Analytics in the restaurant industry

Here are three links that are worth your time.

Lastly, here’s a map of the most well-funded AI star­tups in each of the the US states.

Let your customers talk

Here are three links from the data sci­ence world that are worth your time.

  1. Actionable data sci­ence in sales. Let your cus­tom­ers talk for 4+ minutes. Don’t talk about your com­pany for more than 2 minutes. And more data-driven ad­vice.
  2. How to get in­to nat­ur­al lan­guage pro­cessing. YCombinator has star­ted a series of blog posts titled Paths on get­ting star­ted with emer­ging fields. The first is on NLP.
  3. The cur­rent state of auto­mated ma­chine learn­ing. An over­view of lib­rar­ies that auto­mat­ic­ally ap­ply ma­chine learn­ing tech­niques to data­sets.

But re­mem­ber: amid­st all this big data, we have a big­ger small-data prob­lem.

Which charting library to use?

Here are three links you should go through this week.

  1. What I learned re­cre­at­ing one chart us­ing 24 tools is an ex­cel­lent com­par­is­on by Lisa of 12 visu­al­isa­tion ap­plic­a­tions and 12 lib­rar­ies, with a good sum­mary of which tool to use when.
  2. Can we pre­dict flu deaths with ML and R? Read this R note­book for a step-by-step walk-through of pre­dict­ing wheth­er a pa­tient will sur­vive or not. (There’s also a part 2 that im­proves on this mod­el.)
  3. One of our col­leagues nearly lost a piece of ana­lys­is re­cently. Here’s the most bor­ing / valu­able ad­vice she can get on how to or­gan­ise ana­lys­is — or any form of work for that mat­ter. Of course, you could al­ways learn git.

    If that doesn’t fix it, git.txt con­tains the phone num­ber of a friend of mine who un­der­stands git. Just wait through a few minutes of ‘It’s really pretty sim­ple, just think of branches as…’ and even­tu­ally you’ll learn the com­mands that will fix everything.