Top Questions to Prepare for Data Science Job Interviews

A range of data science interview questions to look out for when applying for jobs

Data science is an interdisciplinary field that mines unprocessed data, analyzes it, and discovers patterns from which to derive useful insights. The key technologies of data science include statistics, computer science, machine learning, deep learning, data analysis, and data visualization.

1. What is Data Science?

Data Science is an interdisciplinary field made up of several scientific techniques, tools, algorithms, and machine-learning strategies with the goal of extracting patterns and useful knowledge from the given raw input data.

2. What distinguishes data science from data analytics?

Data science is the process of transforming data using a variety of technical analysis approaches in order to produce insightful findings that a data analyst can subsequently apply to various business contexts.

In order to make business-related decision-making more effective and efficient, data analytics is concerned with analyzing the information and theories already in existence.

3. What are Eigenvectors and Eigenvalues?

Column vectors or unit vectors with a length/magnitude of 1 are known as eigenvectors. Also known as right vectors. When eigenvalues are applied to eigenvectors, different lengths or magnitudes are assigned to the vectors.

Eigen decomposition is the process of dissecting a matrix into its Eigenvalues and Eigenvectors. They are subsequently included in machine learning techniques like PCA (Principal Component Analysis) in order to extract insightful information from the provided matrix.

4. When is resampling done?

Re-sampling is a technique for sampling data that is used to increase precision and quantify the uncertainty of population parameters. It is done to make sure the model is adequate by training it on various dataset patterns to make sure variations are handled. Also, it is done while doing tests while changing the labels on data points, or when models need to be validated using random subsets.

5. What do you understand by imbalanced Data?

Data is said to be severely imbalanced if it is distributed unevenly over multiple categories. The model performance is imprecise and erroneous as a result of these datasets.

6. What do you understand by Survivorship Bias?

This bias refers to the illogical mistake of concentrating on elements that have withstood some processes and ignoring those that have failed because they were not given as much attention. The result of this bias could be incorrect judgments.

7. Define confounding variables.

Confounders are sometimes referred to as confounding variables. These variables are a particular category of auxiliary variables that have an impact on both independent and dependent variables, leading to erroneous mathematical relationships between variables that are correlated but are not incidentally related to one another.

8. Define and explain selection bias?

Selection bias occurs when the researcher must decide which subject to explore. Selection bias occurs when study participants are picked in a non-random manner. The selection bias is often referred to as the selection effect. The selection bias is a result of the sample-gathering procedure.

9. What is the difference between the Test set and the validation set?

The trained model's performance is tested or assessed using the test set. It assesses the model's capacity for prediction.

The training set includes the validation set, which is used to choose parameters to prevent model overfitting.