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Data Science Is Now a Job Market Based Entirely on Merit - IEEE Spectrum

A start-up ranks data scientists and creates competitions between them for specific consulting projects
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luiy's curator insight, May 18, 2013 3:14 PM

When you need to hire a professional these days—a programmer, a doctor, a lawyer—it can be hard to choose. They sort of rank themselves, by their fees; generally the better ones are more expensive, but that’s a pretty inexact rule of thumb. What if you could rank them independent of price?

 

Then too, they’re not exactly interchangeable—you don’t need just any doctor, you need an oncologist, you don’t need any lawyer, you need a bankruptcy attorney. In fact, you need the best person for your situation, which, darn it, isn’t exactly like anyone else’s. What if you could get them bidding to solve your particular problem—and telling you exactly how they would solve it?

 

There’s one market where this is actually happening—the market for data scientists, the sort of mathematicians we used to call statisticians, until data became big and sexy, like the way we renamed the Patagonian toothfish Chilean sea bass.

 

My guest today is Anthony Goldbloom, founder and CEO of Kaggle, which describes itself as “the world's largest community of data scientists. They compete with each other to solve complex data science problems, and the top competitors win interesting projects from some of the world’s biggest companies.”

 

Goldbloom himself is a data scientist, with a degree in economics and econometrics from the University of Melbourne. Before starting Kaggle, he did macroeconomic modeling for the Reserve Bank of Australia and the Australian Treasury. He joins us by phone.

Data is big
"The future is here. It's just not evenly distributed yet." - William Gibson     :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
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AudioSet

A large-scale dataset of manually annotated audio eventsAudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos. The ontology is specified as a hierarchical graph of event categories, covering a wide range of human and animal sounds, musical instruments and genres, and common everyday environmental sounds. By releasing AudioSet, we hope to provide a common, realistic-scale evaluation task for audio event detection, as well as a starting point for a comprehensive vocabulary of sound events.
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A large-scale dataset of manually annotated audio events AudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos.
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Explaining the decisions of machine learning algorithms | StatsBlogs.com | All About Statistics

Explaining the decisions of machine learning algorithms | StatsBlogs.com | All About Statistics | Data is big | Scoop.it
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Machine Learning Boot Camp - live and archived videos Jan. 23 – Jan. 27, 2017

Machine Learning Boot Camp - live and archived videos Jan. 23 – Jan. 27, 2017 | Data is big | Scoop.it
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Machine Learning Boot Camp Jan. 23 – Jan. 27, 2017
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Introduction to Forecasting with ARIMA in R

Data Scientist Ruslana Dalinina explains how to forecast demand with ARIMA in R. Learn how to fit, evaluate, and iterate an ARIMA model with this tutorial.
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46 Questions on SQL to test a data science professional (Skilltest Solution)

This article features 46 questions on SQL every data science professional should know. Questions related to DDL, DML, Joins, Update, Drop, where, Groupby
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Making an R based ML model accessible through a simple API

Building an accurate machine learning (ML) model is a feat on its own. But once you’re there, you still need to find a way to make the model accessible t
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Analyzing Genomics Data at Scale using R, AWS Lambda, and Amazon API Gateway | AWS Compute Blog

Analyzing Genomics Data at Scale using R, AWS Lambda, and Amazon API Gateway | AWS Compute Blog | Data is big | Scoop.it
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Learning Reinforcement Learning (With Code, Exercises and Solutions) | Open Data Science

Skip all the talk and go directly to the Github Repo with code and exercises. WHY STUDY REINFORCEMENT LEARNING Reinforcement Learning is one of the fields I’m
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What I Learned Recreating One Chart Using 24 Tools - Features - Source: An OpenNews project

What I Learned Recreating One Chart Using 24 Tools - Features - Source: An OpenNews project | Data is big | Scoop.it
Source - Journalism Code, Context & Community
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50 things I learned at NIPS 2016 – Ought

50 things I learned at NIPS 2016 – Ought | Data is big | Scoop.it
I learned many things about AI and machine learning at the NIPS 2016 conference. Here are a few that are particularly suited to being communicated in the space of a few sentences.
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Introducing the Data Science Maturity Model

Introducing the Data Science Maturity Model | Data is big | Scoop.it
The Data Science Maturity Model helps leaders and practitioners identify existing gaps and direct investment in their data science programs.
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Explained Visually

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Explained Visually (EV) is an experiment in making hard ideas intuitive inspired the work of Bret Victor's Explorable Explanations.
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Unpacking Assignment %<-% 

The zeallot package defines an operator for unpacking assignment, sometimes called parallel assignment or destructuring assignment in other programming languages. The operator is written as %<-% and used like this.

{ lat : lng } %<-% list(38.061944, -122.643889)
The result is that the list is unpacked into its elements, and the elements are assigned to lat and lng.
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The most comprehensive Data Science learning plan for 2017

This article features a year long learning path for aspiring data scientist, intermediate & transitioner to progress in data science industry for R & Python
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How to find daily good deals online, automatically with R?

How to find daily good deals online, automatically with R? | Data is big | Scoop.it
As defined here, “a data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.” Therefore, this blog post focuses on…
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Deep Learning Gallery - a curated list of awesome deep learning projects

Deep Learning Gallery - a curated list of awesome deep learning projects | Data is big | Scoop.it
Deep Learning Gallery - a curated list of awesome deep learning projects
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A Guide to Deep Learning by YerevaNN

Deep learning is a fast-changing field at the intersection of computer science and mathematics. It is a relatively new branch of a wider field called machine learning.
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Daniel Oratokhai's curator insight, January 2, 5:35 AM

A Guide to Deep Learning by YerevaNN

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Practical Deep Learning For Coders—18 hours of lessons for free

Practical Deep Learning For Coders—18 hours of lessons for free | Data is big | Scoop.it
Welcome to fast.ai's 7 week course, "Practical Deep Learning For Coders, Part 1", taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing... it's totally free!
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Daniel Oratokhai's curator insight, January 3, 1:01 AM

Practical Deep Learning For Coders—18 hours of lessons for free

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State of the art deep learning model for question answering

State of the art deep learning model for question answering | Data is big | Scoop.it
We introduce the Dynamic Coattention Network, a state of the art question answering deep learning model that significantly outperforms all existing systems on the Stanford Question Answering dataset.
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The major advancements in Deep Learning in 2016 - Tryolabs Blog

Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
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