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
PV
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.
SM
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.
JS
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
TT
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.
KS
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.
JS
This course had a lot of practical, helpful guidelines for managing technology professionals in general, in addition to specific pointers for managing a data science team.
MG
Hi everyone ! It is my honor to appreciate what I learnt from this course. I love it,the university,and the instructors. It is a step forward for my carrier.
ST
good overview and introduction to manager as to what traits to lookout for and organising the team. Very practical approach and implementable without being over the top.
PW
Excellent course. All leadership in organizations that are data-driven or taking steps towards being data driven should enroll to this course. The quizzes could be better though.
RV
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.
FC
This class had hard quiz.Sometimes it seemed very basic, but some are hard unless you read and learn really well. Great class/knowledge. Practical real-life examples help.
RS
1 start less because of the ever-confusing quiz questions. Hit and trials make you go mad, and insance when you figure out the correct answer that you couldn't have imagined.
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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.
I've taken the first two courses in this area. I've noticed a data science issue that seems to be 'skirted around'....that is understanding the actual data and how it is created. I work in a health care organization. Our Epidemiologists are 'quasi-data scientists' however, their main strength is data analysis and presentation. We have one research database that is well documented and uses world wide standards....this doesn't cause any challenges. However, our clinical system is a transactional database that is used for managing patient appointments, treatments, and their electronic chart. There are two challenges....first, the epidemiologists have very little understanding of the process and business rules that are used for entering data and they have a reluctance to dig out that information. This is a big issue for them because when they approach data engineers to provide them with data they don't understand the 'business' issues associated with that data and therefore there requests are often not meaningful. An intermediary of some sort is needed to help the epidemiologists understand what they are asking for and what problems they will encounter with the data. The second, somewhat lesser problem is that the the clinical management system database is in no way optomized for data extraction. It's a transactional database with hundreds of tables and therefore is not directly usable by an epidemiologist. We have dedicated data engineers who extract data from this database. I think there is a gap in our organization between the Epidemiologists who are statisticians and the data engineers ...... this gap is my concern.
Horrible experience with one quiz where I lost hours just because it is completely misleading, tricky and bad formulated. Many people had the same experience from long time ago and they didn’t have the courtesy to fixed it.
was more generic rather than data science specific.
Brief Profile: Sohail Butt
I am a man of 58 years old and having an experience of almost 30 years of Business Management of Pharmaceutical & Nutraceutical Industries of Pakistan. Presently I am having my own Consulting Company " AIMMS CONSULTING" and extending my services as Management Consultant to different companies of said sectors.
I am of the conviction that learning is never ending and have a habit of learning new ideas about my favorite subject about Data Science. Although I have the limited usage of this subject in my working areas but I love to know about new areas of different specialties.
I really appreciate highly the efforts of respected instructor and enjoyed the course material and videos presentation of this course. Mind blowing approach was adopted especially in the basic components of Building A Data Science Team Data Analysis.
SUGGESTION:
MY PERSONAL HUMBLE REQUEST, Please make also the important components of course material as a part of this Certificate with % AGGREGATE so that it has a much more worth & impact for the courses participated.
A separate Transcript must be issued with having Aggregate % Score and important Components of participated course.
Thanks & best wishes to all Coursera Team.
This class was a great introduction to the topic of building a data science team.
The material was practically oriented, cited a lot of relevant external sources (e.g., news articles about data science), and was easy to understand.
The production quality was excellent - easy to listen to, ready to follow the slides, and the questions immediately followed each short segment.
I would have preferred it if the Professor had gone through different examples of data scientist and data engineering resumes to show me what performers in each quintile look like and what signals distinguish members of each quintile from the higher or lower quintile. Apart from the signals, and interview questions used to elicit these signals, how would the ability levels be different.
Well organized body of knowledge on how to build efficient data science team, from 0 to 60. Addresses a number of challenges including how to build an inter-disciplinary team, what to expect from practitioners from different disciplines, how to recognize right candidates, how to assign the right mix of practitioners to right business problems, how to cross-polinate the knowledge, how to keep feedback loops at professional level as it's fraught with potential for conflicting opinions, when to stop analysis, how to empower the team to initiate next iterations with business, common pitfalls and more. Worth every $ and all the time you'll spend on it.
This is a great course for helping you decide where you fit in the data science domain. The content is very clear, informative and have definite suggestions and conclusions. Unlike most of the information available on this topic, which mostly says that you need to be a unicorn to be in this space, this course talks sense, divides scope, suggests achievable targets and you can plan and assess rationally.
Also Jeff is a classic teacher,it's always rewarding to listen to him whatever be the topic.
Good content.
It explains very simple the things you will require if your are planning to create a team of data analytics.Like understaing the roles of analyst, engineer and manager, consider important factors as empowerment and control expectations of what data anlysis can achieve.
Just consider increasing the time for reading sessions, they usually take more than the 10min said, and having the more realistic time, help us to plann our study sessions.
Great course
great for existence human and android based life-form simulation internal lifestyle...
The course improves life within the simulation 10 fold at least(when combined with the other specialization courses) ...
Perfect learning tool for those who have worked professionally in research field sin our simulation and yet now have a touch of "the turrings" or you know : CBI...
Special thanks to the designers of the course.
Top scores for coursera.org &John Hopkins ...
My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career.
Please, take a look at my LinkedIn profile: https://www.linkedin.com/in/joseantonio11
I did this course to get new knowledge about Big Data and better understand the technology and your practical applications.
The course was excellent and the classes well taught by teachers.
Congratulations to Coursera team and Instructors.
Regards
Very informative and easy to digest. I am the Manager of a Data Science team Manager, so I'm a bit removed from the team and this course is helping me appreciate and understand more about what goes on within the team and how we as an organisation can get more from them and they from the organisation. Very good lectures, with transcripts and all well paced in small, easy to swallow chunks. Very good.
I really like the way this course was divided into manageable sections. It is really engaging and fun (and for those of you wondering, no, it's not boring at all). The course offers all the basics of how to build a data science team. I look forward to more courses like this from JHU and I would love to complete more courses and specialization courses from JHU. Kudos and Thank you!
What a great overview of a data science team and what it takes to build one. As a graduate with a MBA, concentration in DSBA, it took this course to really understand the makeup of a data science team and how to build one. I plan on meshing my understanding of how a business works with my newfound understanding of DS teams!
This is an excellent course for those who want to conceptualize a Data Science role or team for their organizations. The course not only explains the roles played by a data scientist, data engineer and data science manager, but also cover the intricacies working with the data science team. Their interaction with othe
As usual the team does a great job of distilling complex ideas without sacrificing the quality of information. Data Science is a complicated field. Don't be fooled by the topics, the walk before you run approach is super productive for your learning curve if you're not native to this field.
Its an exceptional course. A must pursue course for every manager either as new learning or refresher of knowledge.
A great thanks to course trainers. Their teaching approach is very target oriented to put the concepts in student brain in simpler and efficient way.
Very useful course for data managers on how to build, empower, support teams and communicate with them.. Not the usual statistics or quants, but real life scenarios at work places beautifully brought out by someone who must have experienced them.
I really enjoyed learn the specific roles of each person in a data science team. By learning the role of each team member, it is a start in learning how to manage each individual role and the whole class came together in a cohesive way.
Great class!
Very interesting, because it deals with "soft skills" too and handling these seems increasingly important. It helps you understand how a data team works and how to interact with them and manage the environment best. I find this course very useful.