Prediki all-purpose prediction

An intersting Start Up in the Big Data Prediction field.

http://gigaom.com/europe/predikis-all-purpose-prediction-promises-attract-austrian-government-cash/

Accurate predictions have been a tantalizing prize since the days of the soothsayers, but these days the business is getting more technical, from collaboration-centric financial forecasting techniques to Nate Silver’s data-driven political predictions . Of course, those two examples are pretty different, but an Austrian firm called Prediki is now trying to come up with a tool for general-purpose predictions.

It’s still all relatively stealthy – that link above won’t tell you much – but the company has nonetheless announced $650k in seed financing from the Austrian government-funded Federal Promotion Bank.

In a statement, CEO Hubertus Hofkirchner said Prediki’s patent-pending technology would be able to “unveil information about the future where traditional market research and opinion survey instruments have proven unreliable or inapplicable”, for clients ranging from companies to governments.

Hofkirchner has form in this business. He was formerly CEO of a company called Redmonitor, which dealt in financial predictions and sold out to CMC Markets , a UK-based derivatives dealer. However, Prediki’s technology has also evolved out of systems that have been used for political polling.

“In the past, the base technology upon which we’re building was mostly used for political forecasts,” Hofkirchner told me. “In recent times it’s probably twice as good as opinion polls for a fraction of the cost. But it was very hard in the past to apply the technology to anything else.

“There has been lots of work done to apply the technology to things like sales forecasts, pharmaceutical approval forecasts, technology adoption, evaluating innovations, evaluating media campaigns and so on – lots of work has been done by various players in the last 10 years. But, while there has been success in the predictive performance of these things, nobody ever cracked the problem that it’s really hard to come up with a model for a truly generic all-purpose prediction market.

So does Prediki’s technology work? What am I, a fortune teller? But the Austrian government seems to have some faith in it (after all, it can apparently be used for NGOs and even governments), so let’s see. The big unveiling should take place sometime in the first quarter of next year.

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Date Converter: Transform a date into different formats

One very common data transformation is the stadardization of dates. This Date Converter Code can be customized to your need. You can use foreign languages or change the order of day, month and year. Also you could include other standardizations like ’01’ and ‘1’.
[sourcecode language=”python”]# Date Converter
# Write a procedure date_converter which takes two inputs. The first is
# a dictionary and the second a string. The string is a valid date in
# the format month/day/year. The procedure should return
# the date written in the form <day> <name of month> <year>.
# For example , if the
# dictionary is in English,
english = {1:"January", 2:"February", 3:"March", 4:"April", 5:"May",
6:"June", 7:"July", 8:"August", 9:"September",10:"October",
11:"November", 12:"December"}
# then  "5/11/2012" should be converted to "11 May 2012".
# If the dictionary is in Swedish
swedish = {1:"januari", 2:"februari", 3:"mars", 4:"april", 5:"maj",
6:"juni", 7:"juli", 8:"augusti", 9:"september",10:"oktober",
11:"november", 12:"december"}
# then "5/11/2012" should be converted to "11 maj 2012".
# Hint: int(’12’) converts the string ’12’ to the integer 12.
def date_converter(dic, string):
first_split = string.find(‘/’)
month = string[0:first_split]
second_split = string.find(‘/’,first_split+1)
day = string[first_split+1:second_split]
year = string [second_split+1:]

month_name= dic[int(month)]

return day+’ ‘+month_name+’ ‘+year
print date_converter(english, ‘5/11/2012’)
#>>> 11 May 2012
print date_converter(english, ‘5/11/12’)
#>>> 11 May 12
print date_converter(swedish, ‘5/11/2012′)
#>>> 11 maj 2012
print date_converter(swedish, ’12/5/1791’)
#>>> 5 december 1791[/sourcecode]

Reducing text to it’s components

This short phyton programm takes a Webpage as an input and reduces it to it’s components. The components are the words on the webpage. You can use this and customize this to fit your purpose. This code can be applied in web-crawlers, text analytics and other fields. For example if you want do leave out stop words you would define a dictonary of this word and include this with anouther if statement. This could be applied if you want to reduce patent data to it’s components and leave generic terms like ‘a’ ‘this’ ‘innovation’ etc. out. You would do this because words like this have no information value.

[sourcecode language=”python”]

def remove_tags(source):

output = [ ]

atsplit = True

splitlist = [‘ ‘,’>’,'<‘,’n’]

i = 0

while i < len(source):

if source[i] == ‘<‘:

i = source.find(‘>’,i+1)

if source[i] in splitlist:

atsplit = True

else:

if atsplit:

output.append(source[i])

atsplit = False

else:

output[-1] = output[-1] + source[i]

i = i + 1

return output[/sourcecode]

 

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Programming like Google, Facebook … : Getting the Basics

It is a new eara. Today programming changed radically from functionality to usablity. A customer expects to be served in milliseconds. Also the amount of data is increasing every second. This requires high performance programming. Internet Companies like google faced this issue ealy on. These companies developed tools and methods to overcome these challange. Many of use call this Big Data Programming. 

Before we can understand Big Data Progamming, we need to understand why it was developed. So I recommend that you learn the basics of googles business: building a search engine. This is also a good start for everybody who never programed before. Afterware in a second post I will introduce you to state of the art Big Data Technologies that help us to use this basic principles on a large scale. Keywords are Hadoop, NoSql, Parallel Programming and a Shared Nothing Architecture. 

 

1. Learn how to built your own search engine

Fortunatly there are great resource out there that help you in a very professional manner. I recommend to you the Python course by Udacity that is though by Sebestian Thrun a Stanford Professor and google fellow. The course is online and can be taken for free. Take a look:

 

Now it is your turn, sign up and learn to bulit your own search engine

 

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Big Data und Algorithmen verändern unsere Karriere

„Predictive analytics“ entscheiden in Zukunft, für welche Jobs wir geeignet sind. Ein US-College kann mit einfachsten Big Data Methoden nach 8 Unterrichtstagen vorhersagen (mit einer 70%-igen Korrektheit) ob ein Student eine Note von Drei oder besser erreicht. Gleichzeitig verwenden Colleges Software, die auf Basis von Schul- und CV-Daten, die optimalen Fächer für den Studenten heraussucht. Auch im Business Bereich ist solche Software vermehrt im Einsatz. Diese kann unter anderem Daten der Computernutzung, unserer Zugangskarten, unsere Handydaten (Nutzung und GPS Ortung) verwenden.

Viele Menschen sorgen sich um diese Fremdbestimmung. In Endeffekt führt dies aber dazu, dass Mitarbeiter Jobs bekommen die Sie nicht überfordern, ihrem Charakter entsprechen und optimal ihre Karriere vorantreiben. Viele Mitarbeiter heute brennen sich in jungen aus. Das ist langfristig schädlich für den Einzelnen und für die Unternehmen. Daher tragen solche Systeme langfristig zu Steigerung der Lebensqualität des Einzelnen, dem Unternehmenswert, dem Wirtschaftswachstum und Wohlstand der Gesellschaft bei.

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