Wednesday, September 21, 2016

Fwd: Top 10 algorithms, data engineering, procurement analytics, + more

I say give this a chance.  It is probably not your cup of tea but this stuff is getting more applicable to more lives and may become of interest.

---------- Forwarded message ----------
From: O'Reilly Data Newsletter <>
Date: Wed, Sep 21, 2016 at 8:01 PM
Subject: Top 10 algorithms, data engineering, procurement analytics, + more

Deep neural networks and the nature of the universe  View in browser > 

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O'Reilly Data Newsletter

1. How to improve procurement analytics

Federico Castanedo explains how your company can significantly improve procurement analytics to solve business questions quickly and effectively.

2. The State of Data Engineering (hint: there's a shortage looming)

"The number of data engineers has doubled in the past year, but engineering leaders still find themselves faced with a significant shortage of data engineering talent." This report takes a look at the current state of data engineering.

+ Michael Li talks about the state of data engineering and data science training programs (podcast.)

Want to speak at Strata + Hadoop World 2017?

Strata + Hadoop World 2017 is coming to San Jose March 14-16, and the call for presenters is now open. If you have great ideas, proven best practices, intriguing case studies, or exceptional technical skills to share, check out our tips for submitting a proposal, and get your proposal in by the September 30 deadline.

3. The extraordinary link between deep neural networks and the nature of the universe

"Deep neural networks are now better than humans at tasks such as face recognition and object recognition....But there is a problem. There is no mathematical reason why networks arranged in layers should be so good at these challenges. Mathematicians are flummoxed. Despite the huge success of deep neural networks, nobody is quite sure how they achieve their success. Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. These guys say the reason why mathematicians have been so embarrassed is that the answer depends on the nature of the universe. In other words, the answer lies in the regime of physics rather than mathematics."

+ Here's the paper.

4. Can bots replace lawyers?

In this episode of the O'Reilly Bots Podcast, Joshua Browder, Pete Skomoroch, and Jon Bruner discuss bots that fight bureaucracy.

+ Bot Day is coming to San Francisco October 19. Space is limited; reserve your spot today.

5. Obfuscated? Not so much.

Internet privacy measures, such blurred or pixelated images, are easier to crack than ever. "Using simple deep learning tools, the three-person team was able to identify obfuscated faces and numbers with alarming accuracy....On an industry standard dataset where humans had 0.19% chance of identifying a face, the algorithm had 71% accuracy (or 83% if allowed to guess five times)."
In collaboration with Tamr

Free chapters from Data Wrangling with Python

Learn enough Python to get stuff done. Data Wrangling with Python shows non-programmers how to process information that's initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. And—compliments of Tamr—you can get two chapters from this useful guide, free. Chapter 6 covers acquiring and storing data; chapter 7 covers data cleanup.
Download the free chapters →

6. When did data visualizations become popular?

Here are some charts about when charts became popular.

Bot Day Early Price deadline is this Friday

Bot Day is happening in San Francisco on October 19, and the Early Price deadline for Bot Day is midnight PT this Friday, September 23. If you're interested in building bots or developing a bot strategy, register now to save money—and to save your spot (space is limited).
Learn more →

7. Top 10 algorithms used by data scientists

A KD Nuggets poll asked "which methods/algorithms you used in the past 12 months for an actual Data Science-related application." Here are the results.

8. Mind the gap

When a project that used social media to predict unemployment by the frequency that people used words like "jobs", "unemployment," and "classifieds" saw a sudden spike, researchers anticipated a surge in the unemployment rate. But people stayed employed—and saddened by the death of Steve "Jobs." JP Morgan's $6B London Whale trading loss was partly the result of Excel errors in a financial model. A recent survey showed that 80% of the organizations surveyed said they'd made a strategic decision based on flawed information at least once in the last three years. Bad data isn't your only concern; misinterpreted data can be costly and embarrassing too.
In collaboration with Teradata

How to derive value from the data lake

Teradata surveyed 200 IT and business professionals to find best practices and sticking points for data lake usage. Join Nik Rouda, Senior Analyst for Enterprise Strategy, in a free 60-minute webcast, where he'll share the results along with insights into why businesses still struggle to drive value from their Hadoop data lake—and what they can do about it.
Thursday, October 6 10 am PT
Learn more →

9. Most mathematicians hail from just 24 "families"

"Most of the world's mathematicians fall into just 24 scientific 'families', one of which dates back to the fifteenth century. The insight comes from an analysis of the Mathematics Genealogy Project (MGP), which aims to connect all mathematicians, living and dead, into family trees on the basis of teacher–pupil lineages, in particular, who an individual's doctoral adviser was."

Upcoming Spark online courses

There are two upcoming online Spark courses you should take a look at:

Distributed Computing with Spark for Beginners
Develop, build, and deploy Spark jobs (October 10, 12 & 14)

Managing Enterprise Data Strategies with Hadoop, Spark, and Kafka
Learn how to ensure the success of your data pipeline project and avoid common mistakes (October 18 & 19)

Space is limited, so make your plans soon.

10. Editor's pick: The Global Impact of Open Data (free ebook)

The Global Impact of Open Data: Findings from Detailed Case Studies Around the World has just been released. It presents detailed case studies of open data projects throughout the world, along with in-depth analysis of what works and what doesn't.
Download the free ebook →
Read more about data on →

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