rshorey

Lights, Camera, Algorithm

Act out and discuss machine learning algorithms. This activity is from a SRCCON 2018 session led by Jeremy Merrill and Rachel Shorey.

Materials

• Index cards
• pens/pencils
• Dice with varying numbers of faces (several D10 and one D6 for sure)
• Masking tape to mark floor
• Paper, easel, marker
• Stickers in several colors

vitalyfriedman

1. Highlight a recommended option,

2. Allow users to switch currency (€/$/£)

3. Allow users to switch pricing monthly/yearly

4. Keep the entire pricing plan area clickable

5. Use slider to calculate how much a user would save

6. Provide free first month for good engagement

7. Prominently highlight testimonials prominently

8. Repeating call to action on top and bottom

9. Sell benefits instead of features

10. Indicate that users can cancel any time


machikoyasuda

----- Tech -----

拡張現実【かくちょうげんじつ】 augmented reality, AR

投資家【とうしか】 investor

静的【せいてき】 static

hadley

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

briantjacobs

Storytelling from Space: Tools/Resources

This list of resources is all about acquring and processing aerial imagery. It's generally broken up in three ways: how to go about this in Photoshop/GIMP, using command-line tools, or in GIS software, depending what's most comfortable to you. Often these tools can be used in conjunction with each other.

Acquiring Landsat & MODIS

Web Interface

• USGS Earth Explorer - Browser and data access (create a login)
  http://earthexplorer.usgs.gov/

• Landsat archive

mynameisfiber

[
    {
        "name": "Lower East Side",
        "center": [
            -73.9836180698,
            40.7155233255
        ]
    },
    {
        "name": "Marble Hill",

dannguyen

#!/usr/bin/env ruby
# nyt-list-scrape.rb
# Dan Nguyen @dancow
# Created: 2014-07-26
# Updated: 2014-07-29
#
# Scrape the titles/urls of articles recommended to the user by the NYT,
# and also, the most popular ones at the moment
#
# Requirements: Ruby 1.9.x and the Mechanize gem and a NYT digital subscriber account

briantjacobs

Start querying (also scroll down for more query examples!)

• Freebase: http://www.freebase.com/query
• DBPedia: http://dbpedia.org/snorql
  • Scroll for dbpedia examples: https://wiki.base22.com/display/btg/SPARQL+Query+Examples
• Factforge: http://factforge.net/sparql

Wikipedia stats

• http://en.wikipedia.org/wiki/Wikipedia:Wikipedians

ashaw

# https://www.mapbox.com/blog/processing-landsat-8/
task :landsat_2014 => :environment do
  dirs = Dir["#{Rails.root.to_s}/db/initial/raster/*"].reject {|q| q =~ /tar\.gz/ }
  dirs.each do |dir|
    scene_id = dir.split("/")[-1]
    [4,3,2].each do |band|
      `gdalwarp -t_srs EPSG:3857 #{dir}/#{scene_id}_B#{band}.TIF #{dir}/#{scene_id}_B#{band}-projected.tif`
    end
    `convert -combine #{dir}/#{scene_id}_B{4,3,2}-projected.tif #{dir}/#{scene_id}_RGB-projected.tif && \
     convert -channel B -gamma 0.925 -channel R -gamma 1.03 -channel RGB -sigmoidal-contrast 50x16% #{dir}/#{scene_id}_RGB-projected.tif #{dir}/#{scene_id}_RGB-projected-corrected.tif && \

rossant