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Opiniones y comentarios de aprendices correspondientes a Building a Data Science Team por parte de Universidad Johns Hopkins

4.5
estrellas
2,610 calificaciones
349 revisiones

Acerca del Curso

Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organize them to give them the best chance to feel empowered and successful, and how to manage your team as it grows. This is a focused course designed to rapidly get you up to speed on the process of building and managing a data science team. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. The different roles in the data science team including data scientist and data engineer 2. How the data science team relates to other teams in an organization 3. What are the expected qualifications of different data science team members 4. Relevant questions for interviewing data scientists 5. How to manage the onboarding process for the team 6. How to guide data science teams to success 7. How to encourage and empower data science teams Commitment: 1 week of study, 4-6 hours Course cover image by JaredZammit. Creative Commons BY-SA. https://flic.kr/p/5vuWZz...
Aspectos destacados
Applicable teachings
(79 revisiones)
Brief, helpful lectures
(11 revisiones)

Principales revisiones

NN

Dec 15, 2017

This course was an exceptional experience where it introduces me to building a data science team, its challenges, nuances and also what kind of approach to take while building and sustaining the team.

JS

Mar 12, 2017

Extremely practical and essentially human, this was really interesting to better understand the different roles and how to help data science teams to work together, highly recommended

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326 - 342 de 342 revisiones para Building a Data Science Team

por Terry L

Oct 25, 2016

This material is important, but the assessment quizzes were tedious as the right answer often hung on some small obscure remark that was hard to recall.

por Polychronis ( P

Nov 13, 2018

Why wright a review? Since you do not present them publicly so it doesn't make sense to me to give any feedback. :-)

por Florian K

Aug 21, 2019

Some useful insights particular to managing a data science team, otherwise pretty Basic leadership stuff.

por Marcelo H G

Jul 13, 2017

Needs review and grow a bit the content. Improve concepts and include Operations and Project Management.

por Sarah S

Dec 30, 2019

Really heavy on the basics of management, vs the skills needed in individual roles.

por Cheng-Jiun M

Oct 12, 2015

information is useful. But, still, it is a shame to split one month course to 4.

por Scott W

Mar 14, 2018

Good material, but lectures were poorly organized. Quizzes were poorly done.

por Craig A B

May 17, 2019

It's a bit too general, and listy.

por Himanshu P

Nov 21, 2017

pointless course....

por David J

Nov 04, 2019

Should not be part of a course "Executive Data Science". very basic and no real insight into what makes a data science team different (if indeed it is).

por Luis H

Jun 22, 2018

No me gustó, dado que la información no es clara y no se puede avanzar de forma adecuada debido a que las evaluaciones son muy confusas.

por Karen D

Jun 22, 2017

Seemed like his personal ideas. I didn't see any indication the information was based on any research or empirical study.

por Deleted A

Nov 02, 2016

Rudimentary HR / new supervisor level information. Not specific to Data Science either.

por Ananth K

Mar 12, 2017

This seemed more appropriate for a startup, and most of the content was rudimentary

por Poon F

Jan 26, 2018

Course materials are not organized. Too much talk with too little substance.

por Sachin G

Jun 29, 2017

was more generic rather than data science specific.

por Seyyed M A D

Apr 19, 2018

Thanks