返回到 Building a Data Science Team
Johns Hopkins University

Building a Data Science Team

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

状态:Organizational Leadership
状态:Data Science
课程小时

精选评论

PV

4.0评论日期:Jan 22, 2017

Good overview of forming Data science team. The challenges are typical of any SDLC project. However I was looking to glean some specific challenges between DSc team and product or marketing teams.

VW

4.0评论日期:Jan 30, 2017

Good overview of the roles and skills needed, but loses a little momentum when it gets into the team management side of things, which is covered in many other business courses.

RV

4.0评论日期:May 21, 2016

Good but should come after the Managing Data Analysis for understanding better what the people you hire are actually going to do. Preventing the "Dilbertification" of the data science manager.

TT

5.0评论日期:Nov 7, 2016

The mode of teaching was interesting and I hope you can do better in the area of providing answers to questions we missed after the course. Thanks for the opportunity given to me.

JS

5.0评论日期:Mar 11, 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

KS

4.0评论日期:Aug 13, 2023

Applied advice for the manager of the data science team. Much of the content will be familiar for those with a background in management or are common sense, but a useful reminder nevertheless.

BB

5.0评论日期:Mar 30, 2019

Well thought and constructed instructional materials. Easy to understand and more practical. I actually struggled with the quizzes unlike the First Course in this executive program.

JA

4.0评论日期:Apr 15, 2018

Excellent content - delivery could be improved somewhat but happy overall. Lots of practical advice relevant to the job and team dynamics. Read the supplementary material/links

TB

5.0评论日期:Mar 23, 2016

High level but with enough details and context to make sense of the manager/exec/director's role in data science or the enterprise employing data science.

SM

5.0评论日期:Jan 14, 2021

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.

DD

5.0评论日期:Jan 28, 2017

Great course, learned lots about building and leading a data science team; and also some tips on keeping teams motivated and ensuing communication and collaboration works well.

EB

5.0评论日期:Dec 22, 2024

Great overview of the responsibilities of a data science team manager, interaction with internal and external teams, and guidelines to keep a data science team motivated.

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显示:20/455

Balakrishnan.R
5.0
评论日期:Mar 31, 2019
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5.0
评论日期:May 29, 2020
Deleted Account
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评论日期:Jun 18, 2017
Anna Szypszak
5.0
评论日期:Nov 24, 2017