- James Igoe MS Azure Notebooks that utilizes MS's implementation of Jupyter Notesbooks.
- Mathematical Library, a basic mathematical NuGet package, with the source hosted on GitHub.
- Basic Statistical Functions: Very basic F# class for performing standard deviation and variance calculations.
- Various Number Functions: A collection of basic mathematical functions written in F# as part of my learning via Project Euler, functions for creating arrays or calculating values in various data structures.

- Functions (non-state, no side effects)
- Tail recursion
- Non-mutable variables
- Currying
- Lambda notation
- Pattern matching
- Sequences, arrays, lists, and tuples
- Array slicing

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.

- Understanding and Applying Logistic Regression (using Excel, Python, or R)
- Data Mining Algorithms in SSAS, Excel, and R

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:

- 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

- 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

- Neural Networks (Part 1 of 4) - Logistic Regression and neuralnet on State 'Personality' and Political Outcomes
- Neural Networks (Part 2 of 4) - caret and nnet on State 'Personality' and Political Outcomes
- Neural Networks in R (Part 3 of 4) - Neural Networks on Price Changes in Financial Data
- Neural Networks (Part 4 of 4) - R Packages and Resources
- Attractive Confusion Matrices in R Plotted with fourfoldplot