Data Science Interview Questions and Answers

Data Science Interview Questions and Answers

Data science (Data-driven science) is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, classification, cluster analysis, uncertainty quantification, computational science, data mining, databases, and visualization.

In particular, data science also covers

  • Data integration
  • Distributed architecture
  • Automating machine learning
  • Data visualization
  • Dashboards and BI
  • Data engineering
  • Deployment in production mode
  • Automated, data-driven decisions

What is Data Science?

What are the major Skills Data Scientist need?

What is Data Scientist?

Can you explain Data Munging or Data wrangling?

Why is Data Munging useful?

Can you explain Data Mining?

Can you explain Data Preparation?

Can you define Data Discretization?

Can you define Data Reduction?

Can you define Analytics?

What is the difference between an analyst and a data scientist?

Can you define Feature vector?

How do Data Scientists use Statistics?

Can you explain Recommender System?

Can you explain Collaborative filtering?

What is difference between SAS, R and Python programming?

What are the two main components of the Hadoop Framework?

Which one would you prefer for text analytics Python or R?

Can you explain Data Cleansing?

Can you define Cluster Sampling?

Can you explain Interpolation and Extrapolation?

Can you define Linear Regression?

Can you explain Supervised Learning?

Can you explain unsupervised learning?

Can you explain Eigenvalue and Eigenvector?

Can you define A/B Testing?

Can you explain Systematic Sampling?

Can you define power Analysis?

How can you assess a good logistic model?

Explain while working on a data set, how do you select important variables?

What is the advantage of performing dimensionality reduction before fitting an SVM?

What is the differences between univariate, bivariate and multivariate analysis?

When is Ridge regression favorable over Lasso regression?

What are various steps involved in an analytics project?

How do you understand by Bias Variance trade off?

How would you evaluate a logistic regression model? I know that a linear regression model is generally evaluated using Adjusted R² or F value.

How do you treat missing values during analysis?

Can you explain root cause analysis?

Can you use machine learning for time series analysis?

Can you write the formula to calculate R-square?

Can you explain difference between Data modeling and Database design?

What is the difference between Bayesian Estimate and Maximum Likelihood Estimation (MLE)?

Can you explain K-Mean?

In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors. Why not manhattan distance?

Can you define Convex Hull?

Can you explain the difference between a Test Set and a Validation Set?

What do you understand by Type I vs Type II error?

Why is resampling done?

What cross validation technique would you use on time series data set? Is it k-fold or LOOCV?

How is True Positive Rate and Recall related? Write the equation.

Can you explain cross-validation?

What are the various aspects of a Machine Learning process?

What are the Applications of Data Science?

What is the difference between Data science, Machine Learning and Artificial intelligence?

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