This site is often under construction as it is used to expand my skills by exploring ideas and techniques for .NET, combining ASP.NET, Silverlight applications, web services, and a VB/C# desktop client to access selfsame web services.

Updates and Announcements


Hierarchical and K-Means Clustering Demo in R

On my Data Analytics Workouts blog, I provide an exploration of clustering in Clustering: Hierarchical and K-Means in R on Hofstede Cultural Patterns, as well via MS' Azure Notebook.


F# is Part of Microsoft's Data Science Workloads

I have not worked in F# for over two (2) years, but am enthused that Microsoft has added it to it languages for Data Science Workloads, along with R and Python. To that end, I hope to repost some of my existing F# code, as well as explore Data Science Workloads utilizing all three languages. Prior work in F# is available from learning F#, and some solutions will be republished on this site.

Data Science Workloads

Published Work in F#

Explored in F#

I explored F# some time ago, intrigued by the idiom of functional languages and the strengths of F#. Additionally, some solutions can improved upon by using .NET components to speed the process, e.g., parallel processing, and the language itself is not simply functional, but can also be used for object-oriented and procedural development.
  • Functions (non-state, no side effects)
  • Tail recursion
  • Non-mutable variables
  • Currying
  • Lambda notation
  • Pattern matching
  • Sequences, arrays, lists, and tuples
  • Array slicing


Pluralsight Courses - Opinion

My list is kind of paltry, but I’ve sat through others or started many but decided against finishing. The best courses I’ve finished have been along the lines of project management:
I’ve also sat through this, and useful, although very rudimentary:
I do my own reading for data science, and have my own side projects, but I’ve also taken some data science courses via Pluralsight. The beginner demos are done well, although less informative than the intermediate ones, which are ultimately more useful. For the latter, I typically do simultaneous coding on my own data sets, which helps learn the material.