Here are the top 10 general data science questions that are frequently asked in AI interviews.

Over the years, data science has turned into an interdisciplinary field that mines raw data, analyses it, and comes up with the patterns that are used to draw valuable insights for business profits. Data is considered as the new catalyst of the future that will fuel business growth if analyzed and harnessed properly. Artificial intelligence and data science are the leading technologies in the world. Big tech organizations are hiring skilled professionals in this field to properly harness the power of these technologies. In this article, we list down the top 10 general data science questions asked in AI interviews.

What do you understand by data science?

Data science is an interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques, working to search common patterns and gather sensible insights from the raw input data using statistical and mathematical analysis.

Do you understand linear regression?

Linear regression is a supervised learning algorithm that helps in understanding the linear relationship between the dependent and the independent variables. There are two variables in play, one is the predictor or the independent variable and the other is the response or the dependent variable.

What is the difference between data science and data analytics?

Data science involves the task of transforming data by using various technical analysis methods into meaningful insights which the data analysts can apply in business scenarios. On the other hand, data analytics deals with checking the existing hypothesis and information to answer complex questions for a better and more effective business-related decision-making process. Data science drives innovation by answering questions to build solutions for futuristic problems, whereas data analytics focuses on getting solutions from existing historical data using predictive modeling.

How is data science different from traditional application programming?

In traditional programming paradigms, we analyze the input, figure out the expected output, and write the code, which contains rules and statements needed to transform the provided input into the expected output. These rules are difficult to write. In data science, this process is shifted as the rules are automatically generated or learned from the given data.

What is bias in data science?

Bias is a type of error that occurs in a data science model because of using an algorithm that is not strong enough to capture the underlying patterns or trends that exist in data. This error occurs when the data is too complicated for the algorithm to understand, so it builds a model that makes simple assumptions.

What do you understand by imbalanced data?

The data is said to be highly imbalanced if it is unequally distributed across different categories. These datasets result in an error in model performance and result in inaccuracy.

Why is data cleaning crucial?

While running an algorithm on any data, to gather proper insights, it is necessary to have correct and clean data that contains only relevant information. Unclean data often results in poor or incorrect insights and predictions that can have damaging effects. Data cleaning helps to identify and fix any structural issues in data and removes any duplicates to maintain the authenticity of the information.

Discuss artificial neural networks (ANN).

Artificial neural networks are a special set of algorithms that have revolutionized machine learning. It helps the user to adapt to the changing inputs. So, the network generates the best possible results without redesigning the output criteria.

What is a normal distribution?

A normal distribution is a set of continuous variables spread across a normal curve or in the shape of a bell curve. Users can consider it as a continuous probability distribution, which is quite useful in statistics. It analyses the variables and their relationships while using the normal distribution curve.

What is variance in data science?

Variance is a type of error that occurs in a data science model when the model ends up being too complex and learns features from data, along with the noise that exists in it.