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Building Data Science Teams
Imagine cooking a stew with a single ingredient or growing a country garden with a single type of flower. One-dimensional efforts like these yield bland and boring results. Now imagine staffing a data science team with only PhDs in machine learning. In spite of the impressive pedigree, the result wo...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc.,
2015.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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| 520 | |a Imagine cooking a stew with a single ingredient or growing a country garden with a single type of flower. One-dimensional efforts like these yield bland and boring results. Now imagine staffing a data science team with only PhDs in machine learning. In spite of the impressive pedigree, the result would be similar: bland, boring, and, possibly worse, ineffective. But if not just data people, then who? Data scientist Paco Nathan answers that question and more in this video on how to build a data science team. Cited in 2015 as one of the "Top 30 People in Big Data and Analytics" by Innovation Enterprise, Nathan offers insider tips gleaned from his 30+ years in technology. Assess the need for a data science team: Advantages, disadvantages, and how big should it be? Identify internal corporate sponsors to get buy-in for the data science approach Manage the transition of the data science team into the organization Discover how to identify and hire the right people for the role Learn best practices for setting up, organizing, and managing the team Practice cultivating the team and their professional growth Perform team gap analysis and workflow analysis Absorb invaluable Dos and Don'ts | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 542 | |f Copyright © O'Reilly Media, Inc. | ||
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| 588 | |a Online resource; Title from title screen (viewed November 16, 2015) | ||
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