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Description
Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on their skills, professional social networks could make searching better with automated tagging, advertisers can target more carefully, and students can

Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on their skills, professional social networks could make searching better with automated tagging, advertisers can target more carefully, and students can better find a career path that fits their skillset. The aim in this project is to apply deep learning to the world of professional networking. Deep Learning is a type of machine learning that has recently been making breakthroughs in the analysis of complex datasets that previously were not of much use. Initially the goal was to apply deep learning to the skills-to-company relationship, but a lack of quality data required a change to the skills-to-industry relationship. To accomplish the new goal, a database of LinkedIn profiles that are part of various industries was gathered and processed. From this dataset a model was created to take a list of skills and output an industry that people with those skills work in. Such a model has value in the insights that it forms allowing candidates to: determine what industry fits a skillset, identify key skills for industries, and locate which industries possible candidates may best fit in. Various models were trained and tested on a skill to industry dataset. The model was able to learn similarities between industries, and predict the most likely industries for each profiles skillset.
ContributorsAndrew, Benjamin (Co-author) / Thiel, Alex (Co-author) / Sodemann, Angela (Thesis director) / Sebold, Brent (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This project, originally inspired by the photography blog Humans of New York, is a series of digitally drawn portraits and profiles of individuals in the downtown Phoenix area. Phoenix is a relatively young city and the city's character and style has not yet been cemented. But this city is just

This project, originally inspired by the photography blog Humans of New York, is a series of digitally drawn portraits and profiles of individuals in the downtown Phoenix area. Phoenix is a relatively young city and the city's character and style has not yet been cemented. But this city is just as lively and interesting as older, more established places and deserves the same kind of attention that people documenting their homes have given their subjects.

The profiles, which have been collected at https://rebeccaspiess.com/humans-of-phoenix-pg/, were created from subjects I met at coffee shops, art galleries, on study abroad trips and through personal research. The only criteria for inclusion in the project was their connection to Phoenix. Additionally, because of the digital nature of the portraits, I have included timelapse videos showing the process of creating each image on my YouTube channel, called Rebecca Spiess.

I want the “Humans of Phoenix” project to be like speed-dating the city, getting to know the stories and the people you might pass on the street. People love to get a glimpse into the lives of others. I love the thrill of meeting new people with great stories, and I want the readers of this project to get that satisfaction as well. And hopefully, I want these narratives to engage readers in a way that elicits empathy, understanding and excitement.
ContributorsSpiess, Rebecca Lea (Author) / Gilger, Kristin (Thesis director) / LaCroix, Kristin (Committee member) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05