# Top 15 Questions for a Data Science Interview

## Introduction

Data Science is increasingly becoming a job everyone is after. Glassdoor, recently conducted a survey on ‘50 best Jobs in America’ and Data Science ranked number one on the survey. Correspondingly, the requirement for a Data Scientist exists in every industry, from journalism to technology. So, if you have what it takes to be a Data Scientist, it is time to prepare yourself to nail the job interviews.

Have a look below to know the most frequently asked questions in a Data Science interview!

### What is Data Science and what are its requirements?

Data Science is an amalgamation of various fields like Statistics, Mathematics, Programming and domain knowledge. The domain knowledge helps analyze the data present and make predictions for future.

### How is Data Science different from Data Analytics?

– Data Scientists are expected to know the business domain knowledge in addition to advanced data visualization skills, as opposed to a data analyst.

– A Data analyst usually works on data from a single source, whereas a data scientist is expected to analyse data from various disconnected sources.

– Questions, whose solutions are likely to benefit the business are formulated by a data scientist. A data analyst, however, is responsible for solving the questions the businesses give them.

– A data Scientist is expected to know how to build statistical models and should be well versed in Machine Learning. This is however not expected of a Data Analyst.### Which Language is best suitable for Data Science, R or Python?

Even though statistical analysis uses R it isn’t best suited for Data Science. Thus making Python the best contender as it is easy to learn and is extremely coherent, compact and object oriented language.

### Compare the languages R and Python?

Python is a general-purpose programming language which is easy to write as well as understand. It reads more like a regular human comprehensible language such as English. Over the past few years, its popularity has been increasing. Therefore, it now finds uses in a wide variety of fields including web development and scientific computing. R, on the other hand, is a language that is built almost exclusively for statistical computing. Researchers in statistics widely use R.

To know more about the difference between the two, have a look at our article on ‘Comparing Python and R. Which one should you use?’

### What steps does a Data Science analysis involve?

Data Science analysis can be divided into six steps:

- Asking an interesting question
- Designing a data collection program
- Collecting and reviewing the data
- Cleansing of Data
- Processing the data
- Model and analyze the data sets
- Visualize and communicate the results

### What are the most important skills for a Data Scientist to have?

A Data Scientist is required to have a variety of skills as their job is multidisciplinary. Some of the must have skills for a Data Scientist include:

- Problem-Solving Skills
- Programming
- Statistics and Mathematics
- Good Communication Skills
- Effectively use Visualization techniques to present data
- Machine Learning
- Having a basic knowledge of Big Data processing platforms

### How is Data Science different from Machine Learning?

Machine learning deals with building and implementing production machine learning systems. They also look after the health of machine learning systems; this includes speed, reliability and performance. Data Science, however, evaluates potential or existing approaches, features and algorithms to help improve machine learning systems. They also analyse the impact of machine learning algorithms on key metrics.

### What are Univariate, Bivariate and Multivariate analyses?

Univariate, Bivariate and Multivariate analyses methodologies have a single, double or multiple variables. A Univariate analysis has one variable and summarizes data and finds patterns in it to make actionable decisions. A bivariate analysis analyses relationship between two data sets. In like manner the Multivariate analyses deals with multiple data sets.

### What is Linear Regression?

Linear regression, a commonly used predictive analysis is the most basic form of regression. In its simplest form a linear regression equation (including one dependent and one independent variable) has the formula of y = c + b*x, where y = estimated dependent score, c= constant, b = regression coefficients, and x = independent variable.

### What is Logistic Regression?

Logistic regression is the regression analyses used when the dependent variable is binary. The analyses is predictive and describes data.

### What is Recommender System?

The recommender systems works with collaborative and content-based filtering. The system uses the past behaviour of a person to build a model for the future. It predicts the future product buying, movie viewing or book reading that people might engage in.

### How is Statistics used in Data Science?

Statistics is one of the major subfield in Data Science and is used for various purposes:

- Designing and interpreting experiments
- Building models that are predicting signal; not noise
- To turn big data into a big picture
- Understanding user engagement, retention, conversion and leads
- To estimate intelligently
- Lastly, to tell a story with Data

### What is Data Cleansing?

The process of Data Cleansing involves detecting and correcting data that is corrupt or inaccurate from the Data sets.

### What is the importance of Data Cleansing in Data Science?

Data Cleansing is one of the most important aspects of being a Data Scientist. It is very important to cleanse the data before analyzing and interpreting it. Bad quality data could lead to high costs for a business. Moreover, bad data can cause discrepancies in the accuracy of the analysis.

### What challenges does a Data Scientist usually face?

**Handling data from multiple sources**: The value of the data mined increases when a data scientist is able to reach across the expanse of the data landscape and access data from multiple platforms and data sources.**Predicting Outcomes from the data**: The final result is not necessarily the same as predicting data. Results might be unexpected even when datasets are given.**Communicating with people**: Understanding the data is not enough for a Data Scientist. It is also important to make it presentable to the people as well.

## Endnotes

Knowing the answers to these questions can help you ace the interviews process for Data Science. However, if you are new to data science and want to know more about this field read our articles on ‘How to become a Data Scientist?’, ‘A Day in the Life of a Data Scientist’, ‘How to become a Freelance Data Scientist?’.