Top 50 data science interview questions in 2024

Top 50 data science interview questions in 2024

The tech world is constantly that too at a rapid speed. One of the relatively newer concepts is data science which is on the rise. Given the increase in demand for data science professionals, there are more opportunities now. With a good data science course, you’ll be prepared for any job you want. We understand how overwhelming it must be for aspirants to deal with data science interview questions and job applications. It is good to have a reference before you navigate into the job market. Having an idea of what kind of data science questions you get asked in an interview makes things easier. There are several skill sets that individuals can develop through a data science training course before preparing for an interview. Recruiters also do a thorough assessment via machine learning interview questions. What is data science? Data science is an interdisciplinary approach to mine and analyse raw data. The purpose is to recognise patterns and extract useful insights from them. The core foundation of data science consists of concepts from statistics, data analysis, computer science, data visualisation, deep learning and of course machine learning. So if you are seeking a job in this industry be ready to face my interview questions as well! Knowing just data science questions won’t be enough. What do recruiters seek through data science interview questions? The focus is to see whether an interviewee has strong basics and clarity in practical applications. Apart from proper knowledge of data science core tools and processes, you must be prepared for ML interview questions as well. Here we discuss a bunch of data science questions along with machine learning questions and answers for your reference. Check out these probable questions if you are someone aspiring to be a data scientist. We give you an idea of the kind of questions you might encounter and hopefully crack your job interview! What are the top data science interview questions that interviewers might ask? 1) How would you define data science? Ans. Sounds like an easy question, right? But you might get asked.Data science as a term emerged from the evolution of data analysis, statistics and big data. It is an interdisciplinary field which extracts insights from a wide range of data using several scientific methods. Analysis of raw data leads us to hidden patterns. 2) State the difference between data science and machine learning. Ans. On one hand, data science has algorithms, machine learning, tools and processes that help in pattern recognition from raw data. On the other hand, we have a branch of computer science called machine learning that teaches system programs to learn and improve automatically. 3) What do you understand about a decision tree? Ans. We use a decision tree in operation research, strategic planning and machine learning. It consists of endpoints that connect to a branch called a node. The more the nodes, the more accurate the decision. The decision is made at the leaves of the tree which are the last nodes. 4) Explain prior probability and likelihood. Ans. In a data set, the proportion of dependent variables is the prior probability. Whereas, the classification probability of a given observant in another variable’s presence is what likelihood entails. 5) What does Recommender Systems mean? Ans. Recommender systems assist in users’ preference prediction. In simpler terms, it is a sub-category of information filtering techniques. 6) What biases can occur during sampling? Ans. We can fall prey to three types of biases during sampling which are-     ● Survivorship bias     ● Selection bias     ● Under coverage bias 7) Why is resampling required? Ans. There might arise a situation where we need to resample. For example, the following cases-     ● Label substitution on data points for conducting tests     ● Estimation of sample statistics accuracy by drawing randomly with replacement     ● Using random subsets for model validity 8) Why is data cleaning important in data analysis? Ans. Data cleaning has several purposes in data analysis. However, two of its most important ones are-     ● Data cleaning is useful for data transformation so that it is easier to work     ● Data cleaning is also helpful in Increasing machine learning model accuracy 9) What do you mean by Power Analysis? Ans. For an experimental design, power analysis is quite an integral part. We can estimate the required sample size to determine the effect of a given size with a particular assurance level. In a constrained sample size, we can deploy a certain probability with power analysis. 10) What do you understand by collaborative filtering? Ans. Collaborative filtering is a technique that helps to filter out processes that recognise patterns and data by agents, collaborative perspectives or numerous information sources. Most recommender systems use collaborative filtering for pattern recognition. 11) Why do we use A/B Testing? Ans. The purpose of A/B testing is to keep track of any changes made to a website to increase the strategy outcome. It is a statistical hypothesis testing method required for randomised experiments that uses two variables, i.e., A and B. 12) How would you define a P-value? What significance does a P-value have? Ans. The probability that an observation is made regarding a data set is a random chance is what the p-value expresses. If the p-value is under 5% then it is strong evidence which supports the findings against the null hypothesis. So the higher the p value the likelihood of the result being valid decreases. 13) What is meant by linear regression and logistic regression? Ans.  Method Description Linear Regression In linear regression, we use statistical methods to predict the score of variable Y in comparison to the score of variable X. Here, X is the predictor variable whereas Y is the criterion variable. Logistic Regression Logical regression, otherwise known as logistic regression is also a statistical method. This is the technique for binary outcome prediction from a linear combination of predictor variables. 14)