Science, and Why Is It Important?
Data science is a multidisciplinary field combining elements of mathematics, statistics, computer engineering, and AI. Data scientists use these skills to extract meaningful insights from data to help businesses make decisions. Interviewers want to see you have broad and deep knowledge of the field.
Explain the Difference Between Supervised, Unsupervised, and Reinforcement Learning
The key difference between these types of machine learning models is how they receive data:
- Supervised learning is when a model is provided with labeled data. Labeled data has a classification attached to it. For example, if we have a set of photographs of various animals and we know which photos are cats, then we have labeled data. Usually, what we are trying to do with this kind of data is build a model that predicts which label to assign to data that’s similar to the labeled dataset, but doesn’t have a label attached. Continuing the example, we might use that dataset to build a model that predicts whether an unlabeled photograph is of a cat.
- Unsupervised learning refers to the model being asked to find patterns within unlabeled data. Unlabeled data is data without a classification attached to it. For example, if we have a set of photographs of animals but we do not know which animals are in each photo, we have unlabeled data. We might use this data to cluster the photographs into groups of similar photographs.
- Reinforcement learning involves the model taking action and receiving feedback based on those actions. For an example of reinforcement learning: a video streaming service uses a model to decide which movie to offer you (the action) based on what it thinks you’ll like to watch. The streaming service’s model then learns based on whether you decide to watch the movie (the feedback), and its model improves over time.
What Are the Steps in a Data Science Project Lifecycle?
A data science project involves several steps:
- Define the problem. Answer the question: What do we know (what data do we have) and what do we want to learn?
- Collect and prepare data.
- Explore and analyze the data.
- Build, evaluate, and refine a model to learn from the data.
- Deploy and maintain the model.
Python and Data Analysis Questions
Python is a popular tool/programming language for data analysis, thanks to its relatively simple syntax and wealth of libraries.
What Are Some Python Libraries Commonly Used in Data Science?
Some common Python libraries used for data science include:
- NumPy. This library includes several tools to assist with linear algebra. If you need to do anything involving matrices or vectors, this library probably has what you’re looking for.
- Matplotlib. The Matplotlib library streamlines the process of visualizing data.
- Pandas. This fast, flexible library helps developers analyze, cleanse, and transform large data sets. Pandas has become the standard way to interact with and load datasets in Python. Many other libraries use Pandas dataframes.
- SciPy. This data science library assists with differential equations, eigenvalue problems, and other areas of scientific computing. This library is also where you’ll find the distribution functions of common distributions, like the normal or gamma distribution. It also has functions to generate random variables drawn from these distributions. SciPy also has optimization routines that are useful when you need to minimize a loss function.
- PyTorch. This set of tools helps data scientists build, train, and work with machine learning models.
Explore these topics in more depth with this course on data science and machine learning with Python, covering key concepts and hands-on applications.
Explain Data Manipulation Using Pandas and NumPy
Pandas is built on top of the NumPy library. While NumPy can be used as a standalone library for calculating averages, multiplying matrices, and working with multidimensional arrays, Pandas excels at importing and working with datasets.
Data scientists can create data frames in Pandas either by using one of Pandas’ functions to load a dataset (from, say, a CSV or Parquet file) or by defining it manually (usually only useful for small datasets). To define a data frame manually, provide Pandas with a dictionary where each key represents the name of a column in the dataset and the list associated with the key represents the rows of the dataset.
Pandas provides a variety of tools to help developers sort, filter, and manipulate data frames.
How Do You Handle Missing Values in a Dataset?
Pandas provides two functions to identify and handle missing values: isnull() and notnull(). Once you’ve found the missing values, you can handle them with one of the following functions:
- fillna() to replace NaN values with specific text or numbers.
- replace() to fill missing values with a specified value.
- interpolate() to use interpolation techniques to calculate what the replacement value should be.
- dropna() to remove rows containing missing values.
Statistics and Probability Questions
Statistics and probability are a vital part of data science. Practicing basic statistics problems and reviewing the terminology before a data science interview is key to a successful interview. You should be able to speak confidently about a broad range of statistical topics. Time spent learning and reviewing the basic skills of data science is never time wasted. To sharpen these skills, consider taking this course on statistics and probability.
What Is the Central Limit Theorem, and Why Is It Important?
The central limit theorem says that the distribution of the sample mean is arbitrarily close to a normal distribution, given a large enough sample size. It’s one of the most important results in statistics and data science. We use the Central Limit Theorem to understand how statistical error affects our estimates. The theorem underlies many formulas for confidence intervals and p-values.
Explain Hypothesis Testing and P-Values
Hypothesis testing is a procedure that tests whether there is enough evidence to reject our default conclusion (e.g., the new landing page has no effect) in favor of an alternative conclusion (e.g., the new landing page increases sales). Data scientists state their hypothesis, collect data to test it, and use a statistical test to determine whether the hypothesis should be rejected. We determine whether a hypothesis should be rejected by comparing the value of a test statistic to a critical value for the test.
The p-value is a number calculated from a statistical test to describe how frequently we’d observe a test statistic as extreme as the one we actually observed, assuming the null hypothesis is true. For example:
- A null hypothesis might be that eating eggs doesn’t make you live longer.
- The alternative hypothesis would be that people who eat eggs do live longer.
If the null hypothesis is correct, the test statistic will tend to be small because life expectancy will be similar for both the egg-eating and non-egg-eating groups in your study. In this case, the p-value will be uniformly distributed between 0 and 1. If the alternative hypothesis is correct, then the test statistic will tend to be large, and the p-value will be small. The smaller the p-value is, the more unlikely it is that the data was generated under the null hypothesis.
How Do You Calculate Correlation and Covariance?
Covariance measures the strength and direction of the linear relationship between two variables. Correlation is the Covariance divided by the product of the standard deviations of the two variables, which scales the measure to be between -1 and 1, and is useful for forming an intuition about how strong the linear relationship is between the two variables.
The corrcoef() function in the NumPy library accepts lists of data and returns a matrix of correlation coefficients. SciPy provides tools to calculate the Pearson (standard linear relationship described above), Spearman (a “nonlinear” correlation measure that measures how well the relationship between two variables can be explained by an unknown increasing or decreasing function), and Kendall Tau (a correlation measure for ordinal data) correlation coefficients.
NumPy also provides a function for calculating covariance, cov(). To use this library, pass two arrays to the function, and it’ll return the covariance matrix. The covariance matrix gives the variance of each of the two variables along the diagonal and the covariance on the off-diagonal part of the matrix. Positive covariance indicates when one variable is greater, the other is likely to also be greater, while negative covariance means the variables move in opposite directions.
Machine Learning Interview Questions
Machine learning is becoming an increasingly important part of data science. Being able to demonstrate an understanding of the foundations of the field in a job interview is crucial. To sharpen your skills, browse these courses on machine learning.
What Is the Difference Between Classification and Regression?
Classification is used to predict discrete class labels. Regression is used to predict a continuous quantity. The two concepts are similar and, sometimes, the word ‘regression’ is used for discrete classification as well. For example, you will hear the phrase ‘logistic regression’ to refer to a particular type of binary classification method.
Explain the Concept of Overfitting and Underfitting
Overfitting refers to a model that’s too flexible and fits the training data too closely, leading to the model having poor performance on new data not originally in the dataset. An overfit model doesn’t “generalize” to new data. An underfit model is too simple to capture the relationship between the features (independent variables) of the model and the labels (dependent variable).
How Do You Select Important Features in a Dataset?
Selecting the right features is crucial when working with large datasets. Consider which features are most critical to the model by evaluating their statistical properties using methods such as correlation-based feature selection to identify subsets of features with the highest correlation with the target feature.
Other tools, such as mutual information and principal component analysis, can also help narrow down features to focus on.
Aside from statistical criteria, it is also useful to use knowledge of the real world problem you’re analyzing to think about what features are likely to be strong predictors. For example, if you’re trying to predict how many eggs will be produced next quarter, clearly the number of hens this quarter will be highly predictive.
Data Visualization and EDA Questions
Exploratory data analysis and visualization techniques help data scientists understand and communicate their results. Check out our selection of data visualization courses.
What Is Exploratory Data Analysis (EDA), and Why Is It Important?
Exploratory data analysis helps identify patterns (and outliers) in a data set. It helps scientists spot potential errors and identify trends in the data and relationships between variables.
Which Data Visualization Tools Do You Prefer and Why?
Interviewers want to know you have some experience with data visualization tools and, ideally, the tools used by the company. Try to gain some hands-on experience with at least one of the following tools:
Be prepared to give examples of projects you’ve completed using these tools.
How Do You Interpret Skewness in Data Distributions?
Skewness refers to the degree of asymmetry in a dataset. If the data is positively skewed, the values are concentrated on the right-hand side of the distribution, with a spread-out tail on the left-hand side. Negatively skewed datasets have more data points on the left-hand side of the distribution.
The normal distribution is an example of a distribution with zero skewness, where a histogram plot of the data would appear symmetrical.
Advanced Data Science Interview Questions
Refresh your memory on advanced data science topics.
Explain Deep Learning vs Traditional Machine Learning
Traditional Machine Learning generally refers to Machine Learning methods that rely on a pre-determined set of features. For example, a regression model with a fixed set of covariates.
Deep Learning is a subset of Machine Learning that uses neural networks to automatically grow in complexity and add new features as needed.
How Do You Handle Imbalanced Datasets?
A common method of handling an imbalanced dataset—a dataset where one label is much more/less common than the other labels—is to generate synthetic samples achieve a more balanced distribution using techniques like SMOTE.
What Is Dimensionality Reduction, and When Is It Used?
Dimensionality reduction is used to handle a large number of variables or observations. It helps reduce the number of dimensions the data scientist or model is dealing with while retaining the meaningful properties of the original data.
Tips to Ace Data Science Interviews
If possible, practice answering common interview questions with friends or colleagues so you’ll feel more comfortable. Prepare a few anecdotes of projects you’ve worked on or times you used various skills so you don’t have to try to remember them on the spot. A few minutes of data science interview prep makes a huge difference in how confident and competent you’ll sound when interviewing..
Follow these tips to make a good first impression:
- Prepare yourself for coding challenges using platforms like LeetCode or HackerRank so you can solve common problems quickly and confidently.
- Practice whiteboard coding in case the interviewer uses it. This will help you avoid feeling confused or stressed if you’re asked to code without your IDE.
- Review math and statistics concepts so you can talk about them confidently.
- Practice communicating clearly and thinking like a data scientist. You have a short time to demonstrate your skills to an interviewer, so use that time to show that you know how to define a problem, outline a solution, collect and structure data, create a model, and interpret the results. It is important to show these skills even if you stumble on the particular details of a question. Practice talking while you think so you can explain your thought process to the interviewer. The interviewer can only know you understand a concept if you say it out loud, so bias towards overexplaining.
- Prepare lots of examples of previous work so you can demonstrate your skills. If this is your first job, use hobby projects as an example. This indicates that you’re self-motivated. But be prepared to answer follow-up questions about the project. Don’t use a project you haven’t put a good amount of thought into.
- Self-study counts for a lot. Enroll in a bootcamp-style course like the complete data science bootcamp to brush up on key data science concepts.
Start Your Data Science Career With Git
Preparing for data science interviews requires mastering technical concepts and practicing problem-solving. By reviewing these questions, studying for certificates in data science, and working on practice projects, you’ll be well-prepared to land your dream job. Start preparing today to ace your next data science interview!
