Fundamental Models in Data Science

1. Classification (probability estimation or scoring): binary or categorical.Attempt to predict, for each individual in the population, which of a (small) set of classes this individual belongs to. Classification will bucket individuals, and scoring will provide quantification of likelihood of being in a particular bucket.

2. Regression (“value estimation”): numeric. attempts to estimate or predict, for each individual, the numerical value of some variable for that individual.

3. Similarity Matching: attempts to identify similar individuals based on data known about them.

4. Clustering: attempts to group individuals in a population together by their similarity, but not driven by any specific purpose.

5. Co-occurrence (also known as – frequent item mining, association rule discovery, and market-basket analysis): attempts to find associations between entities based on transactions involving them.

6. Profiling (also known as behavior description): attempts to characterize the typical behavior of an individual, group, or population.

7. Link prediction: attempts to predict connections between data items, usually suggesting that a link should exist, and possibly also estimating the strength of the link.

8. Data reduction: attempts to take a large set of data and replace it with a smaller set of data that contains much of the important information in the larger set.

9. Casual modeling: attempts to help us understand what events or actions actually influence others.

“The Internet Economy” via @cdixon #innovation #AI #Video #Voice

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“The Internet Economy” @cdixon

“We are living in an era of bundling. The big five consumer tech companies — Google, Apple, Facebook, Amazon, and Microsoft — have moved far beyond their original product lines into all sorts of hardware, software, and services that overlap and compete with one another…” “Amazon’s vision here is the most ambitious: to embed voice services in every possible device, thereby reducing the importance of the device, OS, and application layers (it’s no coincidence that those are also the layers in which Amazon is the weakest). But all the big tech companies are investing heavily in voice and AI….” “This would mean that AI interfaces — which in most cases will mean voice interfaces — could become the master routers of the internet economic loop, rendering many of the other layers interchangeable or irrelevant…”

Read the full article on Mediumhttps://medium.com/@cdixon/the-internet-economy-fc43f3eff58a

We Are Coming for You, Tesla, And You, Too, Google, Says Hacker Hotz #AI #Engineering #Leadership

Hacker George Hotz is mobbed after telling SXSW he's coming for Tesla, GM, and Google.

The legendary hacker George Hotz, known by his nom de guerre “geohot,” who first came to public attention by hacking Apple’s (AAPL) first iPhone, spoke this morning at the South by Southwest conference about taking on Tesla’s (TSLA) self-driving car initiatives with his own garage efforts, a talk titled “I built a better self-driving car than Tesla.”By the end of the talk, it was clear he had numerous targets, including Alphabet’s (GOOGL) self-driving car efforts, despite mighty respect for the search giant.Hotz’s achievement, rigging up home made parts to an Acura ILX to make it self-driving, first came to prominence with an article in mid-December by Bloomberg’s Ashlee Vance. Judging by the thunderous applause at the end of the session, and the gaggle of those crowding Hotz to ask questions, Hotz made some converts and fans.

Source: We Are Coming for You, Tesla, And You, Too, Google, Says Hacker Hotz – Tech Trader Daily – Barrons.com

What’s Next in Computing? via @cdixon #AI #iot #mobile #insight #prediction #trends 

 

Tech product cycles are mutually reinforcing interactions between platforms and applications. New platforms enable new applications, which in turn make the new platforms more valuable, creating a positive feedback loop… We can try to understand and predict the product cycle by studying the past and extrapolating into the future.

Read More (via Chris Dixon): What’s Next in Computing?

Just submitted for @shoporg 2016: “Programmatic Commerce: Why Unified Commerce is so important.” #retail #insights #wishmeluck

Proposal Abstract Description:

Unified Commerce [formerly known as Omnichannel] is no longer a nice to have, it is a foundational imperative…but why? Some may say Unified Commerce will provide differentiation and help a retailer meet customer needs and expectations; I believe there is something much greater happening, and if retailers do not take notice their firms could lose complete relevancy in the coming years.

With the advancements being made in artificial intelligence, shoppers will be less and less likely the “buyer.” Virtual private assistants will take over more and more of the routine purchasing decisions. I call this “Programmatic Commerce.” During this session, we will discuss this concept and how it fits into the near term achievement of Unified Commerce. Unified Commerce is just a milestone, not the end game.