Developing Data Products Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

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Developing Data Products Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]
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Hello Peers, Today we are going to share all week’s assessment and quiz answers of the Developing Data Products course launched by Coursera totally free of cost✅✅✅. This is a certification course for every interested student.

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About The Coursera

Coursera, India’s biggest learning platform launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach in a very well manner and in a more understandable way.

Here, you will find Developing Data Products Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Developing Data Products from Coursera Free Certification Course.

Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.

About Developing Data Products Course

This course introduces the fundamentals of developing data products with Shiny, R packages, and interactive graphics.

Course Apply Link – Developing Data Products

Developing Data Products Quiz Answers

Week 1 Quiz Answers

Quiz 1: Quiz 1

Q1. Which of the following are absolutely necessary for creating a functioning shiny app? (Check all that apply)

  • A server.R file containing a call to shinyServer()
  • A ui.R file containing a call to shinyUI()
  • A shiny.R file containing calls to shinyServer() and shinyUI()
  • A ui.R file that contains information about the CSS and styling of the App
  • A server.R file that sets configuration options for hosting the App

Q2. What is incorrect about the following syntax in ui.R?

library(shiny)shinyUI(pageWithSidebar(headerPanel("Data science FTW!"),sidebarPanel(h2('Big text')h3('Sidebar')),mainPanel(h3('Main Panel text'))
  • The h2 command does not take text arguments
  • Missing comma after the h3 command
  • Missing a comma in the sidebar panel
  • The h3 command should be an h2 command

Q3. Consider the following in ui.R

library(UsingR)
data(galton)

shinyServer(  
    function(input, output) {    
       output$myHist <- renderPlot({      
          hist(galton$child, xlab='child height', col='lightblue',main='Histogram')      
          mu <- input$mu      
          lines(c(mu, mu), c(0, 200),col="red",lwd=5)      
          mse <- mean((galton$child - mu)^2)      
          text(63, 150, paste("mu = ", mu))      
          text(63, 140, paste("MSE = ", round(mse, 2)))      
          })      }
)

Why isn’t it doing what we want? (Check all that apply.)

The phrase “Guess at the mu value” should say “mean” instead of “mu”

It should be

mu <- input$mean

in server.R

  • The server.R output name isn’t the same as the plotOutput command used in ui.R.
  • The limits of the slider are set incorrectly and giving an error.

Q4. What are the main differences between creating a Shiny Gadget and creating a regular Shiny App? (Check all that apply)

  • Shiny Gadgets are designed to be used by R users in the middle of a data analysis.
  • Shiny Gadgets are specially designed for use on mobile phones and tablet computers.
  • Shiny Gadgets are designed to have small user interfaces that fit on one page.
  • Shiny Gadgets can be run on a user’s personal computer, unlike a regular Shiny App which needs to be hosted online.
  • Shiny Gadgets are smaller programs and therefore run faster than Shiny Apps.

Q5. Consider the following R script:

library(shiny)
library(miniUI)

pickXY <- function() {
  ui <- miniPage(
    gadgetTitleBar("Select Points by Dragging your Mouse"),
    miniContentPanel(
      plotOutput("plot", height = "100%", brush = "brush")
    )
  )

  server <- function(input, output, session) {
      output$plot <- renderPlot({
        plot(data_frame$X, data_frame$Y, main = "Plot of Y versus X",
           xlab = "X", ylab = "Y")
      })
      observeEvent(input$done, {
        stopApp(brushedPoints(data_frame, input$brush,
                          xvar = "X", yvar = "Y"))
      })
  }

  runGadget(ui, server)
}

my_data <- data.frame(X = rnorm(100), Y = rnorm(100))

pickXY(my_data)

Why isn’t it doing what we want?

  • The input data is defined in such a way that it is not compatible with pickXY()
  • The wrong column names are passed to brushedPoints()
  • No arguments are defined for pickXY()
  • The call to plot() references the column names of the data frame in the wrong order.

Week 2 Quiz Answers

Quiz 1: Quiz 2

Q1. What is rmarkdown? (Check all that apply.)

  • A format that can be interpreted into markdown (which is a simplified markup language).
  • A form of LaTeX typesetting.
  • A simplified XML format that can be interpreted into R.
  • A simplified format that, when interpreted, incorporates your R analysis into your document.

Q2. In rmarkdown presentations, in the options for code chunks, what command prevents the code from being repeated before results in the final interpreted document?

  • echo = FALSE
  • comment = FALSE
  • cache = FALSE
  • eval = FALSE

Q3. In rmarkdown presentations, in the options for code chunks, what prevents the code from being interpreted?

  • eval = FALSE
  • run = FALSE
  • cache = FALSE
  • eval = NULL

Q4. What is leaflet? (Check all that apply.)

  • An R package for creating 3D rendered isomaps
  • A javascript library for creating interactive maps
  • A tool for reproducible documents
  • An R package interface to the javascript library of the same name

Q5. The R command

df %>% leaflet() %>% addTiles()

is equivalent to what? (Check all that apply)

  • leaflet(df) %>% addTiles()
  • leaflet(addTiles(df))
  • addTiles(leaflet(df()))
  • addTiles(leaflet(df))
  • df(leaflet(addTiles()))

Q6. If I want to add popup icons to my leaflet map in R, I should use.

  • addTiles
  • leaflet
  • dplyr
  • addMarkers

Week 3 Quiz Answers

Quiz 1: Quiz 3

Q1. Which of the following items is required for an R package to pass R CMD check without any warnings or errors?

  • example data sets
  • vignette
  • unit tests
  • a demo directory
  • An explicit software license

Q2. Which of the following is a generic function in a fresh installation of R, with only the default packages loaded? (Select all that apply)

  • lm
  • colSums
  • show
  • predict
  • mean
  • dgamma

Q3. What function is used to obtain the function body for an S4 method function?

  • getS3method()
  • getMethod()
  • getClass()
  • showMethods()

Q4. Please download the R package DDPQuiz3 from the course web site. Examine the \verb|createmean|createmean function implemented in the R/ sub-directory. What is the appropriate text to place above the \verb|createmean|createmean function for Roxygen2 to create a complete help file?

#' This function calculates the mean
#'
#' @return the mean of x
#' @export
#' @examples
#' x <- 1:10
#' createmean(x)
#' This function calculates the mean
#'
#' @param x is a numeric vector
#' @return the mean of x
#' @export
#' @examples
#' x <- 1:10
#' createmean(y)
#' This function calculates the mean
#' 
#' @param x is a numeric vector
#' @return the mean of x
#' @export
#' @examples 
#' x <- 1:10
#' createmean(x)
This function calculates the mean
@param x is a numeric vector
@return the mean of x
@export
@examples
x <- 1:10
createmean(x)

More About This Course

A data product is the outcome generated by a statistical analysis. Data products automate complex analysis chores or employ technology to increase the utility of a model, algorithm, or conclusion that is informed by data. This course introduces the fundamentals of developing data products with Shiny, R packages, and interactive graphics. The course will concentrate on the statistical principles of building a data product that can be used to convey a wide audience a story about data.

This course is included in numerous curricula.
This course is applicable to a number of Specialization and Professional Certificate programmes. This course will contribute to your education in any of the following programmes:

  • Statistics and Machine Learning Specialization in Data Science
  • Data Science Specialization

WHAT YOU WILL Discover

  • GoogleVis is used to develop fundamental apps and interactive graphics.
  • Create interactive annotated maps with Leaflet.
  • Construct a R Markdown presentation with a data visualisation.

Create a data product that tells a mass audience a story.

SKILLS YOU WILL GAIN

  • Interactivity
  • Plotly
  • Web Application
  • R Programming

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Developing Data Products Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.

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