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5 min readMay 22, 2023

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15 Data Science Interview Questions

Data science is an interdisciplinary field which mines raw data, analyses it, and discovers patterns that can be used to extract valuable insights.

The core foundation of data science is statistics, machine learning, computer science, deep learning, data analysis, data visualisation, and various other technologies. Due to the importance of data, data science has grown in popularity over the years.

Data is regarded as the future’s new oil, which, when properly analysed and harnessed, can be extremely beneficial to stakeholders. Not only that but a data scientist is exposed to work in a variety of domains, solving real-world practical problems using cutting-edge technologies.

In this article, we will look at the most frequently asked Data Science Interview Questions, which will be useful for both aspiring and experienced data scientists.

Data Science Interview Questions

Q1. What is Data Science?

A. Data Science is an interdisciplinary field that consists of various scientific processes, tools, algorithms and machine learning techniques that work to help find common patterns and gather meaningful insights from given raw input data using statistical and mathematical analysis.

Q2. What is the difference between data science and data analytics?

A. Data science is the task of transforming data through the use of various technical analysis methods in order to extract meaningful insights that a data analyst can apply to their business scenarios. On the other hand data analytics involves testing existing hypotheses and information and providing answers to questions in order to make better and more effective business decisions.

Q3. What are the conditions of Underfitting and Overfitting?

A. Underfitting: It occurs when the model is so simple that it is unable to identify the correct relationship in the data and thus performs poorly even on test data.

Overfitting: occurs when a model performs well only on a subset of the training data. When new data is fed into the model, it fails to produce any results.

Q4. What is the difference between Eigenvectors and Eigenvalues?

A. Eigenvectors are column vectors or unit vectors with the same length/magnitude. They are also known as right vectors and Eigenvalues are coefficients that are applied to eigenvectors to give them different length or magnitude values.

Q5. When is Resampling performed?

A. It is done to ensure that the model is good enough by training it on different patterns in a dataset to ensure that variations are handled. It is also done when models need to be validated using random subsets or when labelling data points while performing tests.

Q6. What do you mean by Imbalanced Data?

A. When data is distributed unequally across different categories, it is said to be highly imbalanced. These datasets cause an error in model performance and inaccuracy.

Q7. Are there any differences in the expected and mean values?

A. There aren’t several differences between these two, but it’s important to note that they’re used in different contexts. In general, the mean value refers to the probability distribution, whereas the expected value is used in contexts involving random variables.

Q8. What do you mean by Survivorship Bias?

A. This bias refers to a logical error that occurs when focusing on aspects that survived a process and ignoring those that did not work due to a lack of prominence. This bias can lead to incorrect conclusions.

Q9. Define the variables that cause confounding effects.

A. These variables are a type of extraneous variable that influences both independent and dependent variables, resulting in incorrect association and mathematical relationships between variables that are associated but not causally related to each other.

Q10. What is the confusion matrix?

A. It is a matrix with two rows and two columns. It receives four outputs from a binary classifier. It is used to calculate specificity, error rate, precision, accuracy, sensitivity, and recall.

Q11. Define logistic regression?

A. The logit model is another name for logistic regression. It is a method for predicting a binary outcome from a linear combination of variables (referred to as the predictor variables).

Q12. Define Linear Regression?

A. Linear regression is a technique that predicts the value of a variable Y based on the value of a predictor variable X. Y is known as the criterion variable.

Q13. What is deep learning?

A. Deep learning is a machine learning paradigm. Multiple layers of processing are used in deep learning to extract high-value features from data.

Q14. Define Gradient.

A. Gradient is a property measure that shows how much the output has changed in relation to a small change in the input.

Q15. Define Gradient Descent

A. Gradient descent is a minimization algorithm that reduces the activation function to its simplest form. It can minimise any function that is given to it, but it is usually only given the activation function.

Conclusion

In conclusion, data science is a rapidly growing field that involves extracting valuable insights from raw data through the use of statistical analysis, machine learning, and various other technologies.

How do you find these data science interview questions helpful for you? These questions covered fundamental concepts such as data science, the difference between data science and data analytics, underfitting and overfitting, eigenvectors and eigenvalues, resampling, imbalanced data, and more. Familiarity with these concepts will be beneficial for both aspiring and experienced data scientists preparing for interviews.

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