For those interested in data science, please join us for a 1 hour Wolfram Live Coding Session Tuesday Mar 19 2019 at 4pm ET on Zoom (https://wolfram.zoom.us/j/4659236576) . Please see the Linkedin post:

https://www.linkedin.com/pulse/functional-dataflow-wolfram-live-coding-session-mar-19-calvitti-phd

Abstract

This live coding session will focus on functional methods and patterns to write compact but flexible pipes for data science using real-world examples. The Wolfram Language is ideal to data science due to its functional orientation, large-scale symbolic processing and pattern matching abilities. Many of these features have no direct counterpart in object-oriented languages like Python. Consequently WL workflow is more programmer efficient: line-count ratios of 5:1 and often much more, are common relative to equivalent Python (inclusive of its extensive libraries). Functional programming lets you do more with less code and is more convenient and safer to refactor. After a brief tour of the forthcoming book Functional Dataflow, the focus will be on data transformation, aka "data wrangling" - recognized to be a highly time-consuming aspect of workflow - rather than on math or statistical methods. Exposition will be adapted from Excursion chapters in FD. Key skills described include composing operator pipes, combining tabular, hierarchical, temporal and graphical datasets, exploring queries on data slices then scaling up to the whole dataset, creating insightful visualizations, and writing flexible utility (eg one-liner operator) libraries for reuse in future projects.

Overview Mathematica notebook containing links to data and dependencies: enter link description here