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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Questions

Factual

  • How would you compare the results of the different models and forecasts to select the appropriate constraints?
  • In the initial plot of the river data, is there something that could be used to foresee the seasonality and/or trend immediately without breaking into components?

When, how, and why?

  • While the automated selection provided ARIMA values, how would you select the different parameters?
  • How would you decide on the different modeling techniques used for your dataset?

Challenges

  • Several of the forecasts involved negative values for the river flow. How can that be avoided?
  • Either use a time series that you have available or find one that has the components addressed in the chapter and apply the analysis available in R.
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Tech Concepts
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Programming languages
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R for Data Science
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