20 Most Popular Data Science Interview Questions & Answers

With tons of data being generated everyday on cloud storage and social media, data science and scientists who research data have gained immense popularity. There are many new job rules that have started thriving and if you are planning to attend one such interview, this guide should make it easy by posting the 20 most popular  data science certification course  interview questions with detailed answers.

1. What are feature vectors?

The term feature vector refers to an n-dimensional vector of numerical features which are used to represent an object. The number and symbolic characteristics in  machine learning  are referred to as features and feature vectors make it easier to identify them in a mathematical environment.

2. What are the Steps to Create a Decision Tree?

1. Start by taking the entire data set as an input. 
2. A split is a test which has the capability to divide a data into two sets. 
3. Search for a split in the data so that it could maximize the separation of the classes. 
4. Dive the input data by applying the split.
5. Continue the process by following the above steps on the divided data.
6. When you meet the stopping criteria, stop the process.
7. Proceed to pruning. It’s a process of cleaning the tree in case you have used too many splits than required.

3. Describe Root Cause Analysis?

The definition is self-descriptive as you do root cause analysis by getting into the root of an issue or problem to identify the shortcoming. The method was originally used to find the source in industrial accidents.When you remove a factor and if it solves the undesired event in the end, the factor is considered the root cause.

4. What does Logistic Regression mean?

Logistic regression is a strategy used to forecast the binary outcome of a linear combination which contains predictor variables. It is also known as logit model.

5. What does Recommender Systems Denote?

A subclass of information filtering systems, Recommender Systems are used to predict the preferences of a user or the possible ratings they would leave for a product after using it.

6. Explain What is Cross-Validation in Detail?

Cross-validation is the strategy used to predict the outcome of statistical analysis and its ability to generalize based on an independent data set. The technique is primarily used in the background in a scenario where the objective is forecast.It helps a data scientist to determine whether a model will work as intended in practice. Cross-validation allows a user to test a data set in a training phase to avoid problems like overfitting and find how well it can generalize when matched with an independent data set.

7. What does Collaborative Filtering Stand for?

Collaborative filtering is a filtering process used by almost every recommender systems. These systems use the filtering to identify patterns and makes use of collaborative perspectives, multiple data sources with several agents to provide comprehensive information.

8. Are Gradient Descent Methods Designed to Converge at the Similar Point Every Time?

The answer is no. Gradient descent methods may sometimes converge at a local minima or at a local optima point. The end point is determined data and the starting conditions but not all will reach the global optima point.

9. What is the Ultimate Purpose of A/B Testing?

The experiment involves two variables A and B in a randomized environment which will be tested using a statistical hypothesis. By using the A/B testing,the tester will be able to detect changes in a web page and optimize it to get maximize the outcome of a strategy.

10. What are the Disadvantages of Using the Linear Model?

The most commonly known disadvantages of going with the linear model are,

  • The model is not useful to count outcomes or binary outcomes


  • It can’t solve the overfitting problems


  • An assumption on the linearity of the errors


11. What is the definition of Law of Large Numbers?

The Law of Large Numbers is a theorem which is used to describe the result when the same experiment is conducted multiple times. The theorem helps form the fundamentals of frequency style thinking. According to this, sample mean, sample variance and sample standard converge at the same point of estimate.

12. What does Confounding Variables Refer to?

Confounding variables are extraneous variables found in a statistical model. They can correlate directly or indirectly with an independent variable as well as the dependent variable. The estimate will not be able to detect the confounding factor.

13. Give an explanation about Star Schema

Star schema is a traditional database schema used by satellite tables. They use it to map IDs and connect them with physical names or descriptions before migrating the big data to a central fact table with the help of the ID fields.The table is primarily used in real-time applications as they use less memory and are commonly known as lookup tables. The schema sometimes uses multiple layers of summarization to save time and get the information needed quickly.

14. How Frequently Should an Algorithm be Updated?

You should update your algorithm when the particular model is expected to evolve as data is shared through the infrastructure when there is a change made to the underlying data source and also when there is a non-stationarity case.

15. What do Eigenvalue and Eigenvector Denote?

The Eigenvectors are used to understand linear transformation by calculating the number of eigenvectors for a correlation or in a covariance matrix. Eigenvalues are also used in data analysis to denote when a select linear transformation performs an action either by flipping, compressing or by stretching.

16. What are the Different Types of Biases that you may Witness During Sampling?

The types are selection bias, undercoverage bias, survivorship bias.

17. What is Selective Bias?

Selective bias refers to an error which is created because of a non-random population sample.

18. What does Survivorship Bias Stand for?

Survivorship bias is a method in which a logical error takes place because it supports some processes that survived while ignoring others that as they are not so prominent. The bias leads to inaccurate conclusions.

19. Do you Know How Resampling is Done?

Resampling is done when a data scientist is interested the sample statistics accuracy is to be estimated, when they need to substitute labels on data labels, and using random subsets to validate models.

20. Working for a Random Forest?

A strong learner is provided by the combination of weak learners and this involves creating a decision tree with a random sample of mm predictors work as split candidates and the majority rule is prediction.