Get yourself trained on The Comprehensive Statistics with this Online Training The Comprehensive Statistics and Data Science with R Course.
Online Training The Comprehensive Statistics and Data Science with R Course
This course, The Comprehensive Statistics and Data Science with RCourse,is mostlybased on the authoritative documentation in the online “An Introduction to R” manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed10-session course which covers much of the content in thecontemporary 105-page CRAN manual. Theten sessions follow the outline in the An Introduction to R online manual and specifically instruct with respect to the following user topics:1. Introduction to R; Inputting data into R2. Simple manipulation of numbers and vectors3. Objects, their modes and attributes4. Arrays and matrices5. Lists and data frames6. Writing user-defined functions7. Working with R as a statistical environment8. Statistical models and formulae; ANOVAand regression9. GLMs and GAMs10. Creating statistical and other visualizations with RIt is a comprehensive and decidedly “hands-on” course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are includedwith the downloadablecourse materials.The course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through “hands-on” practice to master the input and output of data, as well as all of themajor and important objects and data structures that are used within the R environment. The second half of the course is a detailed “hands-on” transcript for using R for statistical analysis including detaileddata-driven examples ofANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of “hands-on” instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R.The course is veryuseful for any quantitative analysis professional who wishes to “come up to speed” on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.