Introduction to Predictive Analytics
Today's leading businesses, governments, and other organizations collect large amounts of data about their operations and their customers. Classic examples are data about Amazon customers, call center data, or Twitter data. All that information is collected and then analyzed in order to predict future needs of individual customers, make sensible purchase suggestions to them, or try to market products and services directly to those customers. Another example is analysis of Google data that can be used for many purposes, for instance, to construct a model to predict outbreaks of infectious diseases based on sudden spikes in the frequency of certain words being googled.
Building such predictive models is part of Predictive Analytics, which is also associated with large amounts of unstructured data and a very broad range of practical applications. Companies move from traditional forms of predictive reporting to Predictive Analytics, so they can better react to changing environment. The companies want to be proactive rather than reactive.
In this two-day seminar you will learn basic skills in Predictive Analytics. You are going to harness the hidden power of Excel, and then use more sophisticated tools such as MINITAB® and R. Basic familiarity with Excel or other spreadsheet software is expected. 1.4 CEUs.
- How to use Excel to retrieve data from Web on a predetermined schedule
- How to make business decisions based on data
- How to optimize operations in a call center
- How to analyze text data such as a large number of product reviews
- How to exploit association rules between individual customer preferences and product prevalence on the market
- How to evaluate various marketing strategies
You will benefit by:
Collecting your own data from Websites Learning about software tools for Predictive Analytics. Learning about your customer base Using Predictive Analytics as an improvement on traditional reporting
Who should attend:
Managers, supervisors and other professionals who want to understanding and use Predictive Analytics. No prior experience with Minitab® or R is required. Basic familiarity with Excel or other spreadsheet software is expected.
Thursday, March 26 - Friday, March 27 8:30 am - 4:30 pm
$600.00 per person. Cost includes continental breakfast and lunch each day
The RIT campus at the Center for Quality & Applied Statistics located in the Hugh L. Carey Building (building 14)
Director of Business Development