5 Most Easy Data Science Interview Questions & Their Answers

Most Common Data Science Interview Questions

If you’re planning to move your career in the field of Data Science, you’ve come to the right place. In this article, we’re going to discuss the 5 Most Common Data Science Interview Questions that you might face in an interview.

Data Science is the latest field in the world of technology which helps in mining, analyzing, and cleaning the data to get better insights about the business. As a data scientist, one must require vast knowledge of statistics, advanced mathematics, machine learning, data analysis, and data visualization tools.

If you’re a bit confused about the job roles in the data domain, you must read 5 Simple Differences Between Data Analyst and Data Scientist.

When you pursue your career as a professional in the Data Science field, you’ve given numerous tangible as well as complex tasks. You must have a good knowledge of programming languages like Python, or R, machine learning algorithms, data models, and software like Power BI, and Tableau.

5 Most Common Data Science Interview Questions

5 Data Science Interview Questions & Their Answers

Here are the 10 most common questions that might get asked from you when you go for an interview in the Data Science field:

Question 1: What do you understand by the term Data Science?

Answer: Data Science is an interdisciplinary technology field that consists of various tools, processes, algorithms, and machine learning models. The tasks of data scientists are focused on analyzing, processing, cleaning, and mining structured & unstructured data with a goal to discover the trends & patterns hidden in the raw data.

In the series of 5 Most Common Data Science Interview Questions, here is the second question which you might get asked during an interview.

Question 2: In your preferred programing language, write a program to print the numbers ranging from 1 to 50

Answer: The python code to print numbers ranging from 1 to 50 is as follows-

For i in range(1,51):

print(i)

Question 3: What is sampling and what are the most common sampling techniques?

Answer: It becomes difficult to analyze if you have a large amount of data with big datasets. In situations like these, we take some sample data out of the raw data and use it to analyze the patterns & trends. This process is called sampling.

There are 2 most techniques that are used when you perform sampling, these are:

  1. Probability Sampling Technique
  2. Non-probability Sampling Technique

You’re reading the article: 5 Most Common Data Science Interview Questions & Their Answers

Question 4: What is the difference between data analytics & data science?

Answer: As data analysts, professionals collect the data, analyze it, and identify the patterns that help them in making strategic decisions for business growth. The discipline of Data Analytics is focused on working with structured data and make statistical analyses to solve complex business problems.

The tasks of a Data Scientist are more focused on interpreting the data, developing new tools, writing new algorithms using machine learning to extract the data that an organization needs in order to solve tangible business problems and make predictions about the future.

If you want to know more about the difference between these two, you can prefer this article: 5 Simple Differences Between Data Analyst and Data Scientist

Question 5: What is R language and how it is used in Data Science?

Answer: R is an integrated suite of software facilities for data manipulation, calculation, and graphical display. R is used in data visualization as it has many inbuilt functions and libraries that help in data visualizations. These libraries include ggplot2, leaflet, lattice, etc.

We hope you liked this article, 5 Most Common Data Science Interview Questions & Their Answers. If you’re eager to know more about job roles, salaries, and vacancies in the Data Domain, you can refer to this article: Top 5 Companies in India Where You Can Kickstart Your Career As A Data Scientist

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