Coding

How can you simulate 1000 normally distributed data points with mean=x and variance=y in R, summarize the statistics, and create a density plot? Additionally, replicate this task in Python on the Codility editor.

Machine Learning Engineer

Asana

DoorDash

Microsoft

Google

Wayfair

GitHub

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  • How can you simulate 1000 normally distributed data points with mean=x and variance=y in R, summarize the statistics, and create a density plot? Additionally, replicate this task in Python on the Codility editor.
  • Can you demonstrate how to create a normal distribution of 1000 data points with a mean of x and variance of y in R, perform a statistical summary, and plot the density? Follow this by executing the same in Python using the Codility platform.
  • How do you generate a normally distributed dataset of 1000 points with specified mean and variance in R, conduct a summary analysis, and draw a density plot? Also, show how to accomplish this in Python on Codility.
  • What's the procedure for producing 1000 normally distributed samples with mean=x and variance=y in R, conducting a summary statistical analysis, and graphing a density plot? Implement the equivalent in Python via the Codility online editor.
  • How would you create a set of 1000 data points following a normal distribution with given mean and variance in R, analyze the descriptive statistics, and visualize with a density plot? Then, could you perform the same operations in Python on Codility's code editor?
  • In R, how can you generate a dataset of 1000 normally distributed points with mean=x and variance=y, provide summary statistics, and plot the density? Also, replicate these steps in a Python script within Codility's editor.
  • What steps would you take to generate 1000 normal distribution data points with mean=x, variance=y in R, summarize the data, and plot the density? Then, how would you achieve the same outcome using Python in the Codility code editor?
  • Could you produce 1000 data points in R with a normal distribution, defined mean, and variance, analyze the dataset summary, and plot its density? Also, show how to do this in a Python environment on Codility.
  • How to generate a normal distribution of 1000 data points with a set mean and variance in R, complete a statistical summary with explanation, and create a density plot, and then perform these steps in Python using the Codility editor?
  • Your main responsibility is to generate 1000 normally distributed data points using R and Python, with a specified mean and variance value. Calculate summary statistics of the dataset and clarify their meaning. Create a density plot of the data in R and Python, as well.
  • In R generate 1000 data points normally distributed, with mean=x, and variance=y. Perform summary stat on the data points, explain stat metrics. Create density plot. Do the same in python script, in codility.com editor
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Interview question asked to Machine Learning Engineers interviewing at Pendo, Red Hat, Affirm and others: How can you simulate 1000 normally distributed data points with mean=x and variance=y in R, summarize the statistics, and create a density plot? Additionally, replicate this task in Python on the Codility editor..