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

4.5
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Оценки: 3,031
Рецензии: 416

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.

PA
6 июля 2020 г.

Well Taught Course , loved the lecturer present the information,for you who want to improved your management skill in data science this course can provide the introduction ,REST ? read a book

Фильтр по:

251–275 из 409 отзывов о курсе Building a Data Science Team

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: GIacomo V

20 дек. 2015 г.

I really enjoyed this course and I have found a lot of similarities with issues and challenges that I face every day at work. This has been very useful to me bot as a way to get inspired on new ideas and techniques, and as a way to confirm what I am already doing.

However, there were few occasions where I found the quizzes not to be clear enough.

In some instances this was due to the fact that the question asked required some extra knowledge that couldn't possibly be achieved only by reading the course material or listening to the class. I was lucky I new the answers because of my personal experience but it seemed quite unfair in my opinion. Also lectures materials are very short and don;t provide any extra information.

In other cases, the answers, especially when there were multiple answers didn't seem to be clear enough and sometimes contradicting what I had listened in the class. I don't remember specific cases at the moment, however I have left feedbacks throughout the course. You should have my feedbacks where I mentioned specific questions that in my opinion were confusing.

Hope this helps,

Giacomo

автор: John H

29 сент. 2015 г.

Good coverage in a very fast intro to the subject. Definitely take away some things I am able to put into use in my work. I get the feeling this is quite a new course (compared with the very well established Data Science Specialization) and does not have the student interaction base yet. I hope this aspect develops - perhaps even with the TAs. For this level course student interaction could be even more valuable than the more hands on data science subjects. Hope this specialization takes off as well as the DSS did!

автор: Stellios S

17 сент. 2017 г.

Good course that puts various aspects of a Data Science manager day-to-day activities into perspective presented by a talented instructor. However, I feel that there are a couple of things that could fit into the course like the role of Data Curators in a team or the distinction between Analysts and Scientists (which, I know, is not always clear). The notions of KPIs and OKRs could also have been pointed out when it comes to setting goals and managing.

автор: Cyril B

22 февр. 2017 г.

A very interesting course focus on the true life with data science teams. This course is more about the day to day life and problem to manage a data science team and less about pure organization. The financial aspects are not covered by Jeff nor the complex problems of people organization. I have noticed that I was the sole person to post on the forum.

автор: Reinaldo B N

3 апр. 2016 г.

I have studied this course as part of the Executive Data Science Specialization. I think this set of four courses meet my objectives by providing a very nice overview on the key points of data science projects. They are good to give a flavor on data science and data science projects helping decide if you want to search for more in depth knowledge.

автор: Rong-Rong C

1 дек. 2017 г.

Informative overview of the various roles in a data science team. The course is well-paced with plenty of supplemental curriculum to add substance beyond hiring. This covers the qualitative qualities that define and differentiate the human interactions of a successful team from that of the technical.

автор: Ben W

10 окт. 2016 г.

A good, enjoyable course with some interesting additional reading. I think I would have preferred a little more detail/content to have given it a full five stars. That said...I am very happy to have taken and passed the course. Thank you all involved. Now on to 'Managing Data Analysis'!!

автор: Charissa B

3 нояб. 2020 г.

Really helpful for a manager of a data science team. Not overly technical for those without a data science background. Would certainly recommend to those in management positions who need to get up to speed on data science and managing teams involved with this discipline.

автор: Vidhyambika S

25 июля 2020 г.

A Very useful course and is recommended for leaders, entrepreneurs who plan to organise and manage a data science team in their company also recommended to all students who plan to become a data science manager or want to just know what makes a datascience team

автор: Miomir Z

30 июня 2017 г.

Data science part was very valuable to me while some basics of people management are just basic. Overall solid course that i would recommend. More advance/modern/popular tools such as coaching and shared leadership were missing to make it 5 stars.

Best,

Miomir

автор: Pascal N

18 нояб. 2015 г.

Ver good with clear and on point material. I would suggest to include real life stories of success and failure some managers had in building a data science team, and discuss about the mistakes or the strengths of their recruitment process.

автор: Olga W

4 авг. 2020 г.

Mostly relevant information and useful pointers for a quick introduction. Since I am already managing a data science team for over 2 years, I was looking for more in-depth insights and advice on solving a variety of problems.

автор: Matthias L

11 авг. 2017 г.

It was a great overview of the different roles and also the interfaces between people and different teams. Nice to see that culture and communication were seriously discussed, showing awareness for organisational theory!

автор: Carlos A H

15 июня 2019 г.

Excellent insight and guidance on essential practices in building a data science team. Area of improvement is emphasizing python more than R as python has become the preferred programming language in data science.

автор: Julien N

20 февр. 2018 г.

Nice explications of a data science team, its players and how they interact within their team and the other departments of their organisation.

The Capstone project is the perfect application of this class