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INTRODUCTION

Data science is basically the science of applying advanced analytics techniques and scientific principles to infer valuable data for business decision-making, strategic planning, company comparisons and other uses. Data science continues to evolve jointly as the foremost promising and in-demand career paths for experienced professionals. Today, successful and knowledge driven data professionals perceive that they must advance past the traditional skills of analysing large amounts of data. In order to uncover useful data for their organizations, they must master the full spectrum of the data science life cycle and reach a level of flexibility and understanding to maximize returns at each phase of the process.

WHY BECOME A DATA SCIENTIST?

Glassdoor ranked data scientist as the top three jobs in America since 2016. The growing demand for data science professionals across industries, is being challenged by a reduced number of qualified candidates who are available to fill the vacant positions. The need for data scientists shows no sign of slowing down in the coming future. LinkedIn listed data scientist as one of the most efficient and promising jobs in 2021, along with multiple data-science-related skills as the most in-demand by companies.

THESE ARE THE TOP 5 DATA SCIENCE INTERVIEW QUESTIONS.

Q1) Differentiate between data analytics and data science.

ANS 1) Data Science is a field of science that deals with extracting sufficient information and insights by applying numerous scientific methods from structured and unstructured information. This field is related to large data and one of the most demanded skills currently.

Basically, data analytics helps to convert a big number of figures in the form of information and into Plain English i.e., conclusions which are further helpful in making the decisions for the company.

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Q2) Mention some techniques used for sampling. What is the main advantage of sampling?

ANS 2) There are a large number of sampling methods which are grouped into two categories as:

·        Probability Sampling

·        Non- Probability Sampling

Probability Sampling

This Sampling technique uses randomization to make sure that each and every element of the population gets an equal chance to be a part of the selected sample. It is also known as random sampling.

Non-Probability Sampling

It does not rely on randomization. This technique is more dependent on the researcher’s ability to choose elements for a sample. Outcome of sampling might be biased and makes it difficult for all the elements of population to be part of the sample equally. This type of sampling is also referred to as non-random sampling.

Q3) What is the difference between supervised and unsupervised learning?

ANS3) Supervised learning is a machine learning approach that’s defined by it’s use of labelled information. These datasets are designed to train algorithms into classifying data or predicting outcomes accurately. Unsupervised learning uses machine learning algorithms to analyse and cluster unlabelled information. These algorithms discover hidden patterns in data without the need for human intervention.

Q4) What are the steps used in making of a decision tree algorithm?

ANS 4)

·        Take the entire data set as input.

·        Calculate entropy of the target variable, as well as of the predictor attributes.

·        After that, Calculate your information gain of all attributes.

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·        Then, choose the attribute with the highest data gain as the root node.

·        Repeat the exact same procedure on each and every branch until the decision node of each branch is finalized.

Q5) Tell the key differences between linear and logistic regression?

ANS5) In the case of Linear Regression, the outcome is continuous while in the case of Logistic Regression the outcome is discrete and to perform Linear regression we require a linear relationship between the dependent and independent variables. But to perform Logistic regression we do not require a linear relationship between the dependent and independent variables.

Linear Regression is all about fitting a straight line in the data while Logistic Regression is about fitting a curve to the data and Linear Regression is a regression algorithm for Machine Learning while Logistic Regression is a classification Algorithm for machine learning.

                                 AUTHOR BIO

Abhyank Srinet is a passionate digital entrepreneur who holds a Masters in Management degree from ESCP Europe. He started his first company while he was still studying at ESCP,and managed to scale it up by 400% in just 2 years. Being a B-School Alumni, he recognized the need for a one-stop solution for B-School to get in touch with schools and get their application queries resolved. This prompted him to create MiM-Essay, a one-of-a-kind portal with cutting-edge profile evaluation and school selection algorithms, along with several avenues to stay informed about

TOP 5 DATA SCIENCE INTERVIEW QUESTIONS IN 2022

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