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

4.5
звезд
Оценки: 3,157
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:

251–275 из 429 отзывов о курсе Building a Data Science Team

автор: Venkata S

5 мар. 2021 г.

Good Course

автор: Deleted A

8 авг. 2016 г.

Good course

автор: 杨波

5 нояб. 2017 г.

Excellent!

автор: Humbert S

1 мая 2017 г.

Excellent!

автор: Mayi P

24 июля 2020 г.

Excelent!

автор: DR. S T C

19 июля 2020 г.

Excellent

автор: Bauyrzhan S

12 июня 2018 г.

Excellent

автор: Carlos M

12 авг. 2017 г.

Excelent!

автор: Ahmed T

24 апр. 2017 г.

Excellent

автор: Kuldeep S S

17 нояб. 2017 г.

awesome

автор: chenneng007

22 окт. 2017 г.

SO GOOD

автор: Pablo A L

8 февр. 2016 г.

Superb!

автор: Carl P R

30 авг. 2020 г.

Great!

автор: Nitin S M

29 июля 2019 г.

thanks

автор: Damon R

25 сент. 2016 г.

Thanks

автор: Lawrence U

7 нояб. 2020 г.

great

автор: david c

18 нояб. 2015 г.

david

автор: Payal A H

13 июня 2020 г.

Good

автор: Rachid R

16 дек. 2018 г.

Good

автор: Rajneesh T

25 нояб. 2019 г.

na

автор: Manas K K

31 дек. 2017 г.

P

автор: William K

27 дек. 2016 г.

G

автор: Anna

28 сент. 2015 г.

G

автор: Chris G

17 июня 2016 г.

This is a great course and a daring venture for what is really an art form, beyond it's scientific requirements. This part of the specialization needs a little refinement.

I posted this in the discussion forum.

· 7 days ago · Edited

First of all.....these guys running this data science department have their hands full. They are teaching live classes for students who have spent OODLES (lots) of money to attend this prestigious college . Johns Hopkins is about as good as it gets for a medical degree. Then they are doing experiments and other data science for the research division of Johns Hopkins which is also as good as it gets........THEN they are doing these MOOC courses on top of all their other responsibilities......Dr. Leek is a University of Washington Alumni, which is also top notch for Data Science.

The video lesson is flawed, there is no denying it. But I must say these teachers are very open to improvement in the course and your comments on what could be better done are received and acted upon, so I would include them in your thank you letter to the teachers.

ALSO I think these MOOC courses are best done by all members of the department contributing. Truly this field IS a team sport. I feel this course was good, but the videos need to be edited and scripted, so unnecessary language, which dilutes the core knowledge, that must be learned, is not diluted where questions are left in the students head about content when being tested. I learned long ago in a college calculus class that if your mark isn't perfect, it's OK, so long as you pass with a high score......even if it is the teachers fault. The course could use better video production with teleprompter scripting......maybe some AV students at Johns Hopkins could get on board. it will happen eventually I'm sure.

You want to take a course that is absolutely one of the best courses I've taken anywhere and truly the best online. Try the number one business course on Coursera:

GROW TO GREATNESS, either part 1 or 2, University of Virginia, Darden School Of Business...........A team created course with one helluva a teacher who is a business person, researcher and award-winning writer. I would recommend this course to ANY student and especially E-Teachers.

The problem with this course is that there is a lot of information that can be included but may not be absolutely necessary as a "core concept". Needless to say, the more technical skills any employee has, the more insight they will have into their teammate's skills, as well, as the overall mission of the data department and the business it serves. I'm more of a tech and infrastructure person, I'm not real passionate about coding. I find it tedious. The more I learn about it, the more I enjoy it, albeit, from a distance. I can't see myself creating great blocks of scripts, but the more I know about how they are created AND what rules the code in a project must abide by, the better my skills will be as a data center manager. So I'm trying to learn as much as possible about R, Python, and companion programs like ggvis for creating visualizations. I'd say visualizations are an essential skill for a data manager, since you have to present results and projects, questions, and answers to higher ups and other departments.

this link comes from the resource section of this course:

https://www.datacamp.com/courses/ggvis-data-visualization-r-tutorial

This link or URL is of much more value to me, than a flawed test question and a reduction in my 100 percent average in the specialization.

Without this lesson, in this course, I would not have this valuable resource.

Another great link, which has a great FREE print publication as well:

http://www.processor.com/ ...these people have been advising data center managers longer than just about anybody !

Verbally and in the transcript are some nebulous statements that point toward the main idea, that concept being: the more any employee, on any data science or technical team member IS, a "jack of all trades", the better. So that could have been included in some more general way on the quiz, because really that is pretty much a general rule, I've found, working in ANY capacity in the tech industry. I have done a great deal of audio editing, working at numerous radio stations, with Adobe Audition. With others like: Pro Tools, or any other really good quality AV digital editor the result is streamlined, near seamless, audio-video, or one or the other. You just learn how to read and edit wave forms of all kinds.

Years ago, in Dallas, Texas, attending Richland College. I learned a valuable lesson. I was taking a college level Calc-Trig math class being taught by the regular professor's WIFE. I don't know if the professor was sick, but this woman, who was teaching the class for the whole semester, frankly, was not qualified. I had always been considered an illiterate by my high school math teachers, a married couple who, frankly, were highly abnormal even on the geekiest scale. These people were acting like they were a world above most people in the class. Needless to say, I assumed, by their "adult" opinions, they were sent by God Himself, to educate me thru denigration.

I was amazed, how 10 years later, in College math how well I was doing. I was carrying a 100 percent average ! So midterm this faux professor declares, "I'll be prefiguring all the arithmetic to be easy, so you won't have to bring your calculators !"

SO I DIDN'T.......and of course the teacher's wife proclaims....."I didn't have time to make the arithmetic easy so you'd better use your calculators !" I literally had pages and pages of figuring in handwriting accompanying my 3 page test. The result was a C plus on the test. I angrily told the sub teacher "I did not bring a calculator to this test because you said it wouldn't be necessary, therefore I must be allowed to redo this test with a calculator !" She of course relented, "No that won't be possible...that's not a bad grade...." she continued, "what are you worried about ?"........

I was so peeved, I was going to drop the class. It was too late in the semester, and I was so disgusted with this woman's cavalier dismissal of my perfect grade that I just stopped going to class. The result was a failing final grade.

Who ultimately suffered from this dilemma ? That, albeit, unfairly was me.....who created this "academic" tragedy, by the aggravation of a deeply flawed situation. Once again, that would be me.

автор: Don R

9 мар. 2021 г.

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.