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Class exercise - collecting data - individual. Peter Fox and Joanne Luciano Data Science – ITEC/CSCI/ERTH-6961-01 Week 4, September 21, 2010. Admin info (keep/ print this slide). Class: ITEC/CSCI/ERTH-6961-01 Hours: 9am-11:50am Tuesday Location: CII/LOW 3112 Instructor: Peter Fox
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Class exercise - collecting data - individual Peter Fox and Joanne Luciano Data Science – ITEC/CSCI/ERTH-6961-01 Week 4, September 21, 2010
Admin info (keep/ print this slide) • Class: ITEC/CSCI/ERTH-6961-01 • Hours: 9am-11:50am Tuesday • Location: CII/LOW 3112 • Instructor: Peter Fox • Instructor contact: pfox@cs.rpi.edu , 518.276.4862 • Contact hours: Tuesdays 2:30-3:30pm (or by appt) • Contact location: Winslow 2120 (or JRSC 1C22) • Wiki: http://tw.rpi.edu/wiki/Data_Science_%282010_Fall%29 • Schedule, lectures, syllabus, reading, assignments, etc.
Modes of collecting data, information • Observation • Measurement • Generation • Driven by • Questions • Research idea • Exploration
Management • Creation of logical collections • Physical data handling • Interoperability support • Security support • Data ownership • Metadata collection, management and access. • Persistence • Knowledge and information discovery • Data dissemination and publication
Practical details for this week • The preparation for collection was Assignment 1 (due today) • This week is the practical part of data collecting – scope your effort so that you can conduct it within class hours (or less) • Ground rules • Come with two data collection options, others will be available/ offered • No one off collections, i.e. must be something you could repeat • This is an individual exercise
Practical details for this week (ctd) • A write up will be required, details in Assignment 2 (available today) and presented in weeks 5 and 6 (i.e. keep detailed notes) • No analysis is required • What are you planning to do?
Presenting your data • ~10 min each • Split over two weeks but all need to be prepared to present next week • 4-5 slides MAX • Present • The goal, and the mode of collection • How data was acquired • Physical and logical organization • Other ‘management’ aspects • Metadata and documentation collected/ stored • Some data in some form