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| Author: |
Ralf Klinkenberg |
| Subject: |
ANN: US Training Courses for Open Source Data Mining Software RapidMiner (October 2008) |
| Body: |
US Training Courses for Open Source Data Mining Software
RapidMiner (New York & San Francisco, October 2008)
Data Mining Training in New York, October 6-10th, 2008:
http://www.rapid-i.com/content/view/106/125/
Data Mining Training in San Francisco, October 20-24th, 2008:
http://www.rapid-i.com/content/view/105/121/
Open source software nowadays is a reliable and often more powerful
alternative to closed source software. This is especially true in the
case of software for data mining, text mining, web mining, predictive
data analysis, and business intelligence (BI). Open source based
solutions in this field provide powerful functionality for a much
lower price than proprietary alternatives. As a consequence, freely
available data mining and business intelligence solutions like
RapidMiner [ http://www.RapidMiner.com/ ] are now among the most
widely used solutions world-wide. According to the 2008 Data Mining
Tool Poll by the leading data mining web portal KDnuggets, RapidMiner
is the leading open source data mining software. Rapid-I [
http://www.rapid-i.com/ ], the company providing the open source data
mining software RapidMiner, is a provider of predictive analytics,
data mining, text mining, web mining, and sentiment analysis solutions
and services and now offers the following one-day data mining training
courses in New York and San Francisco on data mining in general, on
data mining for customer relationship management, sales, and
marketing, on advanced data mining methods, on data mining for time
series predictions, on data mining for financial forecasting, on text
mining, on web mining and sentiment analysis, and on data mining for
developers:
(1) Data Mining and Predictive Analytics: Methods and
Applications (New York, October 6th, 2008):
Compact introduction into the foundations of data
mining including both background knowledge as well as
many practical exercises. Topics include methods like
Decision Trees, Rule Learning, and Neural Networks as
well as basic pre-processing techniques and a
discussion of the most important explorative analysis
methods.
(2) Data Mining for Marketing and Sales Optimization
(New York, October 7th, 2008):
Using data mining to optimize marketing and sales.
Customer data analysis leads to models describing the
behaviour of your customers to better target your
marketing activities. This course also describes
the practical steps necessary to create such models
with the software RapidMiner. Topics include up- and
cross-selling, market basket analysis, product
recommendations, personalization, and customer
relationship management (CRM).
(3) Advanced Data Mining Techniques and Processes for
Professionals (New York, October 8th, 2008):
Covers the automatic optimization of parameters, the
optimization of the process structure itself, extended
possibilities for guided feature selection and feature
construction, the collection of process statistics,
extended control of inputs and outputs, the definition
and usage of macros, loops in processes and other meta
operations.
(4) Data Mining for Predictive Time Series Analysis and
Forecasting (New York, October 9th, 2008):
Compact introduction into the foundations of
statistical learning for forecasting and prediction.
The task is to find the most probable value of a
series of measurements for future time points. Topics
include necessary pre-processing steps for numerical
data transformations, an introduction into statistical
regression methods, neural networks, and support
vector machines (SVM), and a discussion of validation
methods in order to measure the goodness of the
predictions. These methods are especially useful for
numerical predictions from series data as they often
occur in financial markets but also in production
settings and many other applications.
(5) Data Mining in Finance and Financial Forecasting
(New York, October 10th, 2008):
Demonstrates how data mining can be employed in
various tasks in the financial sector by banks,
investment funds, hedge funds, insurance companies,
other financial institutions and companies in the
finance sector as well as sophisticated private
investors and traders. Financial data often comes
in the form of time serieses, e.g. stock market
prices, commodity prices, utility prices, or currency
exchange rates observed over time. Data mining
techniques can be used to analyze financial time
series data, to find patterns, to detect anomalies
and outliers, to recognize situations of chance and
risk, to detect temporal changes in the correlation
patterns and structures, to predict future demand,
prices, and rates, to determine the most successful
indicators, and to optimally combine such indicators
to achieve strong predictive power.
(6) Data Mining and Predictive Analytics: Methods and
Applications (San Francisco, October 20th, 2008):
Compact introduction into the foundations of data
mining including both background knowledge as well as
many practical exercises. Topics include methods like
Decision Trees, Rule Learning, and Neural Networks as
well as basic pre-processing techniques and a
discussion of the most important explorative analysis
methods.
(7) Data Mining for Marketing and Sales Optimization
(San Francisco, October 21st, 2008):
Using data mining to optimize marketing and sales.
Customer data analysis leads to models describing the
behavior of your customers to better target your
marketing activities. This course also describes the
practical steps necessary to create such models with
the software RapidMiner. Topics include up- and
cross-selling, market basket analysis, product
recommendations, personalization, and customer
relationship management (CRM).
(8) Text Mining: Advanced Pre-Processing, Classification,
and Clustering Techniques for Automated Categorization,
Ranking, and Filtering of Text and Web Documents
(San Francisco, October 22nd, 2008):
Introduction into knowledge discovery from unstructured
data like text documents. It focuses on the necessary
pre-processing steps and the most successful methods
for automatic text classification (including Naive
Bayes and Support Vector Machines, SVM) and text
clustering. Many practical exercises for different
settings (for example e-mail spam detection, automated
e-mail routing, adaptive personal news filtering,
etc.) will enable the participants to transfer the
gained knowledge to own text mining problems.
(9) Web Mining: Analysing Web Usage, Extracting
Information from Web Sources, and Automatically
Analyzing Customer Sentiments from Web Blogs
(San Francisco, October 23rd, 2008):
Shows how you could know what your customers and
potential customers think about your products in a
very timely and affordable fashion from information
provided by them freely available in the web. This
course enables you to quickly build mash-ups to
extract and integrate information from various sources
on the web and to automatically crawl and categorize
web pages using the latest text mining technologies.
(10) Advanced Data Mining for Developers: Customizing and
Extending RapidMiner and Integrating RapidMiner into
Your Applications (San Francisco, October 24th, 2008):
Aims at software developers and analysts with
background knowledge in development. Gives a step-
by-step introduction showing how new methods and
operators can be integrated into the data mining
solution RapidMiner. The second part of this course
deals with the integration of RapidMiner into other
software products as a data mining engine. This
allows, for example, the application of learned
models with one simple click for non-analysts or the
addition of adaptive behavior to your products. All
necessary steps will be discussed at hand of a simple
but complete integration example.
Further information:
* Data Mining Training in New York:
http://www.rapid-i.com/content/view/106/125/
* Data Mining Training in San Francisco:
http://www.rapid-i.com/content/view/105/121/
About the Trainer of these Courses:
These training courses in New York and San Francisco will be given by
Ralf Klinkenberg, initiator of the open source project RapidMiner
(formerly YALE) and co-founder of Rapid-I, the company behind the
project. Ralf Klinkenberg has more than 15 years of experience in
data mining, text mining, web mining, sentiment analysis, machine
learning, and related fields and has consulted many companies on how
to best leverage these technologies for automating and/or optimizing
their business.
About RapidMiner:
RapidMiner is the leading open source data mining software [
http://www.RapidMiner.com/ ]. According to a poll of the most
important web portal for data mining and knowledge discovery,
KDnuggets.com, in May 2008 among 347 data mining experts, RapidMiner
is the most widely used open source data mining tool, the second most
frequently employed software for data analysis overall. RapidMiner
has thousands of users in more than 40 countries world-wide. During
the last three years, RapidMiner was downloaded more than 300,000
times. RapidMiner provides more than 500 different modules for
detecting patterns in data and for their 2D and 3D visualisation.
RapidMiner supports data import from common data formats of other data
mining tools, Excel sheets, SPSS files, all common databases, and
unstructured text documents like news texts, e-mail messages, web
pages, web blogs, PDF documents, as well as time series and audio
data.
About Rapid-I:
Rapid-I [ http://www.rapid-i.com/ ] is a provider of predictive
analytics, data mining, and text mining software, solutions, and
services offering its customers automatable intelligent data analysis
of large amounts of data and text including automatically generated
classification and forecasting systems. The open-source data mining
specialist Rapid-I enables other companies to use the latest
technology for intelligent data analysis and for the discovery of
still unused company knowledge from existing data. Rapid-I won the
highly rewarded Open Source Business Award 2008. Rapid-I has a broad
user and customer base in more than 40 countries world-wide including
top companies like Ford, Honda, Nokia, Miele, Philips, IBM, HP, Cisco,
Merrill Lynch, BNP Paribas, Bank of America, mobilkom austria, Akzo
Nobel, Aureus Pharma, PharmaDM, Cyprotex, Celera, Revere, LexisNexis,
and Mitre as well as many small and mid-sized companies. Rapid-I is
headquartered in Dortmund, Germany. Besides of its since 2001
continuously improved data mining software RapidMiner, Rapid-I offers
its customers a broad range of services including consulting,
training, professional support, software development, RapidMiner
customization and extensions as well as complete data analysis
services from a single source. Further information is available at
http://www.rapid-i.com/ .
Best regards,
Ralf Klinkenberg
http://www.ovum.com/news/euronews.asp?id=7137
Ovum Analyst Report: "German Rapid-I pushes open source
data mining forward" (London, UK, June 2008).
http://rapid-i.com/content/view/99/66/
Rapid-I wins the Open Source Business Award 2008,
the most highly rewarded European start-up prize.
http://rapid-i.com/content/view/65/74/
KDnuggets Polls 2007 and 2008: RapidMiner is among the
top 3 data mining tools worldwide and the leading open
source data mining software.
http://rapid-i.com/content/view/8/56/
Selected customers of Rapid-I and users of RapidMiner:
* Market Research: GfK AG, Schober Information Group,
AFO Marketing, maanto;
* Automobile: Ford, Honda;
* Production Industry: Schott AG, ThyssenKrupp Nirosta,
Salzgitter Mannesmann, AMS Engineering;
* Elektronics: Nokia, Philips, Miele;
* IT Sector: IBM, Cisco, HP, HP Labs, HRL Laboratories,
TNO, BBN Technologies, LexisNexis, Mitre,
General Dynamics Advanced Information Systems;
* Pharma, BioTech, Chemical Industry: Akzo Nobel,
Aureus Pharma, Celera, Cyprotex, Elexso, PharmaDM,
Revere, and Sanofi-Aventis, Europe's leading pharma
company;
* Telecom Sector: mobilkom austria AG, Austria's
leading mobile phone service provider;
* Utilities: E.ON Ruhrgas;
* Financial Sector and Insurance Companies:
* comdirect, a leading German online bank and broker,
* BNP Paribas, leading bank of France and Europe,
* Bank of America, second largest bank of the USA,
* Merrill Lynch, US investement bank,
* aiinvesting.com, US consultancy and hedge fund,
* NeuralMarketTrends.com, US consultancy and trader,
* Ineas, French insurance company,
* PentaSecurity, Chilenian insurance company,
* Allianz, Europe's leading insurance company.
http://rapid-i.com/content/view/7/95/
Current data mining and RapidMiner courses
(detailed course schedules by clicking on course titles)
----------------------------------------------------------
Ralf Klinkenberg, Managing Director, Rapid-I
WWW: http://www.rapid-i.com/
E-Mail: klinkenberg@rapid-i.com
Phone: +49-(0)231-425-786-90
Desk: +49-(0)231-425-786-92
Address: Rapid-I GmbH
Stockumer Strasse 475
44227 Dortmund
GERMANY
Managing Directors (Geschaftsfuhrer):
Ingo Mierswa & Ralf Klinkenberg
Value Added Tax Registration Number (VAT Reg.No.)
(Umsatzsteuer-Identifikationsnummer (USt.IdNr.)):
DE 257490380
Applicable Law (Anwendbares Recht):
German Law
Place of Jurisdiction (Sitz & Gerichtsstand):
Dortmund, Germany
Trade Register (Handelsregister):
HRB 20720, Amtsgericht Dortmund
----------------------------------------------------------
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| Topic: |
ANN: US Training Courses for Open Source Data Mining Software RapidMiner (October 2008) |
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*Message 1* |
Ralf Klinkenberg |
Fri, 19 Sep 2008, 8:15 pm |
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