Metis Enters Emerald City with Part-Time Intro to Data Science Course for Students
Interested in Learning Data Science Fundamentals
Note to editors: Kaplan is a subsidiary of Graham Holdings Company (NYSE: GHC)
Seattle, WA (October 10, 2016) – To help fill the growing need for data science skills in the Seattle area — one of the top three data science markets in the country — leading data science training provider Metis has announced the launch of its first program in the Emerald City, a part-time Introduction to Data Science course designed to help students learn how to deliver solutions using the data scientific approach to thinking about data-heavy problems.
Metis’ launch in Seattle follows a series of recent moves that include the provider’s expansions to San Francisco and Chicago, the acquisition of Booz Allen Hamilton’s Explore Data Science program, and the hiring of noted data scientist Dr. Deborah Berebichez.
The Introduction to Data Science course is for anyone with a basic understanding of data analysis techniques interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way. In Seattle, the course is taught by Trent Hauck, a data science consultant in the insurance and e-commerce industries who previously worked as a data scientist for Zulily. Upon completing the course, students will have:
- An understanding of problems solvable with data science and an ability to attack those problems from a statistical perspective.
- An understanding of when to use supervised and unsupervised statistical learning methods on labeled and unlabeled data-rich problems.
- The ability to create data analytical pipelines and applications in Python.
- Familiarity with the Python data science ecosystem and the various tools one can use to continue developing as a data scientist.
Classes begin November 2nd and run for six weeks on Mondays and Wednesdays from 6:30-9:30 PM at 83 South King Street. The early enrollment deadline is October 17th. Students should have some experience with Python and familiarity with basic statistical and linear algebraic concepts. No prior advanced mathematical training beyond an introductory statistics course is necessary.
With this new course, Metis joins established affiliate company Dev Bootcamp, which is also part of Kaplan’s New Economy Skills Training unit, in Seattle. In their 2016 rankings, GoodCall named Seattle the third-best large city in America for data science jobs.
“Seattle’s strong interest in data science learning, from introductory concepts to focused areas of specialty, made it an obvious choice as we look to bring best-in-class programs to wider geographies,” said Jason Moss, co-founder and president of Metis. “We’re excited to serve the demand for data science competencies from employers, professionals and job-seekers in tech hubs across the country. Our goal is to bring a holistic approach to data science education through bootcamps, part-time professional development courses, introductory resources, and corporate training.”
While the role of data scientist ranks as the best job in America on jobs website Glassdoor’s 2016 list, McKinsey and Company reports that 140,000-190,000 job postings for data scientists will go unfilled by 2018.
To speak with a Metis spokesperson about the expansion to Seattle or the new course, please contact Michael Tague at 212-974-2785 or email@example.com. For more information on all of Metis’ programs and offerings, visit www.thisismetis.com.
Metis (www.thisismetis.com) is a leading provider of data science skills training for individuals and businesses. Part of Kaplan’s New Economy Skills Training (NEST) unit, Metis delivers training programs designed by world-class industry practitioners and runs the industry’s only accredited intensive data science bootcamp. Metis, a d/b/a of Kaplan, is accredited by the Accrediting Council for Continuing Education & Training (ACCET). Kaplan, Inc. (www.kaplan.com) is a leading provider of educational and career services for individuals, schools and businesses.
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