Data Science Interview Questions1. What exactly does the phrase "Data Science" imply?Data Science is an interdisciplinary discipline that encompasses a variety of scientific procedures, algorithms, tools, and machine learning approaches that work together to uncover common patterns and gain useful insights from raw input data using statistical and mathematical analysis. 2. What is the distinction between data science and data analytics?Data science is altering data using a variety of technical analysis approaches to derive useful insights that data analysts may apply to their business scenarios.Data analytics is concerned with verifying current hypotheses and facts, as well as providing answers to queries for a more efficient and successful business decision-making process.Data Science fosters innovation by providing answers to questions that help people make connections and solve challenges in the future. Data analytics is concerned with extracting current meaning from past contexts, whereas data science is concerned with predictive modeling.Data science is a vast field that employs a variety of mathematical and scientific methods and algorithms to solve complicated issues, whereas data analytics is a subset of data science. 4. Make a list of the overfitting and underfitting circumstances.Overfitting: The model only works well with a small set of training data. If the model is given any fresh data as input, it fails to provide any results. These circumstances arise as a result of the model's low bias and large variance. Overfitting is more common in decision trees.Underfitting: In this case, the model is so simple that it is unable to recognize the proper connection in the data, and hence performs poorly even on test data. This can happen when there is a lot of bias and little variation. Underfitting is more common in linear regression. 5. Distinguish between data in long and wide formats.a lengthy format Data Data in a Wide FormatEach row of the data reflects a subject's one-time information. Each subject's data would be organized in different/multiple rows. The repeated replies of a subject are divided into various columns in this example.When viewing rows as groupings, the data may be identified.By viewing columns as groups, the data may be identified.This data format is most typically used in R analysis and for writing log files at the end of each experiment.This data format is most widely used in stats programs for repeated measures ANOVAs and is seldom utilized in R analysis.