Very learning experience, I am a beginner in DS, but the instructors in this course simplified the contents that made me I could easily understand, tools and materials were very helpful to start with.
I throughly enjoyed the course and the fact that everything was explained thoroughly. I always enjoyed Dr. White's personal experience with Data Science as well as other Data Scientists point of view.
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1) For what it was, (predominantly opinion videos) you could probably stand to remove a couple of the videos from the two professors, (especially the second one) without changing the overall effectiveness of the course.
2) You might want to work with that second professor, (or just edit the appropriate video) to remove the clear disdain he expresses toward statisticians since a) they're at least just as much a part of data science as computer scientists and are thus due the same degree of respect, and b) some / several of the people taking this course, (such as me) come from a statistics background, and it's really not a good idea to require us to sit there and watch while this prof decides to backhand us all with his not only inappropriate, but, more importantly, inaccurate statements - really not a good way to help us feel welcome as part of the program.
3) The final question on the final exam was -horribly- rote and pedantic. I get that you're trying to find something that's easy to peer review, but really - one random person's opinion on how to format a paper? That was about as unimaginative / uninteresting as you could possibly get and only goes further into making data science seem like it's going to be an incredibly boring-to-other-than-totally-left-brained-control-freak humans. How about easing it up a bit on the precise content and let people answer more about the Voltaire discussion, (i.e. thinking about how you want to present the results even before writing the report)? How about having people talk about the first professor's discussion of what he did with the Toronto busses - going off on his own to acquire priorly-unrelated data to determine correlations? These are the kinds of things that are going to help someone become a good data scientist later in life, not whether they could list 10 italicized bullet points about how precisely one form of report, (i.e. a very formal, long form - often only used in its entirety in academia and generally inappropriate in other contexts).
By Aykut Ö•
What did I like about the course?
* The contents discribed in the videos were very interesting.
* The rating system is great because you also have to rate other people yourself. With this system you get to know the motiviation and background of other attendees.
* Also, setting a due date is a good decision, as this motivates one to get through the course.
What was not so good?
* Some of the questions were way to easy. Due to the fact that you will be certified at the end, I would like to compare the degree of difficulty with that of a university/collage. After all, we want to be reasonably prepared for the job market after this course. Some questions were even superfluous. In the video the speaker says something like "With Zepplin Notebooks you can run code in different languages". Immediately afterwards the video is paused and the question is "True or false? With Zepplin Notebooks you can run code in different languages"... I hope, since it is the first course, the difficulty will increase through out the course and we can get our hands dirty.
* I don't know if this is a bad point but three weeks for this amount of content is way to long. I think it depends on the attendee but you can finish this course easily in one day.
What else could be improved?
* In some situations, I wish the video player was a bit larger, because you'd like to read along with the script but still want to see everything and with such a small player, it's not possible.
* If you're working on a laptop and have 2 browser windows open side by side, the page is not responsive enough and the player will appear cut off. So therefore it is hart to watch the video in one window and directly try out the stuff in the other one.
By Michael F•
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.
By Imran S•
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.
By Samuel R•
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.
By Michael C•
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.
By Daniel H•
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.
By Andrii L•
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.
By François R B•
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.
By amal j•
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.
By Kristan K•
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.
By Tommi J•
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.
By Cédric M G•
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).
By Alex R•
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.