Most common data science questions asked in AI interviews.
The world’s leading technologies are artificial intelligence and data science. To fully harness the potential of these technologies, large IT companies are employing qualified individuals in this industry.
In this article, we have outlined the frequent data science questions asked in AI interviews:
What does “data science” exactly mean?
Data science is an interdisciplinary discipline that includes a variety of scientific procedures, algorithms, tools, and machine learning approaches that use statistical and mathematical analysis to find common patterns and derive valuable insights from raw input data.
Difference between data science and data analytics?
Data science is the process of translating data into valuable insights that data analysts may apply in business settings utilizing various technical analysis approaches.
Data analytics focuses on verifying existing hypotheses and facts to answer complicated issues for a better and more effective business-related decision-making process.
Data science supports innovation by answering questions to develop solutions for future challenges, whereas data analytics focuses on obtaining solutions from existing historical data through predictive modelling.
Do you know what linear regression is?
Linear regression is a supervised learning approach for determining the linear connection between dependent and independent variables. The predictor, or independent variable, is one of the factors in play, while the response, or dependent variable, is the other.
What differentiates data science from traditional application programming?
In traditional application programming paradigms, we analyze the input, predict the outcome and write the code which contains the rules and statements required to convert the supplied input data into the desired output. It’s tough to write these regulations. This process is shifted in data science since the rules are automatically created or learnt from the data.
What is bias in data science?
Bias is a form of error in a data science model when an algorithm isn’t powerful enough to capture the underlying patterns or trends in the data. When the data is too complex for the algorithm to grasp, it creates a model based on simplistic assumptions.
What is “imbalanced data”?
Unequal distribution of data across several categories is said to be imbalanced data. These datasets cause a performance problem in the model, as well as inaccuracies.
Why is data cleansing so important?
Gathering insights by running an algorithm on any data, it is vital to have correct and clean data that contains only relevant information. Unclean data frequently leads to poor or erroneous insights and predictions, which can be harmful. Data cleaning assists in identifying and correcting any structural errors in data and removing any duplicates to retain the data’s integrity.
Define artificial neural networks (ANN).
Artificial neural networks (ANNs) are a subset of techniques that have transformed machine learning. It assists the user in adapting to changing inputs. As a consequence, the network produces the best results possible without having to modify the output criteria.