Blog posts from 2020 that deserve a second look

January 11, 2021

This post was kindly contributed by The DO Loop - go there to comment and to read the full post.

On The DO Loop blog, I write about a diverse set of topics, including statistical data analysis, machine learning, statistical programming, data visualization, simulation, numerical analysis, and matrix computations.
In a previous article, I presented some of my most popular blog posts from 2020.
The most popular articles often deal with elementary or familiar topics that are useful to almost every data analyst.

However, among last year’s 100+ articles are many that discuss advanced topics.
Did you make a New Year’s resolution to learn something new this year? Here is your chance! The following articles were fun to write and deserve a second look.

Machine learning concepts

Relationship between a threshold value and true/false negatives and positives

Statistical smoothers

Bilinear interpolation of 12 data values

I write a lot about scatter plot smoothers, which are typically parametric or nonparametric regression models. But a SAS customer wanted to know how to get SAS to perform various classical interpolation schemes such as linear and cubic interpolations:

SAS Viya and parallel computing

SAS is devoting tremendous resources to SAS Viya, which offers a modern analytic platform that runs in the cloud. One of the advantages of SAS Viya is the opportunity to take advantage of distributed computational resources. In 2020, I wrote a series of articles that demonstrate how to use the iml action in Viya 3.5 to implement custom parallel algorithms that use multiple nodes and threads on a cluster of machines. Whereas many actions in SAS Viya perform one and only one task, the iml action supports a general framework for custom, user-written, parallel computations:

The map-reduce functionality in the iml action

  • The map-reduce paradigm is a two-step process for distributing a computation. Every thread runs a function and produces a result for the data that it sees. The results are aggregated and returned. The iml action supports the MAPREDUCE function, which implements the map-reduce paradigm.
  • The parallel-tasks paradigm is a way to run independent computations concurrently.
    The iml action supports the PARTASKS function, which implements the map-reduce paradigm.

Simulation and visualization

Decomposition of a convex polygon into triangles

Generate random points in a polygon

Your turn

Did I omit one of your favorite blog posts from The DO Loop in 2020?
If so, leave a comment and tell me what topic you found interesting or useful.
And if you missed some of these articles when they were first published, consider subscribing to The DO Loop in 2021.

The post Blog posts from 2020 that deserve a second look appeared first on The DO Loop.

This post was kindly contributed by The DO Loop - go there to comment and to read the full post.

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