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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

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Text Mining | Introduction to Natural Language Processing (NLP)

The Introduction to Natural Language Processing (NLP) course allows students to explore the vast expanse of textual data for research purposes. Shaped exclusively for GSERM students, the course equips participants with robust exploratory and analytical NLP methods including large language models (LLMs), prompt engineering, and vector database basics. Other NLP topics include bag-of-words analysis, sentiment analysis, document classification and clustering. Overall, the course provides a perfect springboard for qualitative researchers intending to augment their work with text data-driven conclusions. Prior exposure to programming, graduate-level statistics, and mathematical theory is advantageous, though not strictly required. The prime focus is to render the course accessible to researchers eager to enrich their examination with NLP analysis tools.
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M

2024

Text Mining | Introduction to Natural Language Processing (NLP)

The Introduction to Natural Language Processing (NLP) course allows students to explore the vast expanse of textual data for research purposes. Shaped exclusively for GSERM students, the course equips participants with robust exploratory and analytical NLP methods including large language models (LLMs), prompt engineering, and vector database basics. Other NLP topics include bag-of-words analysis, sentiment analysis, document classification and clustering. Overall, the course provides a perfect springboard for qualitative researchers intending to augment their work with text data-driven conclusions. Prior exposure to programming, graduate-level statistics, and mathematical theory is advantageous, though not strictly required. The prime focus is to render the course accessible to researchers eager to enrich their examination with NLP analysis tools.
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