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#
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
- 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.
Geert Hofstede | Defined Corporate Culture
I've been interested in Hofstede's work since B-school, back in the early second millennia, and at one time considered publishing using his country characteristics as predictors for economic and social welfare outcomes. Nowadays, I use the results of his analyses frequently in small R programming demonstrations.
He's an interesting researcher, who's done important work, as The Economist article describes
The man who put corporate culture on the map—almost literally—Geert Hofstede (born 1928) defined culture along five different dimensions. Each of these he measured for a large number of countries, and then made cross-country comparisons. In the age of globalisation, these have been used extensively by managers trying to understand the differences between workforces in different environments.
The Economist article
give a fuller picture of Geert Hofstede
, and anyone interested in reading one of his works might enjoy Cultures and Organizations: Software of the Mind, Third Edition
. An interesting dive into Geert's research and its implication, with a fairly high reader score.
As for sampling of analyses I've posted using Hofstede's cultural dimensions:
Neural Network Series in R
While developing these demonstrations in logistic regression and neural networks, I used and discovered some interesting methods and techniques:
A few useful commands and packages...:
- update.packages() for updating installed packages in one easy action
- as.formula() for creating a formula that I can reuse and update in one action across all my code sections
- View() for looking at data frames
- fourfoldplot() for plotting confusion matrices
- neuralnet for developing neural networks
- caret, used with nnet, to create predictive model
- plotnet() in NeuralNetTools, for creating attractive neural network models
Resources that I used or that I would like to explore...
- MS Azure Notebooks, for working online with Python, R, and F#, all part of MS's data workflows
- Efficient R Programming, that seems to have many good tips on working with R
- Data Mining Algorithms in SSAS, Excel, and R, showing various algorithms in each technology
- R Documentation, a high quality, useable resource
To explore this series...