{"id":9044,"date":"2014-07-22T15:10:55","date_gmt":"2014-07-22T19:10:55","guid":{"rendered":"http:\/\/carleton.ca\/math\/?page_id=9044"},"modified":"2015-02-11T11:08:32","modified_gmt":"2015-02-11T16:08:32","slug":"hands-data-discovery","status":"publish","type":"page","link":"https:\/\/carleton.ca\/math\/cqads\/workshops\/hands-data-discovery\/","title":{"rendered":"Hands-on Data Discovery"},"content":{"rendered":"

Hands-on Data Discovery: Exploring Data with IBM SPSS Modeler and Other Platforms<\/h2>\n\n\n\n\n\n
Date:<\/td>\nThursday, October 30, 2014<\/td>\n<\/tr>\n
Time:<\/td>\n9:00 a.m. to 4:00 p.m.<\/td>\n<\/tr>\n
Place:<\/td>\nTBD<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Workshop III Description:<\/h4>\n

The goal of this workshop is to give participants some preliminary experience extracting patterns and knowledge<\/b> from real datasets using IBM SPSS Modeler and other platforms.<\/p>\n

This will increase participants\u2019 understanding of how data can provide insight into problems or increase decision-making power and the steps involved in carrying out a data science project.<\/p>\n

The workshop will be divided into two sessions. In the first session, a guided tour will show participants how to answer specific questions<\/b> using a typical dataset. In the second session, a backpacking \u201cadventure\u201d will let participants find their own patterns<\/b> in a mystery dataset.<\/p>\n

This workshop is meant to be a companion to the second workshop (Mining for Information Gold).<\/p>\n

Intended Audience:<\/strong><\/h4>\n

Individuals who have some conceptual understanding of data science<\/b>, and who would like to gain some hands-on experience<\/b> with the application of data science concepts, or individuals who are planning a data science project<\/b> and require a more detailed understanding of what that entails<\/b>.<\/p>\n

Pre-requisites:<\/strong><\/h4>\n

Participation in the first workshop (Analytics, Before and After) is not required, but familiarity with its contents may be useful. Some experience with data science techniques is assumed.<\/p>\n

Program:<\/strong><\/h4>\n

Morning:<\/p>\n