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Edward Kwartler

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University of St.Gallen
Harvard University Extension School

Course location

University of St.Gallen

Home university

Harvard University Extension School
ted
Ted Kwartler is the VP, Trusted AI at DataRobot. At DataRobot, Ted sets product strategy for explainable and ethical uses of data technology in the company’s application. Ted brings unique insights and experience utilizing data, natural language processing, business acumen and ethics to his current and previous positions at Liberty Mutual Insurance and Amazon. In addition to having 4 DataCamp courses, he teaches graduate courses at the Harvard Extension School and is the author of multiple textbooks including Text Mining in Practice with R, Sports Analytics in Practice with R and Applied Sport Business Analytics. Ted was appointed to a Congressionally mandated 2-year advisorship within the US Government’s Bureau of Economic Affairs called the “Advisory Committee for Data for Evidence Building” advocating for data-driven policies. Ted holds an MBA from the University of Notre Dame (USA) with a concentration in Marketing Analytics.

Courses taught by this instructor

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Next course

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Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Location

Next course

Text Mining

Text mining is the art and science of extracting insights from large amounts of natural language. The topics of Text Mining will help students add natural language processing techniques to their research, and data science toolset. As a technical course with some machine learning elements, limited exposure to programming, graduate level statistics and mathematical theory is needed but the vast majority of the course content will be focused on applying popular text mining methods. As a result, the target audience may also include qualitative researchers looking to add quantitative analysis to interviews, media and other language based field research as long as participants have some basic R background. If you stay engaged in the course and complete the suggested readings and code: Students will be able to think systematically about how information can be obtained from diverse natural language. Students will learn how to implement a variety of popular text mining algorithms in R (a free and open-source software) to identify insights, extract information and measure emotional content.
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M

2024

Text Mining

Text mining is the art and science of extracting insights from large amounts of natural language. The topics of Text Mining will help students add natural language processing techniques to their research, and data science toolset. As a technical course with some machine learning elements, limited exposure to programming, graduate level statistics and mathematical theory is needed but the vast majority of the course content will be focused on applying popular text mining methods. As a result, the target audience may also include qualitative researchers looking to add quantitative analysis to interviews, media and other language based field research as long as participants have some basic R background. If you stay engaged in the course and complete the suggested readings and code: Students will be able to think systematically about how information can be obtained from diverse natural language. Students will learn how to implement a variety of popular text mining algorithms in R (a free and open-source software) to identify insights, extract information and measure emotional content.
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