How Big Data Could Improve the Business Lending Industry

By | iot


By now, most banks and financial institutions are already investing in big data. They’re pulling data from their customers and financial markets in new ways, and they’re hiring more data analysts and engineers to make the best use of that data. In fact, by the end of the decade, we could see the global existence of 45 zettabytes—that’s 45 trillion gigabytes—and an enormous shortfall of data analysis talent to keep up with demand.

So what exactly are banks using all this data for? In addition to making better investments and improving infrastructure and technology, many banks are putting this data to use in the lending sector—which could herald the changes necessary to spark a revolution in business lending.

The Business Lending Problem

Small business loans are an option available to aspiring entrepreneurs who have strong ideas for new businesses, but not enough capital to make those ideas a reality. Unfortunately, banks are notoriously strict about who they give loans to and how. From a logistical perspective, this is a necessity; if banks lent money to everyone, they’d never make a profit, and they’d cease to exist.

But current circumstances make it hard for entrepreneurs with little experience, or those with poor credit, to get …

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ACEMS Business Analytics Prize 2016

By | ai, bigdata, machinelearning


We have established a new annual prize for research students at Monash University in the general area of business analytics, funded by the Australian Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS). The rules of the award are listed below.
The student must have submitted a paper to a high quality journal or refereed conference on some topic in the general area of business analytics, computational statistics or data visualization.


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What's your Hall number?

By | machinelearning

Today I attended the funeral of Peter Hall, one of the finest mathematical statisticians ever to walk the earth and easily the best from Australia. One of the most remarkable things about Peter was his astonishing productivity, with over 600 papers. As I sat in the audience I realised that many of the people there were probably coauthors of papers with Peter, and I wondered how many statisticians in the world would have been his coauthors or second-degree co-authors.

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Mike Bostock graphs federal income tax brackets and tax rates, and I connect to some general principles of statistical graphics

By | ai, bigdata, machinelearning

(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Mike “d3” Bostock writes:

Regarding the Vox graph on federal tax brackets, here is a quick-and-dirty visualization of effective tax rates for a given taxable income and year.

However, there is a big caveat: estimating the effective tax rate based on actual income is much harder since it depends on the claimed deductions. This could be estimated empirically, but the IRS doesn’t publish the data (AFAIK).

Bostock writes:

I’ve recreated the graphic [by Alvin Chang for Vox, criticized in my earlier post] below, substituting a log scale for the y-axis. It readily conveys the Reagan-era simplification of tax brackets, as well as the disappearance of tax brackets for the ultra-rich. (In 1936, the highest tax bracket applied to those making more than $83M in 2013-equivalent dollars!)

Yet fewer tax brackets do not imply the overall tax code is simpler; if anything, the tax code continues to get more complex. And looking only at bracket thresholds does not consider the effective rate at different income levels. . . . It is hard to estimate effective tax rates, especially now, because they depend greatly on the amount of itemized deductions. But ignoring that substantial caveat—and that this analysis only considers federal-reported income and not capital gains, the alternative minimum tax, and countless other forms of state and local taxes—we can compute the effective federal income tax rate for a given taxable income (after any deductions) and a given year.

Amounts are in 2013-equivalent dollars when filing as the head-of-household.

Good job.

Here are some relevant principles of statistical graphics:

1. Static graphs can do a lot. Dynamic graphics are fine, but in some settings they do little more than add confusion.

2. The log transform really works.

3. No need to try to cram all the information into one graph. Bostock made one graph of tax brackets, another of tax rates. Someone could come along and make a third graph including other taxes, not just federal income tax.

Also, I don’t think graphics need to be so big. I display Bostock’s graphs above in a more compressed format than were on his page. I think that’s fine; actually I think these smaller versions are easier to read because I can see the whole graph more clearly in my visual field. In general I recommend that people make their graphs smaller, which implies that their labels should be larger relative to the original graphs. For Bostock, I’d actually recommend just putting x-axis labels every 20 years, percentage labels at every 25%, and income labels at 1, 3, 10, 30, etc. Some of this is a matter of taste, but I do think there are general issues of readability, and tradeoffs in that more labels make it harder to see the big picture but easier to identify exactly what is happening when.

The post Mike Bostock graphs federal income tax brackets and tax rates, and I connect to some general principles of statistical graphics appeared first on Statistical Modeling, Causal Inference, and Social Science.

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The post Mike Bostock graphs federal income tax brackets and tax rates, and I connect to some general principles of statistical graphics appeared first on All About Statistics.




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For Arduino Nano V3.0, Elegoo Nano board CH340/ATmega328P without USB cable, compatible with Arduino Nano V3.0 (Nano x 3 w/o cable)

By | iot, machinelearning

The Nano is a small, complete, and breadboard-friendly board based on the ATmega328 (Arduino Nano 3.x). It has more or less the same functionality of the Arduino/Genuino UNO, but in a different package. It lacks only a DC power jack, and works with a Mini-B USB cable instead of a standard one.
You could download the driver of Elegoo Nano V3.0 at: http://bit.ly/2pMF4in

Technical specs
Microcontroller: ATmega328
Architecture: AVR
Operating Voltage: 5 V
Flash Memory: 32 KB of which 2 KB used by bootloader
SRAM: 2 KB
Clock Speed: 16 MHz
Analog I/O Pins: 8
EEPROM: 1 KB
DC Current per I/O Pins: 40 mA (I/O Pins)
Input Voltage: 7-12 V
Digital I/O Pins: 22
PWM Output: 6
Power Consumption: 19 mA
PCB Size: 18 x 45 mm
Weight: 7 g

Package Include :
3 X Nano Board
You could download the driver of Elegoo Nano V3.0 at: http://bit.ly/2pMF4in
The Nano is 100% compatible with Arduino, using the same chips ATmega328P and CH340 with the official version.
It is a smallest, complete, and breadboard friendly board. It has everything that Diecimila/Duemilanove has (electrically) with more analog input pins and onboard +5V AREF jumper.
Nano’s got the breadboard-ability of the Boarduino and the Mini+USB with smaller footprint than either, so users have more breadboard space. It’s got a pin layout that works well with the Mini or the Basic Stamp (TX, RX, ATN, GND on one top, power and ground on the other).
The Nano can be powered via the Mini-B USB connection, 6-20V unregulated external power supply (pin 30), or 5V regulated external power supply (pin 27). The power source is automatically selected to the highest voltage source.

$19.99



Four short links: 25 May 2017

By | ai, bigdata, machinelearning

Crypto vs. Regulation, Crippling Genomic Research, There Are Bots, and Web Security

  1. Chaffinch — crypto system that’s an interesting response to an attempt to regulate crypto. The Chaffinch system allows several further messages to be steganographically concealed behind the main message. This allows cover traffic to be divulged to any authorities who wish to inspect the confidential information, without compromising the hidden material. The system is evaluated not only in terms of the traditional threat to confidentiality, eavesdroppers with significant computing power, but also in terms of its interaction with the U.K.’s Regulation of Investigatory Powers (RIP) Act, one of the first laws to attempt to engage with cryptography.
  2. We’re About to Cripple the Genomic Medical Era (DJ Patil) — When we were developing the Precision Medicine Initiative and meeting with Americans across the country, a key concern was ensuring that their data couldn’t be used against them or their families (this is genetic information, so if you share a biological basis, you have overlap in the data). If there is any threat of this data being used in a way that is contrary to research, my deep fear is that people won’t be willing to donate their data. And there are too many people who have diseases who need us to donate our data to help.
  3. There Are Bots, Look Around (Renee DiResta) — Something very similar happened in finance with the advent of high-frequency trading (the world I came from as a trader at Jane Street): technology was used to distort information flows and access in much the same way it is now being used to distort and game the marketplace of ideas. The future arrived a lot earlier for finance than for politics.
  4. Web Developer Security ChecklistThis checklist is simple, and by no means complete. I’ve been developing secure web applications for over 14 years, and this list contains some of the more important issues that I’ve painfully learned over this period. I hope you will consider them seriously when creating a web application.

Continue reading Four short links: 25 May 2017.




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Mike Bostock graphs federal income tax brackets and tax rates, and I connect to some general principles of statistical graphics

By | machinelearning

Mike “d3” Bostock writes:

Regarding the Vox graph on federal tax brackets, here is a quick-and-dirty visualization of effective tax rates for a given taxable income and year.

However, there is a big caveat: estimating the effective tax rate based on actual income is much harder since it depends on the claimed deductions. This could be estimated empirically, but the IRS doesn’t publish the data (AFAIK).

Bostock writes:

I’ve recreated the graphic [by Alvin Chang for Vox, criticized in my earlier post] below, substituting a log scale for the y-axis. It readily conveys the Reagan-era simplification of tax brackets, as well as the disappearance of tax brackets for the ultra-rich. (In 1936, the highest tax bracket applied to those making more than $83M in 2013-equivalent dollars!)

Yet fewer tax brackets do not imply the overall tax code is simpler; if anything, the tax code continues to get more complex. And looking only at bracket thresholds does not consider the effective rate at different income levels. . . . It is hard to estimate effective tax rates, especially now, because they depend greatly on the amount of itemized deductions. But ignoring that substantial caveat—and that this analysis only considers federal-reported income and not capital gains, the alternative minimum tax, and countless other forms of state and local taxes—we can compute the effective federal income tax rate for a given taxable income (after any deductions) and a given year.

Amounts are in 2013-equivalent dollars when filing as the head-of-household.

Good job.

Here are some relevant principles of statistical graphics:

1. Static graphs can do a lot. Dynamic graphics are fine, but in some settings they do little more than add confusion.

2. The log transform really works.

3. No need to try to cram all the information into one graph. Bostock made one graph of tax brackets, another of tax rates. Someone could come along and make a third graph including other taxes, not just federal income tax.

Also, I don’t think graphics need to be so big. I display Bostock’s graphs above in a more compressed format than were on his page. I think that’s fine; actually I think these smaller versions are easier to read because I can see the whole graph more clearly in my visual field. In general I recommend that people make their graphs smaller, which implies that their labels should be larger relative to the original graphs. For Bostock, I’d actually recommend just putting x-axis labels every 20 years, percentage labels at every 25%, and income labels at 1, 3, 10, 30, etc. Some of this is a matter of taste, but I do think there are general issues of readability, and tradeoffs in that more labels make it harder to see the big picture but easier to identify exactly what is happening when.

The post Mike Bostock graphs federal income tax brackets and tax rates, and I connect to some general principles of statistical graphics appeared first on Statistical Modeling, Causal Inference, and Social Science.

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