In October 2008, Rapid-I will provide training courses on data mining and several data mining applications in New York city as well as in San Francisco. Between October 6th, 2008 and October 10th, 2008, five different one-day courses 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, and on data mining for financial forecasting and other finance-related topics will be provided in New York city.

Special offer: Book four days and get one day free!

Compact introduction into the foundations of data mining. Due to a high number of practical exercises, the participants will be able to transfer the gained knowledge to own data mining problems and solve them quickly and easily. Topics incluide methods like Decision Trees, Rule Learning, and Neural Networks as well as basic preprocessing techniques and a discussion of the most important explorative analysis metdods.

Demonstrates how data mining can be employed to maximize the gain achieved through marketing. Customer data will be analyized and leads to models describing your customers which can be exploited to better target your marketing activities. This course also describes the practical steps which are 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).

Prerequisite: course on October 6th or equivalent knowledge

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. This course is perfect for people with basic data mining knowledge or people who already attended the first two one-day seminars in New York directly before.

Prerequisite: course on October 6th or equivalent knowledge

Compact introduction into the foundations of statistical learning for forecasting / predictions. The task is to find the most probable value of a series of measurements for future time points. The topics include necessary preprocessing 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 others.

Prerequisite: course on October 6th or equivalent knowledge

Demonstrates how data mining can be employed in various tasks in the financial sector by banks, investment funds, hedge funds, insurance companiess, and 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.

Prerequisite: courses on October 6th and October 9th or equivalent knowledge