Tag: marketing

Rapid Miner User Conference 2012

One of those cool conferences that is on my bucket list- this time in Hungary (That’s a nice place)

But I am especially interested in seeing how far Radoop has come along !

Disclaimer- Rapid Miner has been a Decisionstats.com sponsor  for many years. It is also a very cool software but I like the R Extension facility even more!

—————————————————————

and not very expensive too compared to other User Conferences in Europe!-

http://rcomm2012.org/index.php/registration/prices

Information about Registration

  • Early Bird registration until July 20th, 2012.
  • Normal registration from July 21st, 2012 until August 13th, 2012.
  • Latest registration from August 14th, 2012 until August 24th, 2012.
  • Students have to provide a valid Student ID during registration.
  • The Dinner is included in the All Days and in the Conference packages.
  • All prices below are net prices. Value added tax (VAT) has to be added if applicable.

Prices for Regular Visitors

Days and Event
Early Bird Rate
Normal Rate
Latest Registration
Tuesday

(Training / Development 1)

 190 Euro 230 Euro 280 Euro
Wednesday + Thursday

(Conference)

 290 Euro 350 Euro 420 Euro
Friday

(Training / Development 2 and Exam)

 190 Euro 230 Euro 280 Euro
All Days

(Full Package)

 610 Euro 740 Euro 900 Euro

Prices for Authors and Students

In case of students, please note that you will have to provide a valid student ID during registration.

Days and Event
Early Bird Rate
Normal Rate
Latest Registration
Tuesday

(Training / Development 1)

 90 Euro 110 Euro 140 Euro
Wednesday + Thursday

(Conference)

 140 Euro 170 Euro 210 Euro
Friday

(Training / Development 2 and Exam)

 90 Euro 110 Euro 140 Euro
All Days

(Full Package)

 290 Euro 350 Euro 450 Euro
Time
Slot
Tuesday
Training / Workshop 1
Wednesday
Conference 1
Thursday
Conference 2
Friday
Training / Workshop 2
09:00 – 10:30
 Introductory Speech
Ingo Mierswa; Rapid-I 

Data Analysis

 

NeurophRM: Integration of the Neuroph framework into RapidMiner
Miloš Jovanović, Jelena Stojanović, Milan Vukićević, Vera Stojanović, Boris Delibašić (University of Belgrade)

 To be announced (Invited Talk)
To be announced

 

Recommender Systems

 

Extending RapidMiner with Recommender Systems Algorithms
Matej Mihelčić, Nino Antulov-Fantulin, Matko Bošnjak, Tomislav Šmuc (Ruđer Bošković Institute)

Implementation of User Based Collaborative Filtering in RapidMiner
Sérgio Morais, Carlos Soares (Universidade do Porto)

 Parallel Training / Workshop Session

Advanced Data Mining and Data Transformations

or

Development Workshop Part 2
10:30 – 12:30
 Data Analysis

Nearest-Neighbor and Clustering based Anomaly Detection Algorithms for RapidMiner
Mennatallah Amer, Markus Goldstein (DFKI)

Customers’ LifeStyle Targeting on Big Data using Rapid Miner
Maksim Drobyshev (LifeStyle Marketing Ltd)

Robust GPGPU Plugin Development for RapidMiner
Andor Kovács, Zoltán Prekopcsák (Budapest University of Technology and Economics)

 Extensions

Image Mining Extension – Year After
Radim Burget, Václav Uher, Jan Mašek (Brno University of Technology)

Incorporating R Plots into RapidMiner Reports
Peter Jeszenszky (University of Debrecen)

An Octave Extension for RapidMiner
Sylvain Marié (Schneider Electric)
12:30 – 13:30
Lunch
Lunch
Lunch
13:30 – 15:00
 Parallel Training / Workshop Session

Basic Data Mining and Data Transformations

or

Development Workshop Part 1

 Applications

Application of RapidMiner in Steel Industry Research and Development
Bengt-Henning Maas, Hakan Koc, Martin Bretschneider (Salzgitter Mannesmann Forschung)

A Comparison of Data-driven Models for Forecast River Flow
Milan Cisty, Juraj Bezak (Slovak University of Technology)

Portfolio Optimization Using Local Linear Regression Ensembles in Rapid Miner
Gábor Nagy, Tamás Henk, Gergő Barta (Budapest University of Technology and Economics)

 Unstructured Data


Processing Data Streams with the RapidMiner Streams-Plugin
Christian Bockermann, Hendrik Blom (TU Dortmund)

Automated Creation of Corpuses for the Needs of Sentiment Analysis
Peter Koncz, Jan Paralic (Technical University of Kosice)

 

Demonstration

 

News from the Rapid-I Labs
Simon Fischer; Rapid-I

This short session demonstrates the latest developments from the Rapid-I lab and will let you how you can build powerful analysis processes and routines by using those RapidMiner tools.

 Certification Exam
15:00 – 17:00
 Book Presentation and Game Show

Data Mining for the Masses: A New Textbook on Data Mining for Everyone
Matthew North (Washington & Jefferson College)

Matthew North presents his new book “Data Mining for the Masses” introducing data mining to a broader audience and making use of RapidMiner for practical data mining problems.

 

Game Show
Did you miss last years’ game show “Who wants to be a data miner?”? Use RapidMiner for problems it was never created for and beat the time and other contestants!

 User Support

Get some Coffee for free – Writing Operators with RapidMiner Beans
Christian Bockermann, Hendrik Blom (TU Dortmund)

Meta-Modeling Execution Times of RapidMiner operators
Matija Piškorec, Matko Bošnjak, Tomislav Šmuc (Ruđer Bošković Institute) 
19:00
Social Event (Conference Dinner)
Social Event (Visit of Bar District)

 

Training: Basic Data Mining and Data Transformations

This is a short introductory training course for users who are not yet familiar with RapidMiner or only have a few experiences with RapidMiner so far. The topics of this training session include

  • Basic Usage
    • User Interface
    • Creating and handling RapidMiner repositories
    • Starting a new RapidMiner project
    • Operators and processes
    • Loading data from flat files
    • Storing data, processes, and results
  • Predictive Models
    • Linear Regression
    • Naïve Bayes
    • Decision Trees
  • Basic Data Transformations
    • Changing names and roles
    • Handling missing values
    • Changing value types by discretization and dichotimization
    • Normalization and standardization
    • Filtering examples and attributes
  • Scoring and Model Evaluation
    • Applying models
    • Splitting data
    • Evaluation methods
    • Performance criteria
    • Visualizing Model Performance

 

Training: Advanced Data Mining and Data Transformations

This is a short introductory training course for users who already know some basic concepts of RapidMiner and data mining and have already used the software before, for example in the first training on Tuesday. The topics of this training session include

  • Advanced Data Handling
    • Sampling
    • Balancing data
    • Joins and Aggregations
    • Detection and removal of outliers
    • Dimensionality reduction
  • Control process execution
    • Remember process results
    • Recall process results
    • Loops
    • Using branches and conditions
    • Exception handling
    • Definition of macros
    • Usage of macros
    • Definition of log values
    • Clearing log tables
    • Transforming log tables to data

 

Development Workshop Part 1 and Part 2

Want to exchange ideas with the developers of RapidMiner? Or learn more tricks for developing own operators and extensions? During our development workshops on Tuesday and Friday, we will build small groups of developers each working on a small development project around RapidMiner. Beginners will get a comprehensive overview of the architecture of RapidMiner before making the first steps and learn how to write own operators. Advanced developers will form groups with our experienced developers, identify shortcomings of RapidMiner and develop a new extension which might be presented during the conference already. Unfinished work can be continued in the second workshop on Friday before results might be published on the Marketplace or can be taken home as a starting point for new custom operators.

Awesome website for #rstats Mining Twitter using R

Just came across this very awesome website.

Did you know there were six kinds of wordclouds in R.

(giggles like a little boy)

https://sites.google.com/site/miningtwitter/questions/talking-about

 

Simple Wordcloud
Comparison Wordcloud
Tweets about some given topic
Tweets of some given user (ex 1)
Tweets of some given user (ex 2)
Modified tag-cloud

This guy – the force is strong in him

Gaston Sanchez 
Data Analysis + Visualization + Statistics + R = FUN

http://www.gastonsanchez.com/about

 Contact Info
 gaston.stat@gmail.com		 > home
 
linkedIn
pinterest
resume.pdf
About Currently, I’m a postdoc in Rasmus Nielsen’s Lab in the Center for Theoretical Evolutionary Genomics at the University of California, Berkeley. I’m also collaborating with the Biology Scholars Program (BSP) at UC Berkeley, and I am affiliated to the Program on Reproductive Health and the Environment (PRHE) at UC San Francisco. In my (scarce) free time outside the academic world, I often work on collaborative projects for marketing analytics, statistical consulting, and statistical advising in general.

Interview Jason Kuo SAP Analytics #Rstats

Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.

Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.

Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.

Ajay- When did you first start getting interested in R as an analytics platform?

Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.

Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?

Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville

Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.

Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings. 

Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.

About-

Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.

 

Doing RFM Analysis in R



RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail.




RFM stands for




    

  • Recency – How recently did the customer purchase?
    • 

  • Frequency – How often do they purchase?
    • 

  • Monetary Value – How much do they spend?
    • 





To create an RFM analysis, one creates categories for each attribute. For instance, the Recency attribute might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be arrived at by applying business rules, or using a data mining technique, such as CHAID, to find meaningful breaks.


from-http://en.wikipedia.org/wiki/RFM


If you are new to RFM or need more step by step help, please read here


https://decisionstats.com/2010/10/03/ibm-spss-19-marketing-analytics-and-rfm/


and here is R code- note for direct marketing you need to compute Monetization based on response rates (based on offer date) as well






##Creating Random Sales Data of the format CustomerId (unique to each customer), Sales.Date,Purchase.Value

sales=data.frame(sample(1000:1999,replace=T,size=10000),abs(round(rnorm(10000,28,13))))

names(sales)=c("CustomerId","Sales Value")

sales.dates <- as.Date("2010/1/1") + 700*sort(stats::runif(10000))

#generating random dates

sales=cbind(sales,sales.dates)

str(sales)

sales$recency=round(as.numeric(difftime(Sys.Date(),sales[,3],units="days")) )

library(gregmisc)

##if you have existing sales data you need to just shape it in this format

rename.vars(sales, from="Sales Value", to="Purchase.Value")#Renaming Variable Names

## Creating Total Sales(Monetization),Frequency, Last Purchase date for each customer

salesM=aggregate(sales[,2],list(sales$CustomerId),sum)

names(salesM)=c("CustomerId","Monetization")

salesF=aggregate(sales[,2],list(sales$CustomerId),length)

names(salesF)=c("CustomerId","Frequency")

salesR=aggregate(sales[,4],list(sales$CustomerId),min)

names(salesR)=c("CustomerId","Recency")

##Merging R,F,M

test1=merge(salesF,salesR,"CustomerId")

salesRFM=merge(salesM,test1,"CustomerId")

##Creating R,F,M levels 

salesRFM$rankR=cut(salesRFM$Recency, 5,labels=F) #rankR 1 is very recent while rankR 5 is least recent

salesRFM$rankF=cut(salesRFM$Frequency, 5,labels=F)#rankF 1 is least frequent while rankF 5 is most frequent

salesRFM$rankM=cut(salesRFM$Monetization, 5,labels=F)#rankM 1 is lowest sales while rankM 5 is highest sales

##Looking at RFM tables


table(salesRFM[,5:6])
table(salesRFM[,6:7])
table(salesRFM[,5:7])








Code Highlighted by Pretty R at inside-R.org


Note-you can also use quantile function instead of cut function. This changes cut to equal length instead of equal interval. or  see other methods for finding breaks for categories.


 

			


	





	

		
		
iPhones Siri to replace call centers . Is it doable
	


	
	
	

		
I was wondering why the planet  spends  so much money in the $150-billion business process outsourcing industry, especially in voice calls to call centers.


If your iPhone Siri phone can be configured to answer any query, Why can’t it be configured to be  a virtual assistant, customer support, marketing outbound or even a super charged call center interactive voice response .


Can we  do and run some tests on this?



	


	





	

		
		
Interview: Hjálmar Gíslason, CEO of DataMarket.com
	


	
	
	

		
Here is an interview with Hjálmar Gíslason, CEO of Datamarket.com  . DataMarket is an active marketplace for structured data and statistics. Through powerful search and visual data exploration, DataMarket connects data seekers with data providers.




 


Ajay-  Describe your journey as an entrepreneur and techie in Iceland. What are the 10 things that surprised you most as a tech entrepreneur.


HG- DataMarket is my fourth tech start-up since at age 20 in 1996. The previous ones have been in gaming, mobile and web search. I come from a technical background but have been moving more and more to the business side over the years. I can still prototype, but I hope there isn’t a single line of my code in production!


Funny you should ask about the 10 things that have surprised me the most on this journey, as I gave a presentation – literally yesterday – titled: “9 things nobody told me about the start-up business”


They are:

* Do NOT generalize – especially not to begin with

* Prioritize – and find a work-flow that works for you

* Meet people – face to face

* You are a sales person – whether you like it or not

* Technology is not a product – it’s the entire experience

* Sell the current version – no matter how amazing the next one is

* Learn from mistakes – preferably others’

* Pick the right people – good people is not enough

* Tell a good story – but don’t make them up


I obviously elaborate on each of these points in the talk, but the points illustrate roughly some of the things I believe I’ve learned … so far 😉


9 things nobody told me about the start-up business 




Ajay- 


Both Amazon  and Google  have entered the public datasets space. Infochimps  has 14,000+ public datasets. The US has http://www.data.gov/ 


So clearly the space is both competitive and yet the demand for public data repositories is clearly under served still. 


How does DataMarket intend to address this market in a unique way to differentiate itself from others.






HG- DataMarket is about delivering business data to decision makers. We help data seekers find the data they need for planning and informed decision making, and data publishers reaching this audience. DataMarket.com is the meeting point, where data seekers can come to find the best available data, and data publishers can make their data available whether for free or for a fee. We’ve populated the site with a wealth of data from public sources such as the UN, Eurostat, World Bank, IMF and others, but there is also premium data that is only available to those that subscribe to and pay for the access. For example we resell the entire data offering from the EIU (Economist Intelligence Unit) (link: http://datamarket.com/data/list/?q=provider:eiu)


DataMarket.com allows all this data to be searched, visualized, compared and downloaded in a single place in a standard, unified manner.


We see many of these efforts not as competition, but as valuable potential sources of data for our offering, while others may be competing with parts of our proposition, such as easy access to the public data sets.




 




Ajay- What are your views on data confidentiality and access to data owned by Governments funded by tax payer money.




HG- My views are very simple: Any data that is gathered or created for taxpayers’ money should be open and free of charge unless higher priorities such as privacy or national security indicate otherwise.


Reflecting that, any data that is originally open and free of charge is still open and free of charge on DataMarket.com, just easier to find and work with.


Ajay-  How is the technology entrepreneurship and venture capital scene in Iceland. What things work and what things can be improved?




HG- The scene is quite vibrant, given the small community. Good teams with promising concepts have been able to get the funding they need to get started and test their footing internationally. When the rapid growth phase is reached outside funding may still be needed.


There are positive and negative things about any location. Among the good things about Iceland from the stand point of a technology start-up are highly skilled tech people and a relatively simple corporate environment. Among the bad things are a tiny local market, lack of skills in international sales and marketing and capital controls that were put in place after the crash of the Icelandic economy in 2008.


I’ve jokingly said that if a company is hot in the eyes of VCs it would get funding even if it was located in the jungles of Congo, while if they’re only lukewarm towards you, they will be looking for any excuse not to invest. Location can certainly be one of them, and in that case being close to the investor communities – physically – can be very important.


We’re opening up our sales and marketing offices in Boston as we speak. Not to be close to investors though, but to be close to our market and current customers.




Ajay- Describe your hobbies when you are not founding amazing tech startups.




HG- Most of my time is spent working – which happens to by my number one hobby.


It is still important to step away from it all every now and then to see things in perspective and come back with a clear mind.


I *love* traveling to exotic places. Me and my wife have done quite a lot of traveling in Africa and S-America: safari, scuba diving, skiing, enjoying nature. When at home I try to do some sports activities 3-4 times a week at least, and – recently – play with my now 8 month old son as much as I can.


About-


http://datamarket.com/p/about/team/


Management


Hjalmar GislasonHjálmar Gíslason, Founder and CEO: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise. DataMarket is based largely on his vision of the need for a global exchange for structured data.


hjalmar.gislason@datamarket.com




To know more, have a quick  look at  http://datamarket.com/

	


	





	

		
		
On Software
	


	
	
	

		
1) All software has bugs. Sometimes this is because people have been told to code in a hurry to meet shipping deadlines. Sometimes it is due to the way metal and other software interact with it. Mostly it is karma.


2) In the 21 st Century,It is okay to insult someone over his software , but not over most other things. Sometimes I think people are passionate not just for their own software but to just diss the other guys. It is a politically convenient release.


3) Bloggers writing about software are full of bull-by products. If they were any good in writing code, they would not have time to write a blog. Mostly bloggers on code are people whose coding enthusiasm is more than their coding competence.


4) Software is easier than it looks to people who know it. To those who dont know how to code, it will always be a bit of magic.


5) Despite immense progress, initiatives and encouragement- the number of females writing code is too low . Comparatively, figuratively and literally. If you are a male and want a social life- get into marketing while the hair is still black.


Man walks into Bar. Says to Women at Bar. ” Hey,What do you do, Me- I write code”


See!


6) People who write software end up making more money not just because they create useful stuff that helps get work done faster or helps reduce boredom for people. They make more money because they are mostly passionate, logical problem thinkers, focused, hard working and better read on a variety of subjects than others. That’s your cue to how to make money even if you cannot code.


7) I would rather write much more code rather than write poetry. But I sometimes think they are related. Just manipulating words in different languages to manipulate output in different machines or people.


8) Kids should be taught software at early age , as that is a skill that helps in their education and thinking. More education for the kids!


9) Laying off talented software people because you found a cheaper , younger alternative half across the globe is sometimes evil. It is also inevitable. Learn more software as you grow older.


10) The best software is the one in your head. It was written by a better programmer too.