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
Building a Data Science Team
-Welcome to Building a Data Science Team! This course is one module, intended to be taken in one week. the course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials and every lecture has a 5 question quiz. You need to get 4 out of 5 or better on the quiz to pass. Overall the quizzes are worth 17% of your grade each, with the exception of the last quiz, which is worth 15%. I'm excited to have you in the class and look forward to your contributions to the learning community. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. Be sure to introduce yourself to everyone in the Meet and Greet forum.If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center.Good luck as you get started, and I hope you enjoy the course! -Jeff
Gregory completed this course and found the course difficulty to be very easy.
Building a Data Science Team is the second course in “Executive Data Science” specialization offered by John Hopkins University on Coursera. It is a one-week course that defines the different data science roles in an organization, what to look for in...
Building a Data Science Team is the second course in “Executive Data Science” specialization offered by John Hopkins University on Coursera. It is a one-week course that defines the different data science roles in an organization, what to look for in data scientists and strategies for managing and communicating with data scientists. The course has no prerequisites and grading is based on a handful of multiple-choice quizzes.
The content in Building a Data Science Team is similar to the first course in the specialization: it is geared toward a non-technical people who have to manage data scientists. The video quality is good and the instructor is personable, easy to understand and knowledgeable. There’s not too much to dislike about this course apart from its brevity. All of the courses in the Executive Data Science track are only a week long, so they can be completed in one or two learning sessions. This is not necessarily a bad thing: I find it refreshing to get a high-level overview of a topic in a short course, but it may not deliver the amount of content that paying students expect.
Building a Data Science Team is a good course for what it is: a succinct primer how to assemble and manage a data science team.
I give this course 4 out of 5 stars: Very Good.
Amir completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
It gives you structure and some keywords to start and go for it. there is much more thing to know and do practically in process.