Pabs L. pabs-lop

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!

Setting up a MSI laptop with GPU (gtx1060)

Installing Ubuntu 18.04, CUDA, CDNN, Pytorch and TensorFlow

Installing Ubuntu 18.04

Get Image

https://tutorials.ubuntu.com/tutorial/tutorial-create-a-usb-stick-on-ubuntu#0

Install (solving issues)

This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.

The HTML page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.

For a more up to date code example with React & D3, see (VizHub: Stylized Scatter Plot)[https://vizhub.com/curran/3d631093c2334030a6b27fa979bb4a0d?edit=files&file=index.js].

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

	# Editors
	.vscode/
	.idea/

	# Vagrant
	.vagrant/

	# Mac/OSX
	.DS_Store

	#!/bin/bash

	## cf from http://programster.blogspot.com/2014/05/ubuntu-14-desktop-install-fira-sans-and.html

	cd /tmp

	# install unzip just in case the user doesn't already have it.
	if [[ `uname` = Linux ]]; then
	sudo apt-get install unzip -y
	wget "http://www.carrois.com/downloads/fira_4_1/FiraFonts4106.zip"

	#!/usr/bin/python
	# Udacity exercise. Just posted the code here to help anyone who wanted to see the work behind my posted result.
	__author__ = 'Michael Rosata [email protected]'
	__package__ = ''

	from random import random
	import turtle

	class TurtleArtist(turtle.Turtle):
	_origin = (0, 0)

	# Last updated May, 2024 for Apple silicon Macs
	# Install Homebrew if you don't already have it: https://brew.sh

	# install nano from homebrew
	brew install nano nanorc

	# update your nanorc file
	echo 'include "'"$(brew --cellar nano)"'//share/nano/.nanorc"' >> ~/.nanorc

	# close and re-open your terminal and you'll have syntax highlighting