ProGENitor : an application to guide your career
| dc.contributor.advisor | Aziz, Adnan | |
| dc.creator | Hauptli, Erich Jurg | en |
| dc.date.accessioned | 2015-01-20T18:43:24Z | en |
| dc.date.issued | 2014-12 | en |
| dc.date.submitted | December 2014 | en |
| dc.date.updated | 2015-01-20T18:43:24Z | en |
| dc.description | text | en |
| dc.description.abstract | This report introduces ProGENitor; a system to empower individuals with career advice based on vast amounts of data. Specifically, it develops a machine learning algorithm that shows users how to efficiently reached specific career goals based upon the histories of other users. A reference implementation of this algorithm is presented, along with experimental results that show that it provides quality actionable intelligence to users. | en |
| dc.description.department | Electrical and Computer Engineering | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.uri | http://hdl.handle.net/2152/28120 | en |
| dc.language.iso | en | en |
| dc.subject | Graph theory | en |
| dc.subject | Analytics | en |
| dc.subject | Big data | en |
| dc.title | ProGENitor : an application to guide your career | en |
| dc.type | Thesis | en |
| thesis.degree.department | Electrical and Computer Engineering | en |
| thesis.degree.discipline | Electrical and Computer Engineering | en |
| thesis.degree.grantor | The University of Texas at Austin | en |
| thesis.degree.level | Masters | en |
| thesis.degree.name | Master of Science in Engineering | en |