Data Science Interview Questions, Answers for Freshers and Experienced asked in various Company Job Interviews

Data Science Interview Questions

Questions Answers Views Company eMail

How to choose the right chart in case of creating a viz?

317

Can you provide examples for other computations in spark?

275

Describe univariate, bivariate and multivariate analysis?

298

Can you enumerate the various differences between supervised and unsupervised learning?

325

what is the difference between extrapolation and interpolation?

357

How do you work towards a random forest?

321

If you had to choose between the programming languages r and python, which one would you use for text analytics?

403

What are the various steps involved in an analytics project?

337

What do you understand by linear regression and logistic regression?

387

Why data cleaning plays a vital role in the analysis?

386

What is a/b testing in data science?

347

Please explain recommender systems along with an application?

361

For linear regression, what are some of the assumptions a data scientist is most likely to make?

296

What is supervised learning?

308

Could you draw a comparison between overfitting and underfitting?

407

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Un-Answered Questions { Data Science }

What are various steps involved in an analytics project?

334

Why do you need a for loop?

336

Define the term cross-validation

319

Explain about string parsing in R language

345

You created a predictive model of a quantitative outcome variable using multiple regressions. What are the steps you would follow to validate the model?

347

How do you do data import in sas?

341

What is systematic sampling?

329

Define a sql query? How do you use sql in sas, python, r languages?

398

How can you select k for k-means?

322

How many sorting algorithms are available?

381

What are an eigenvalue and eigenvector?

320

What is the job of analytics?

364

How has your prior experience prepared you for a role in data science?

314

Name some packages in r and python for building regression models.

317

Define standard deviation, mean, mode and median.

448

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