Data Science Interview Questions
Questions Answers Views Company eMail

Define the term cross-validation

170

what the aim of conducting a/b testing?

183

What is the k-means clustering method?

204

Name various types of deep learning frameworks

222

State the difference between the expected value and mean value?

191

Explain auto-encoder

184

How do you overcome challenges to your findings?

205

Discuss normal distribution

220

Explain cluster sampling technique in data science

246

What is the importance of having a selection bias?

179

Explain the steps for a data analytics project

222

Treating a categorical variable as a continuous variable would result in a better predictive model?

186

Name three types of biases that can occur during sampling?

194

What is a recall?

180

Explain the method to collect and analyze data to use social media to predict the weather condition?

377


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Un-Answered Questions { Data Science }

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

209


What is the difference between a bagged model and a boosted model?

213


Explain the difference between Supervised and Unsupervised Learning through examples.

234


Can you use a generator to create iterators?

231


What is t test?

192


Differentiate between type I and type ii error?

183


How do you build a custom function in python or r?

201


What is the central limit theorem?

211


What is correlation in R?

244


Which method in pandas.tools.plotting is used to create scatter plot matrix?

242


You have two sorted array of integers, write a program to find a number from each array such that the sum of the two numbers is closest to an integer i.

204


Explain about string parsing in R language

188


Explain data wrangling?

195


How often should an algorithm be updated?

164


State the difference between a validation set and a test set?

194