Christophe Dervieux cderv

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

Statistical knowledge
Programming/hacking skills
Domain expertise

Statistical knowledge

Gerrit vs Github: for code review and codebase management

Sure, Github wins on the UI. Hands down. But, despite my initial annoyance with Gerrit when I first started using it almost a year ago, I am now a convert. Fully. Let me tell you why.

Note: This is an opinionated (on purpose) piece. I assume your preferences are like mine on certain ideas, such as:

Fast-forward submits to the target branch are better than allowing merge commits to the target branch. The reason I personally prefer this is that, even if a non-conflicting merge to the target branch is possible, the fact that the review/pull request is not up to date with the latest on the target branch means feature branch test suite runs in the CI pipeline reporting on the review/PR may not be accurate. Another minor point is that forced merge commits are annoying as fuck (opinion) and clutter up Git log histories unnecessarily and I prefer clean histories.
Atomic/related changes all in one commit is something worth striving for. Having your dev

People

`:bowtie:`	😄 `:smile:`	😆 `:laughing:`
😊 `:blush:`	😃 `:smiley:`	☺️ `:relaxed:`
😏 `:smirk:`	😍 `:heart_eyes:`	😘 `:kissing_heart:`
😚 `:kissing_closed_eyes:`	😳 `:flushed:`	😌 `:relieved:`
😆 `:satisfied:`	😁 `:grin:`	😉 `:wink:`
😜 `:stuck_out_tongue_winking_eye:`	😝 `:stuck_out_tongue_closed_eyes:`	😀 `:grinning:`
😗 `:kissing:`	😙 `:kissing_smiling_eyes:`	😛 `:stuck_out_tongue:`

	## this snippet was the original gist but the Rmd solution below is even nicer

	## auto-generate a visual index as README.md in current working directory
	fig_files <- list.files(pattern = "jpg$") # change to png$ as needed!
	writeLines(paste0(fig_files, "\n",
	"![`", fig_files, "`](", fig_files, ")\n\n"), "README.md")

	library(httr)
	library(devtools)

	# make httr set CURL to ignore SSL verification problems
	# (needed if the SSL proxy replaces certs with its own)

	set_config(config(ssl.verifypeer = 0L))

	# set proxy details
	set_config(use_proxy("10.10.10.10",8080))


	library(shiny)
	library(dplyr)
	library(lubridate)

	# Load libraries and functions needed to create SQLite databases.
	library(RSQLite)
	library(RSQLite.extfuns)
	saveSQLite <- function(data, name){
	path <- dplyr:::db_location(filename=paste0(name, ".sqlite"))

	# Customize BASH PS1 prompt to show current GIT repository and branch.
	# by Mike Stewart - http://MediaDoneRight.com

	# SETUP CONSTANTS
	# Bunch-o-predefined colors. Makes reading code easier than escape sequences.
	# I don't remember where I found this. o_O

	# Reset
	Color_Off="\[\033[0m\]" # Text Reset

	#!/path/2/Rscript

	# License: CC0 (just be nice and point others to where you got this)
	# Author: Robert M Flight <[email protected]>, github.com/rmflight
	#
	# This is a post-commit hook that after a successful commit subsequently increments the package version in DESCRIPTION
	# and commits that. Analogous to the pre-commit at https://gist.github.com/rmflight/8863882, but useful if you only have
	# good reasons for not doing it on the pre-commit.
	#
	# To install it, simply copy this into the ".git/hooks/post-commit" file of your git repo, change /path/2/Rscript, and make

	' Excel macro to export all VBA source code in this project to text files for proper source control versioning
	' Requires enabling the Excel setting in Options/Trust Center/Trust Center Settings/Macro Settings/Trust access to the VBA project object model
	Public Sub ExportVisualBasicCode()
	Const Module = 1
	Const ClassModule = 2
	Const Form = 3
	Const Document = 100
	Const Padding = 24

	Dim VBComponent As Object

	read.kml <- function(file, layers) {
	require(sp)
	require(rgdal)
	read.layer <- function (layer_name) {
	spobj <- rgdal::readOGR(dsn=file, layer=layer_name)
	coords <- coordinates(spobj)
	colnames(coords) <- c('x', 'y', 'z')[1:ncol(coords)]
	df <- data.frame(coords, spobj@data)
	transform(df, layer=layer_name)
	}