30 Data Science Interview Questions and Answers
Demand for data science experts has been rising for decades. In 2019, data scientists saw a 56 percent increase in job openings, according to LinkedIn.1
If you’re pursuing a career in data science, you can expect to sit down for an interview with a recruiter or hiring manager at some point. This interview will be like most other job interviews, but you’ll need to be ready to answer questions related to data science.
Here, we’ll examine thirty questions you might encounter in an interview for a data science position.
1. What is Sampling?
In statistical analysis, sampling is a technique used to select a subset of data points representing a more extensive data set to be examined.
2. What Are Correlation and Covariance in Statistics?
Both describe the degree to which variables tend to deviate from their expected values. While covariance indicates the direction of a linear relationship between variables, correlation indicates both the strength and direction of the relationship between variables.
3. What is Statistical Interaction?
A statistical interaction describes a situation in which an input variable's effect on the dependent variable (the output) depends on the state of a second input variable. For example, in a pain management trial, the dependent variable (level of pain) might depend on the dosage of medication provided (input) but also on the age of the individual taking the dose.
4. What is Selection Bias?
Selection bias is when proper randomization is not achieved in the selection of data for analysis, so the sample obtained is not representative of a larger data set.
5. What Does the Term “Normal Distribution” Mean?
This is a probability function that shows that data near the mean is more frequent than data far from the mean. It usually appears as a bell curve in a graph.
6. What is Linear Regression?
Regression is the idea that a set of predictor variables will determine an outcome. Linear regression is a linear (straight-line) approach to modeling the relationship between a dependent variable and one or more independent variables.
7. What is the Purpose of A/B Testing?
This is a form of statistical hypothesis testing. A hypothesis is made about the relationship between two data sets, which are then compared to determine if the hypothesis is correct.
8. What Does it Mean to Clean a Data Set?
Cleaning a data set involves fixing or removing duplicate, incorrect or incorrectly formatted data within a data set. Doing so improves data quality.
9. Which programming languages are you most comfortable working with?
10. How is Memory Managed in Python?
According to python.org, “Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.”2
11. What Data Types Does Python Support?
Python provides built-in data types like dict, list, set and tuple.
12. What Command is used to Store R Objects in a File?
The function save() command.
13. What is the Purpose of Group Functions in SQL?
These are built-in SQL functions that operate on groups of rows, returning a single value for the entire croup. They are COUNT, MAX, MIN, AVG, SUM and DISTINCT.
14. What is the Difference Between SQL and MySQL?
SQL—Structured Query Language—is a language used to query a database. It is the basic language used for all databases. MySQL is a database management system.
15. How is K-NN Different from K-Means Clustering?
K-nearest neighbors (K-NN) is a supervised classification algorithm that can classify unlabeled data by analysis of the K number of the nearest data points. In this case, K is labeled by the engineer, which is what makes the classification algorithm “supervised.”
K-means clustering is an unsupervised clustering algorithm that gradually learns how to cluster unlabeled data points into groups by analyzing the mean distance between the points. In this case, K represents the number of groups in which the data is gathered.
16. What is precision?
Precision describes the percentage of positive predictions in the model that turned out to be correct.
17. Please Explain the 80/20 Rule.
Known as the Pareto Principle, the 80/20 Rule is the observation that 80 percent of outputs come from 20 percent of inputs.
18. What is an Exact Test?
Fisher’s exact test is a statistical significance test used to analyze contingency tables. It is used when you have two nominal variables, and its primary purpose is to investigate of proportions for one nominal variable are different among values of the other.
19. What is Root Cause Analysis?
Root cause analysis (RCA) is “a technique used to detect the origin of deviations in response parameters within a data set,” according to Daniel Borchert et al.3
Machine Learning Questions
20. What are Parametric Models?
This refers to any model that captures information within a finite number of parameters.
21. What is the Curse of Dimensionality?
This refers to phenomena that occur when classifying and analyzing high dimensional data which do not occur in low dimensional spaces. When moving to higher dimensions, the volume of space represented grows so much that the data becomes sparse.
22. What is the Difference Between Machine Learning and Artificial Intelligence (AI)?
Artificial intelligence is a broad term that refers to the ability of machines to carry out human-like tasks. In contrast, machine learning is an applied example of AI in which machines learn for themselves using data inputs.
23. What is Supervised Learning?
In supervised learning, an algorithm learns on a labeled data set. This essentially provides the algorithm with a key that it can use to check the accuracy of its training data.
24. What is Unsupervised Learning?
An unsupervised learning model trains an algorithm using unlabeled data. Instead, the algorithm tries to understand patterns within the data on its own.
25. What is “Pruning” in Relation to a Decision Tree?
This refers to condensing and optimizing a decision tree by removing sections that are redundant or not important. A decision tree is a support tool that maps decisions and their possible outcomes in a tree-like model.
26. What is Bagging?
Bagging—also called bootstrap aggregating—is a method of decreasing variances in prediction models by generating additional training data from a data set. This is accomplished through combinations and repetitions of the data set, producing multiple sets from the original.
27. What Are Your Personal Goals?
Interviewers usually ask this question because they want to know if you intend to stay with the organization for a while or if you’re using their position to boost your resume. The best way to answer is to determine what your long-term and short-term goals are beforehand.
Show the interviewer that you are passionate about the industry. Showing that your goals align with those of the employer can help as well.
28. How Do You Work with Others?
Working in data science doesn’t mean working alone. Provide your interviewer with examples of how you’ve worked with a team to accomplish results in the past. You can use an example from work, school or even your personal life.
29. Name a Specific Time You Took the Initiative.
When interviewers ask if you are a “self-starter,” this is what they're asking. This is a common question, so don’t hesitate to prepare an answer ahead of time. Describe not just how you took the initiative but what your motivations were for doing so.
30. What’s a Project That You're Proud Of?
Again, this can refer to a work project, a school project or even a project you completed while volunteering. Don’t forget to explain how you completed the project and why you were proud once you’d finished.
Preparing for a Data Science Interview
There’s no single way to prepare for an interview successfully. Start with a good night’s sleep, a healthy breakfast and a practice run. Beyond that, reviewing some of the most frequently asked questions can help you feel a bit more prepared and confident once you start the process.
To better prepare yourself for more opportunities in the data science career, try EmergingEd’s data science courses today.
1. Retrieved on October 21, 2020, from techrepublic.com/article/why-data-scientist-is-the-most-promising-job-of-2019/
2. Retrieved on October 21, 2020, from docs.python.org/3/c-api/memory.html
3. Retrieved on October 21, 2020, from link.springer.com/article/10.1007/s00449-018-2029-6