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Отзывы учащихся о курсе Building a Data Science Team от партнера Университет Джонса Хопкинса

4.5
звезд
Оценки: 3,165
Рецензии: 436

О курсе

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...
Основные моменты
Applicable teachings
(рецензий: 79)
Brief, helpful lectures
(рецензий: 11)

Лучшие рецензии

NN
14 дек. 2017 г.

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.

SM
14 янв. 2021 г.

Very well organized. Might consider adding couple of additional speakers with with more executive and management level experience with organizations that successfully implemented Data Science.

Фильтр по:

376–400 из 429 отзывов о курсе Building a Data Science Team

автор: Gonzalo G A

1 дек. 2016 г.

Some of the videos are very basic concepts on how to lead a team (i.e.: Management strategies, common difficulties,...). It is already known things for a manager with some experience leading teams. This course could be shorter.

автор: Naim J

22 февр. 2019 г.

although important. The content is a bit primitive for professionals who have been in the corporate world for more than a decade. It only confirms that team work is essential in any discipline and any practice.

автор: Neil K

7 авг. 2018 г.

While a good introduction for those new to personnel management and leadership roles, this was pretty basic for someone like myself. Great lecturer, great points made, most all of them familiar to me. Thanks!

автор: Chip J

8 июня 2017 г.

Some good content. Main reason for not scoring hire is there seemed to be too much overlap with standard management practices not unique to data science teams, such as the on-boarding section.

автор: Christopher P

17 июня 2016 г.

This was a fine overview however I was hoping to drill down into more detail on some of the roles and responsibilites data teams can have. For example data architects, stewards etc.

автор: Vijay S K

14 июня 2019 г.

A lot of content is more or less generic, same as any software engineering team building. Specific scenarios should have been covered to make it data science team specific.

автор: Niels v G

8 февр. 2016 г.

Building a Data Science Team does indeed contain helpful recommendations for starting data science teammanagers/executives; for me it had a little too much of a Hu

автор: Marc P M

20 окт. 2017 г.

This course is mainly theoric.

The practice depends on opportunity to apply it on your work place.

Directly, this course doesn't provide any new verifiable skill.

автор: Katherine d T

20 апр. 2017 г.

It was a bit.... general. That could be a bias of mine, as I have done quite a bit of management-type work, so maybe it didn't feel that new or informative.

автор: Todd

17 февр. 2018 г.

Some helpful suggestions but too much content on general, common sense management principles. Should be more focused on specifics of data science.

автор: Bart v d G

3 дек. 2017 г.

A lot of open doors if you have management experience but provides some overview of what to take into account specifically for a data science team

автор: YIMIN Q

15 авг. 2017 г.

This class might not be designed for people who have had years of managerial experiences in a large organization.

автор: Jose C O B

17 июня 2016 г.

The information is useful, but I think the five courses in the specialization could be merged into a single one.

автор: Paul D

10 мая 2018 г.

Informative but many of the insights apply to good managers in general; not just for data science teams

автор: Daniel D

22 окт. 2018 г.

As having experience managing researcher the subject is not completely new, so it can seems long.

автор: pamandeep s g

10 нояб. 2015 г.

Good interesting material but the quizzes were badly designed and did not test concepts well.

автор: Peter L

25 июля 2018 г.

Added value is highly dependent of your experience with data analysis or data engeneering

автор: Grant C

29 сент. 2016 г.

There could be more work in the assignments in this course. The Quizzes are very simple.

автор: Shafeeq S

8 янв. 2019 г.

Good on understanding roles each has to play. but very lengthy to explain those.

автор: Chen S

27 сент. 2015 г.

Great idea and syllabus.

The videos are too short and missing some explanations.

автор: Narek V

6 февр. 2016 г.

Probably, I wouldn't have a course just about how to hire/build a data team.

автор: Thomas N

3 янв. 2017 г.

needed more depth, which still could have been achieved in a 1-week format

автор: Saurabh G

13 авг. 2019 г.

A little dense on theory. Also a bit dated for certain parts I would say.

автор: Ricardo O

5 янв. 2016 г.

The courses in the specialization are - in my opinion, excessively short.

автор: Gregory M S

12 сент. 2019 г.

Too simple. Would have preferred more rigor and more material