## Common Data Science Interview Questions & Answers

Data science is a fun and exciting field to be in. You get to work with a lot of numbers, build models of real-world situations, and if you’re really good at it — build solutions to big problems. It’s more than just a job title. Data science is a definition that indicates how you will approach problems and how you will solve them. This mindset can be applied to any job of your choosing; it’s not confined to just an analytical position.

Data science is a field of study focusing on the use of data to solve problems. It’s an interdisciplinary field that draws upon computer science, statistics, mathematics, and more. Data Science is a very broad field that has many applications in software (e.g. machine learning), business (e.g., market research and analysis), politics (e.g., census analysis), social sciences (e.g., psychology and sociology), and beyond.

As a data scientist, you’ll need to be able to communicate your knowledge of the various machine learning algorithms and statistical tests you’re using. You’ll also have to write reports for non-technical users who may not know how these processes work.

Here are some commonly asked Data Science interview questions:

**Related: **Steps to Learn Data Science as a Beginner

## Data Science interview questions

### 1. Differentiate between data analytics and data science.

The difference between data analytics and data science isn’t just a matter of semantics. The two fields have different definitions, different goals, and ultimately, different career paths.

**Data analytics** is a commercial application of data science. Data analytics describes the gathering, organizing, and analyzing of data for business purposes. The analyst will use statistical methods to apply mathematics in order to solve business problems.

**Data scientists** take the findings from data analytics and use them to create a solution. They may use statistics, machine learning, and computer programming to find solutions to problems that require human interaction such as customer service issues.

A key difference between data analytics and data science is that while data analysts focus primarily on cleaning and organizing a company’s existing data through programming, visualization tools, and statistical methods, a data scientist focuses more on creating models for predicting outcomes based on existing data sets.

### 2. What is the confusion matrix?

The confusion matrix is a fundamental concept in data science, and it is one of the most important tools for analyzing machine learning models. The confusion matrix is essentially a table that describes how well different classes were predicted by your model.

The confusion matrix tells you what percentage of samples were correctly classified and what percentage were incorrectly classified.

### 3. What are the advantages of sampling?

There are many advantages to sampling in data science. In most cases, it’s a much faster method than complete data analysis.

It gives you a rough idea of what your complete results will look like, which can be useful for testing and debugging purposes. It gives you a sense of the range of possible outputs, which is important because you don’t always know what to expect from the data you’re working with.

And it saves a lot of time, giving you an overall idea of the direction in which your results are trending.

### 4. What is cross-validation?

Cross-validation is a method used to determine how well a predictive model will perform on future data where it was not trained. A lot of times this will be used to evaluate how well a machine learning model will generalize.

The cross-validation method is most often used in machine learning algorithms like k-nearest neighbors, decision trees, random forests, and logistic regression.

### 5. Define deep learning.

Deep learning is a subfield of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a network of connected layers. Each layer receives input and transforms it into an output, which is then used as the input for the next layer in the network.

The goal is to train the neural network using large amounts of data so that it can learn to perform certain tasks without being explicitly programmed. For example, a neural network trained to recognize images could be used to identify objects, faces, or even emotions in photos.

### 6. Differentiate between regression and classification.

Regression modeling is used to predict numerical values, while classification is used to predict categorical values.

The difference between the two lies in which type of variable is the target and which is the independent.

In a **regression** problem, the independent variable is numeric (such as income or age), and the dependent variable is also numeric (income or age change).

In a **classification** problem, however, the independent variable can be categorical (such as gender or marital status), whereas the dependent variable is categorical.

### 7. Differentiate between normalization and standardization.

While the terms “normalization” and “standardization” are often used interchangeably, there is an important distinction between the two. Both techniques can be used to improve the accuracy of a machine learning system. They differ in the way that they convert and scale the input data into a format that is suitable for the model.

**Normalization** refers to a systematic approach to converting data into a consistent scheme, while **standardization** is a process that ensures that data conforms to a specific set of rules.

Both processes are essential in order to ensure data quality.

### 8. What is selection bias?

Selection bias is a type of error that occurs in research when the sample is not representative of the population. It can result from non-random sampling, or when data are collected using methods that are biased.

### 9. Define regularization.

Regularization is a method for preventing overfitting in machine learning algorithms. It does so by increasing the generalization error of the model at training time, which is usually acceptable as long as it comes at the cost of reduced complexity of the model.

Regularization is particularly useful when the dataset you are working with has an abundance of features, which can make it more difficult for a model to pick up the important relationships and correlations.

### 10. What is the meaning of imbalanced data?

In data science and statistics, imbalanced data refers to the occurrence when a particular class or group occurs with a lower frequency than the rest of the classes or groups in an input dataset.

The imbalanced data problem is one of the most challenging problems in machine learning and statistics. It often requires different approaches to achieve good results. The most common solution for this problem is to reduce the dimensionality of your datasets. This means that you should discard some features and variables you have collected from your dataset.

### 11. Differentiate between overfitting and underfitting.

**Overfitting** is when our models are too complex, so they match the training data really well but do not generalize to new data.

**Underfitting** is when our models are too simple, so they don’t match the training data that well and also do not generalize to new data.

### 12. Differentiate between TPR and FPR.

The **True Positive Rate (TPR)** of a classifier is the ratio of its correct predictions over the sum of both correct and wrong predictions.

The **False Positive Rate (FPR)** is a measure of the expected proportion of positive test results that are incorrectly identified by a classifier as being positive. In other words, it is a measure of the number of false positives per positive result.

### 13. What is RMSE?

Root Mean Square Error (RMSE) is a measure of the prediction accuracy of an estimation algorithm. It is used to quantify the difference between predicted and actual values.

Practical applications of RMSE include:

- data cleaning and data preparation
- model validation
- model selection and evaluation
- forecasting, and
- estimation

### 14. Differentiate between CNN and RNN.

CNN and RNN are two of the most widely used deep learning models. They both have had a lot of applications in recent years.

CNN is a feed-forward network while RNN is recursive. You can use CNN to detect the objects in the images, while RNN can be used for language modeling and other NLP tasks.

### 15. What is NLP?

NLP or Natural Language Processing is a branch of Artificial Intelligence that deals with the interactions between humans and computers. It basically deals with the understanding and generation of human language by computers. NLP is an integral part of data science as it helps to create intelligent machines which have the ability to understand human languages.

NLP is an interdisciplinary field dealing with the study, design, and implementation of algorithms that allow for interaction between computers and humans in a natural way.

### 16. What is survivorship bias?

Survivorship bias is a type of selection bias that occurs when a sample is drawn from a population where the probability of including members depends on the outcome of interest.

Survivorship bias can arise in experiments, observational studies, and when drawing conclusions from existing data sets.

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## Final words

Most of the above-mentioned questions are asked over and over in data science interviews. But also prepare to be asked questions about your background and interests.

We have put together a detailed guide to technical interviews, so you will definitely get some idea.

Also see:

If you have a related query, feel free to let us know in the comments below.

Also, kindly share this article with your friends who are learning data science and preparing for the interviews.

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