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Data science interview questions and answers for freshers

Data science interview questions and answers for freshers in 2021 | Data science jobs in 2021

If you’re someone who is just starting out with data science and interested in pursuing a career in data science, then you would like to know the most frequently asked data science interview questions and answers for freshers in 2021. 

The world is going through a data-fuelled digital disruption and it should come as no surprise that in this new era of big data, data science professionals are in high demand. Top companies across the world including Amazon, Google, Apple, Intel, Microsoft, and Facebook are leveraging massive amounts of data to improve their services and operations, and they are actively seeking data science graduates and freshers to join their team. According to a survey by analyticsinsight conducted by Analytics Insight, by 2021, there will be 3,037,809 new job openings in data science, worldwide.

So, if you’re moving down the path to becoming a data scientist, you’ve got a ton of opportunities across the world. However, you must be prepared to impress your prospective employers with your knowledge and you’ll need to show that you’re technically proficient with Big data concepts, frameworks, and applications. Following are the frequently asked data science interview questions and answers for freshers in 2021. 

Frequently asked data science interview questions for freshers:

What is the difference between supervised and unsupervised learning?

Both supervised learning and unsupervised learning are a type of machine learning models. In a supervised learning model, the algorithm learns from a labelled training data. It helps you to predict outcomes for unforeseen data. Whereas the unsupervised learning model mainly deals with unlabelled data. Here inferences are drawn from datasets containing input data without labelled responses.

What is logistic regression?

Logistic Regression often referred to as the logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular cricket team will win the tournament or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the fitness of the players, the amount of time spent practicing in the nets, etc 

Note: This is one of the most common data science questions and you must be able to give unique examples to explain this concept.

Explain decision trees

Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves. The leaves are the decisions or the final outcomes. And the decision nodes are where the data is split.

What is a random forest?

Random forests are an ensemble learning technique that builds off of decision trees. A random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction 

What is better, random forest or decision tree?

The random forest will reduce the variance part of error rather than the bias part, so on a given training data set decision tree may be more accurate than a random forest. But on an unexpected validation data set, Random forest always wins in terms of accuracy.

How do you build a random forest model?

A random forest is built up of a number of decision trees. For building a random forest model,

  1. Randomly select ‘k’ features from a total of ‘m’ features where k << m
  2. Among the ‘k’ features, calculate the node D using the best split point
  3. Split the node into daughter nodes using the best split
  4. Repeat steps two and three until leaf nodes are finalized 
  5. Build forest by repeating steps one to four for ‘n’ times to create ‘n’ number of trees

Name a few Deep Learning Frameworks

  • TensorFlow
  • Caffe
  • Pytorch
  • Microsoft Cognitive Toolkit
  • Chainer
  • Keras

 Note:  For this data science interview question, be prepared to answer the follow-up questions related to any specific frameworks. 

What are the drawbacks of the linear model?

  • The assumption of linearity of the errors
  • It can’t be used for count outcomes or binary outcomes
  • There are overfitting problems that it can’t solve

Name some commonly used algorithms in data science.

  • Linear regression
  • Logistic regression
  • Random Forest
  • KNN

How regularly must an algorithm be updated?

You will want to update an algorithm when:

  • You want the model to evolve as data streams through infrastructure
  • The underlying data source is changing
  • There is a case of non-stationarity

Name some Machine Learning Libraries and their benefits

  • Numpy: It is used for scientific computation.
  • Statsmodels: It is used for time-series analysis.
  • Pandas: It is used for tubular data analysis.
  • Scikit learns: It is used for data modeling and pre-processing.
  • Tensorflow: It is used for the deep learning process.
  • Regular Expressions: It is used for text processing.
  • Pytorch: It is used for the deep learning process.
  • NLTK: It is used for text processing.

Note: While answering this data science interview question, adding your experience in working with these libraries would be great.

According to a recent report by Dice , 2020 saw an average of 50% increase in demand for data scientists across healthcare, telecommunications, media/entertainment, banking, financial services, and insurance sectors, and this number is predicted to increase more in 2021. So, learning these most frequently asked data science questions and answers for freshers in 2021 will help you clear a data science interview. 

As a premier business school in India, Praxis offers a 9-month full-time post-graduate program in Data Science. With our vast experience in business education, we offer students both the time to understand the complex theory and practice of data science concepts and the guidance from knowledgeable faculty who are available on campus for mentoring. We also have a well-structured campus placement program that ensures interview opportunities with the most significant companies in the field.

 

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