Summary of R Experience

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Summary of R Experience

49604

Total Lines of code

2030

Total Functions Used

196

Total Packages Used

Table of functions used and # of uses

The (nearly) complete searchable table of all functions used to date in R.

Table of packages used and # of uses

A (nearly) complete searchable table of all packages used to date in R. For ease, uses are counted by the number of occurences of explicit inline calls ie 'package::', implicit calls where a library call was used previously will show lower totals.

Coursework

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Coursework

Description: Introduces the fundamental techniques of quantitative data analysis, ranging from foundational skills; such as data description and visualization, probability, and statistics - to the workhorse of data analysis and regression, to more advanced topics; such as machine learning and networks. Emphasizes real-world data and applications using the R statistical computing language. Analyzing and understanding complex data has become an essential component of numerous fields: business and economics, health and medicine, marketing, public policy, computer science, engineering, and many more. Offers students an opportunity to finish the course ready to apply a wide variety of analytic methods to data problems, present their results to nonexperts, and progress to more advanced course work delving into the many topics introduced here.

For a more comprehensive look at topics covered, see the syllabus

Description: Studies how to build large-scale information repositories of different types of information objects so that they can be selected, retrieved, and transformed for analytics and discovery, including statistical analysis. Analyzes how traditional approaches to data storage can be applied alongside modern approaches that use nonrelational data structures. Through case studies, readings on background theory, and hands-on experimentation, offers students an opportunity to learn how to select, plan, and implement storage, search, and retrieval components of large-scale structured and unstructured information repositories. Emphasizes how to assess and recommend efficient and effective large-scale information storage and retrieval components that provide data scientists with properly structured, accurate, and reliable access to information needed for investigation.

For a more comprehensive look at topics covered, see the syllabus

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