Data Analytics Workouts

More as an experiment, rather an attempt at sharing code and ideas, I created a Pinterest board devoted to my personal data analytics work, done with Python, R, or F#, as well as reviews of books, and was quite surprised with the result. The graphics could do with optimization, but otherwise...

I was exploring Jupyter notebooks , that combines live code, markdown and data, through Microsoft's implementation, known as MS Azure Notebooks , putting together a small library of R and F# notebooks . As Microsoft's FAQ for the service describes it as : ...a multi-lingual REPL on steroids. This is a free service that provides Jupyter notebooks along with supporting packages for R, Python and F# as a service. This means you can just login and get going since no installation/setup is necessary. Typical usage includes schools/instruction, giving webinars, learning languages, sharing ideas, etc. Feel free to clone and comment... In R Azure Workbook for R - Memoisation and Vectorization Charting Correlation Matrices in R In F# Charnownes Constant in FSharp.ipynb Project Euler - Problems 18 and 67 - FSharp using Dynamic Programming

I worked through functools and itertools sections of the Computational Statistics in Python tutorial , and I found these promisingly powerfuul for data modeling and functional programming: # The functools module """The most useful function in the functools module is partial, which allows you to create a new function from an old one with some arguments “filled-in”.""" from functools import partial def power_function(power, num): """power of num.""" return num**power square = partial(power_function, 2) cube = partial(power_function, 3) quad = partial(power_function, 4) # The itertools module """This provides many essential functions for working with iterators. The permuations and combinations generators may be particularly useful for simulations, and the groupby gnerator is useful for data analyiss.""" from itertools import cyc...

I am currently working through Computational Statistics in Python 's module Functions are first class objects , and for this - I have worked with delegates and higher order functions in other languages - I put together this example to better grasp higher order functions in Python: def power_function(num, power): """power of num.""" return num**power def add_function(num, more): """sum of num.""" return num+more def runFunction(fx, x, y): return fx(x,y) num = 2 powers = range(0,64) arrPower = list(map(lambda x: runFunction(power_function, num, x), powers)) arrAdd = list(map(lambda x: runFunction(add_function, num, x), powers)) arrPower arrAdd