{"id":9036,"date":"2014-07-22T14:15:13","date_gmt":"2014-07-22T18:15:13","guid":{"rendered":"http:\/\/carleton.ca\/math\/?page_id=9036"},"modified":"2015-02-11T11:06:10","modified_gmt":"2015-02-11T16:06:10","slug":"mining-information-gold","status":"publish","type":"page","link":"https:\/\/carleton.ca\/math\/cqads\/workshops\/mining-information-gold\/","title":{"rendered":"Mining for Information Gold"},"content":{"rendered":"

Mining for Information Gold Data Science Concepts and Techniques<\/h2>\n\n\n\n\n\n
Date:<\/td>\nThursday,\u00a0October 23, 2014<\/td>\n<\/tr>\n
Time:<\/td>\n9:00 a.m. to 4:00 p.m.<\/td>\n<\/tr>\n
Place:<\/td>\nMacphail Seminar Room, Room 4351 Herzberg Laboratories<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Workshop II Description:<\/h4>\n

An introduction to the fundamental data science concepts involved in data mining, with a detailed discussion of 3 common analytical concepts – classification, clustering and associations. The application of these concepts will be illustrated through examples and case studies, along with a discussion of common challenges and pitfalls. By the end of the course, participants will be able to appreciate the functionality of different types of data science analysis techniques and recognize opportunities for their application when presented with real world data.<\/p>\n

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

Individuals who wish to understand the functionality and capabilities offered by different data science methods<\/b>, even if they aren’t the ones implementing them (e.g. those working on projects with data scientists, or those having access to data and considering how best to use it).<\/p>\n

This workshop is also designed for individuals who want to increase their competency in data science<\/b> by increasing their knowledge of data science techniques, their background, functionality and, at a conceptual level, implementation.<\/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:<\/h4>\n

Morning:<\/p>\n