學生對 IBM 提供的 什么是数据科学？ 的評價和反饋

Data Science is a fascinating field, but I think this particular course has several problems.

Firstly, the quizzes and final exam test purely on memorization of words and phrases, but not on actually learning the material. The final assignment, which is written entirely by the student and peer graded, has grading rubrics that explicitly require the use of these words and phrases. I believe this is the biggest reason non-traditional coursework has, in being accepted by the business sector.

The presenters, though highly regarded, are much less than polished speakers. Teaching effectively requires communicating effectively. As students we (at least I), seek proven organizations and their people to learn from. so a speaker who does not have even rudimentary presentation skills is a big disappointment. Further, it impedes learning even more for those who do not speak American English as a first language.

In addition, the presenter problem is made worse by the inaccurate digitization of speech to text, that accompanies each video. Unfortunately, this is not the only course exhibiting this problem, the entire Google IT Support Specialist Certificate series is an example.

The solution is three-fold: create a concise script of what is to be said (and how the speaker will appear on video), rehearse the presentation thoroughly, and an accurate proof the text that accompanies each video.

Not going to say this course is bad. This course is good for an absolute beginner who doesn't have a clue on what Machine Learning is. However, don't expect to build predictive models from this course. They go through surface knowledge of Data Science (which is a ridiculously broad term so it makes sense to do so as a starting point). There's some Watson projects which is pretty cool, but Watson is like a software where you can implement Data Science. You're not building any new models as such through code here.

It all comes down to personal preference, if you want to learn about what Data Science is and how Watson works, this could be a good path for you to take. For me, my goal was to be able to understand the mathematics and thereby code in popular languages and frameworks, basically going into the micro level rather than having a general surface knowledge of everything. I learned alot about coding from Lynda's Python and Data Science Essentials. However, ML is a very broad umbrella, and I narrowed down my discipline to learning Deep Learning with Andrew Ng's Deep Learning specialization which in my opinion is an excellent course if your learning goal aligns with mine.

The course was OK, which is probably about the best you can shoot for on an "introduction to the subject" type of course. I do have one comment regarding the narrated videos (as opposed to the interview videos). The little animations of block graphics are snazzy. But do they contribute ANYTHING to the lesson? I would say no. The video should help the viewer parse the audio or put it in context or something. Popping up block figures of people in ties when the audio mentions executives is just video for the sake of video. In fact for me, having this constantly-changing screen with little bits moving around makes it harder to concentrate on the actual content, 100% of which is contained in the narration. So I ended up muting the audio and just reading the text. Perhaps the video helps certain types of learners, although it's hard to imagine how. Anyway, I just wanted to suggest that in cases where video has no helpful contribution, maybe consider making it a reading assignment.

Other than that, I want to thank you for your contribution to making learning more accessible to everyone.

While there were helpful elements raised in this course (the multi-disciplinary nature of data science), I found that there were a number of elements (recruiting data scientists for companies; what is the structure of a report) that weren't core to my understanding of the concepts. While I can appreciate the course is designed for a wide range of audiences, some of these concepts could probably have been rolled in to the other courses, or listed as 'optional' learning (e.g. recruitment for companies, how to write reports).

In particular, I didn't find it very helpful to be tested on a very rigid structure of a data science report. Ultimately, the elements of a report are entirely dependent on the audience, and those requirements shift across industries. I would have preferred the final assignment to ask me to recall my knowledge of what the principles of regression are (and why it's a cornerstone of statistical analysis), or the roles of the different programming tools across different types and scales of data.

Would have preferred the course include some walk throughs on from start to finish on how some actual data science projects were conducted. It would need to be at the basic/simple level for those of us who are not technical/mathematical. A combination of professional graphics and narration and actual data scientists comments would probably work well. The brief examples like the one about Netflix didn't provide much insight on how the process was done. The report description part might be a bit confusing based on the repeated listing of some report sections in student responses I reviewed that didn't conform to the strict text book answer. Not sure that that is the best final exam sort of question anyway since it doesn't measure how much someone understood the bulk of the material and in real life one would have a reference to follow for report preparation.

The video material is good, but I have two main issues with this course. First is lack of understandable structure, like you get video "What is Hadoop?", where lecturer talks about his experience in data science, which is interesting by itself, but he only briefly mentions Hadoop and don't explain it at all, so I should go google "Hadoop" by myself to make sense of it, because video "What is Hadoop?" didn't explain to me what is Hadoop. And next reading is randomly about regression or stuff. Second is quiz questions, which are mostly pretty lame like in my opinion "According to reading 1, what was the name of researcher who said "Data science is cool"?". I honestly think that the name of the researcher shouldn't be my main takeaway from this course and questions should be more substance-related rather than random details-related.

The course delivers as advertised. after completion, you will be informed on what data science entails, and what a data scientist is doing occupationally.

However, it seemed to me that the course mainly focusses on profit/efficiencies for companies and earnings for the person. Possible societal consequences are not discussed or mentioned. Also possible negative consequences and/or harmful injustices are not discussed. So in my mind the content is a bit skewed since in technology it is not always happy and glorious.

In some cases the test questions leave room for improvement.

On positive note, I feel that I have a more and complete overview of what data science is. What was new for me, for instance, is the importance of story telling and other "soft" skills. So I am grateful to have learned this.

Thank you

Francois

Too much "telling" not enough doing. I grudgingly appreciate that this module focused on orienting students towards the needs of industries, to the soft skills necessary for data science, and to the crucial aspect of being able to tell compelling stories to stakeholders. However, it was a bit of a chore to spend three weeks being told this, rather than having exercises. Example exercises could include:, summarizing a table of complex data in writing for two different target audiences, or choosing between different visualizations for different target audiences. These could be optional exercises as they might be subjective and hard to grade. Nonetheless, getting students to actually *do* some of the types of thinking and communication that are stressed in the videos and readings would have been welcome.

To be honest, this course felt like a recruitment tool rather than an actual subject matter that I can learn about. Most of the videos were focused on trying to convince me how awesome being a data scientist is and how special data scientists are for being curious and making lots of money, but after completing the course I don't think I can say I have a great understanding of the kind of day-to-day work a data scientist does or how data science can be applied in different fields. The readings were a lot more in depth in terms of actual content than the videos were, which made the course feel very disjointed. I'm a little skeptical now of continuing with the IBM certificate because I don't really want to spend several months on this and not really have learned any new skills.

This course is mainly focused on defining what data science is, so as such it is very high level. Of course before really getting into data science practical skills you need to know what data science is, so as such the course is useful even though it perhaps doesn't result in new practical skills. One negative point for me are the quizzes which instead of testing general knowledge often ask silly specific questions about what a specific persion has said in one of the videos - thus even a master data scientist could not necessarily answer some quiz questions correctly because they don't always test generally applicable data science knowledge but rather test whether or not you watched the videos where someone has made a particular comment.

Fair overview of data science, with positives and negatives detailed here :

+ Good sections on the skills required, careers and positive messages for the younger generation.

+ Good speakers

(-) Content is very simple and very much on the surface of the topic. That's the intent of such a course I guess, but I can easily imagine that many people will leave the course without a much better understanding of what data science is and what is the real difference between data science and simple research / analytics as they were done pre-2010. It's a continuum, but it would be worth explaining what are the unique features of data science (aside the large amounts of data and computational power).

Although this is probably a really good introductory course for the completely uninitiated, I found it to be a little too basic and slow-paced for someone that already has a vague idea what data science is about. As some other reviewers have also pointed out, some of the quiz and exam questions are truly awful. The questions frequently test your ability to remember mundane and inconsequential facts such as the name of an organisation that published a report in 2014, instead of assessing your true understanding of important data science concepts. I'm holding thumbs that the remaining courses in the IBM Data Science Professional Certificate are a little more substantial.

This is a good introduction to the underpinning principles of data science. PROS: I really enjoyed the "documentary-style" videos based on interviews with experts, along with the readings. CONS: The animated videos are a bit too fast paced and superficial. Also, some of the questions in the quizzes are outright confusing. They don't really assess whether you understood the content of the videos and readings, but rather whether you remembered some key sentences by heart. There are also several typos here and there in the material. Overall, it's a good value for the money nonetheless. I would give it 3.5 stars if only the system accepted half stars.

I enjoyed my time with this course though ultimately I was slightly disappointed with certain aspects. It serves its purpose well enough by introducing you to terms like data science, big data, or machine learning, and it informs you on what life will be like as a data scientist. Though, I felt that it included a fair bit of padded content, particularly a plethora of personal anecdotes. I understand some of this padding gives the course some personality, I just think it relied a little too heavily on it and wish there was more overall substance. That being said, it's an introduction course to what seems to be a promising program, so I'm not too upset.

Courses from 1 to 3 are just theory. I can't say if the theory and the hours you put in are useful or not during these courses. Plus the tools that they introduce seems like just promotional or sponsored content. It gets a little better in course 4, they introduce you to some useful libraries like numpy and pandas and tell us some things about them. I still believe that if you're not applying for the certificate, and just wanted to learn Data SCience skip past the first three courses. I didn't think I got much knowledge about them, I could've just googled what is Data Science and read an article and that's it! So just giving three stars for that.

The content of the course is a great introduction to Data Science. I could have done without the peer reviewed assignment at the end. If there are not many people taking the course, it takes a while to have it graded. Also, I had to take all of my quizzes over because they all disappeared. I've had a ticket into Coursera for a while, and got tired of waiting so I just took the quizzes again. Not a big deal in this case because they are short and easy. I know it is outside the scope of this course, but I hope Coursera does not have this happen on other courses. Too bad this course had to take a hit in rating because of this grading fiasco.

A good introductory course if you have no familiarity with "data science" as a concept or as an Industry. The course largely uses interviews with industry experts to explain what data science is, has the potential to do, and set realistic expectations about a pathway to being a data scientist.

If you have an existing familiarity with data science, or the underlying principles, this course will some to be introduction heavy.

What separates this course from 5 stars? First, several points of the videos spend time waxing prophetic on tangential topics. Second, as of this writing (29 Aug 2019) the final exam has question/answer mismatch.

This is a really good introductory course about what Data Science is, and what it entails. I honestly thought it was something totally different, so I did learn quite a bit. The videos were well done and informational, and they gave great ideas and examples.

The reason for 3 stars is that each video is between 2 and 5-ish minutes - which is fine if you're on the run. If, however, you are watching them all at once, it is a big time waster to watch the introduction on the videos over and over and over.....but I think that is the way Coursera is for all their classes.

Also, some of the questions were just a little "off".

Good as an introductory course. to help a learner identify his needs and the future career track. Pretty basic, the way it should be. However, the course holds back on math and programming skils that are an absolute necessity for a data scientist. You probably can do without either advanced math or programming, but not without both at once. The course says nothinf about it, so as not to scare the students away.

The IBM Watson service never registered my account despite various browsers and e-mail addresses used. Should I look forward to more surprises with other external resourses used in this course?

An "OK" introductory course but week 1 and 2 was too general in my opinion.

I also felt like the readings in week 1 and 2 had to be more scientific and less example/story oriented. I understand that it is a way to introduce someone in the field but I would suspect that somebody picking up this course means that the person is already somewhat interested in the field. Τhus, it becomes really boring and incredibly slow pretty quickly.

Week 3 was much better, since there were more definitions and ideas that were more firmly put and communicated.

An entire course of nothing but introduction. Gives a decent-enough overview of what data science is and why you should be enthusiastic about becoming a data scientist... but how many people who sign up for a data science course need that? Surely we don't need an entire short course that covers only that and nothing of actual data science substance.

The next course in the IBM Data Science professional certificate ("Tools for Data Science") is even worse, and convinced me to drop out of the path and look for better course sequences.

For better or worse a VERY basic introduction to data science.

Some of the reviews disparaged that there was such a basic introductory section. To me it was probably a good idea from Coursera's point of view even though I concede that I didn't really get much out of it. Some people may hear the buzz words and the salaries and dive in sight unseen so I don't begrudge it. There was a bit too much reliance on memorizing quotes from readings for the quizzes as opposed to demonstrating understanding.

May be good for people who are completely beginner in data science, but I am disappointed as it is very basic and neither give outline about the complete course i.e. what will a student learn if they complete all the 9 courses. Also, I am not so sure how this course will help anybody to take any step towards the applications of data science. Curiosity and communication goes down the drain when someone don't know how to code. I think this course probably does not help on long term.

The course is quite interesting and its delivered in an unconventional manner with many different people's views and experience considered making it easier to remain interested in learning more and more. However the material and guides haven't been updated making it hard to follow exercises aimed at helping you utilize data science tools. For that reason I wont be continuing with the rest of the courses in the Specialization. I'll look for something better.

Great videos however the test questions need a good review, they are not aligned to the course content. The IBM Watson course piece looks to be outdated and the instructions are not aligned to what I saw on my screen when logging in (I found it after a search). It is very IBM software heavy - as you would expect. A good heap of videos with a great very basic introduction to it all (I would say 4 stars) but let down by errors in questions on quiz