I think the course is in general very well designed but two things bothered me: 1) The content seems to be of 2015 and I think in the fast advancing field of data science at least some updates would be important. E.g. some time is spent talking about projections about the years 2018 and 2020 and for me 2022 or at least 2020 projections about the 2020s would have been more relevant. 2) I think quite a few of the test questions ask about irrelevant facts and instead these questions should focus about data science concepts, methodologies, background etc. Examples for "bad" questions are: "Did BCG or E&Y publish report XY in 2014?" or "Did Larry Page or Serge Brian or XY say that data scientist is a sexy job.

I think that the teacher's answers in the 3rd question in the peer-reviewed assignment isn't entirely correct. First, many peers had an almost correct answer missing 1 component, but missing this 1 component cost them 3 points, which I think is too drastic. Second, it wasn't entirely clear which 10 components are considered to be the main ones from the course reading. The teacher's answer was missing executive summary and literature review. Many students had put these two as main components, which I think they are. I would recommend reviewing the 3rd question as well as the teacher's answer. I would also recommend that the peer reviewing would be done by 2 or 3 students, not 1 to even out bias.

The basic knowledge of Data Science has been introduced which were clear and easy to understand. However, most of the materials are interviews of different data sicence professionals, there were few videos of documented lectures. I think documented lectures are important to present some core concepts in a more academic way. Interviews are sometimes too casual, they are like open discussions, different person could have different feelings or understanding about Data Science. But, as a beginner of Data Science, I need to learn the foundations of Data Science in a more academic way. So I don't think it will be a good idea to use interviews video as the core course material.

It gave a good overview of what Data Science is as a discipline and who Data Scientist should be as a professional. It allows you to understand what Data Science is for a beginner before you consider investing heavily into this field. Please note that although this introductory course is great, rest of the courses in the IBM specialization are not good, especially if you are a beginner (like me) and have no programming and coding experience - you will not learn well because a lot of the content already assumes that you know coding, syntax and complex computer science terms. Read reviews carefully from other courses in this specialization before making any commitment.

I really enjoyed learning through Coursera for so many reasons. First, I really loved the way the course started off with an introduction to what data science is and what a data scientist does. This was done by interviews from multiple angles of data scientists and from different backgrounds too. It really made learning something completely foreign much easier. At times in the reading material there were a few misspelled words and sometimes the wordings could have been simplified. Other than that, I enjoyed this course a lot. The quizzes in the middle of the videos made it easier to remember what I just watched. Thank you for putting such a great course together!

It was a great introductory program! I learned about the beginnings of data science and the course helped me understand what kind of data science I want to pursue. The readings were great and concise. However, quizzes between sections could have been more material oriented rather than random specific details from the readings. Though I feel like I learned the material easily and there were many lessons to learn, I feel it would benefit the program to have (maybe optional) hands-on practice with some of the concepts glossed over in the course. Overall, the course was organized very well, highly enjoyable, and motivating.

It is a good course to remind one of what basic skills are necessary to better understand data science and pursue a specialization in the field. For example, the discussions prompted me to run through a handful of quick linear algebra refreshers on YouTube. However, I found myself ambivalent about all the chitchat and I was perplexed as to why there was so much emphasis on the structure of the final report. I am not certain that you really need all ten elements to communicate a hypothesis/problem, explain the applied methodology, and make succinct points about the results. Looking forward to the rest of the course.

After completing this course, I got a glimpse of terms like Data Science, Data Scientist etc. As the course describes they are all like a teaser for a complete picture.

Coming from a STEM background and with experience of working on millions of records daily, I feel the topics covered are really less. But seeing the title "What is Data Science?", one should not expect complete details on Data Science. If it was too deep, students with less STEM background might be scared to continue.

It was good to hear from professors, data scientists from different background on what Data Science and being data scientist is.

There is a lot of great information in this course. However, there were some technical issues that should be addressed. For instance, the VR exercise in IMB Watson cloud uses screen shots from an old version. I had to read a discussion post to figure out how to do it. Also, the in-video quizzes had some grammatical errors, and some of them were not timed appropriately (coming up right before the material they are referencing). Finally, it would be helpful if the readings were uploaded as a searchable PDF with live links to the references, so we can read them. Overall, solid intro course.

It was interesting to take a course that didn't dive straight into 'How to do Data Science' but which carefully explained what data science was, what skills/aptitudes you needed for this to become a career etc. Overall it was useful though some of the videos were repetitive and some of the questions on the quizzes were poorly thought through - either so vague that anyone could answer the questions, regardless of whether they had taken the course - or were not of any use - for example, a question asking which publisher had published a certain book. But overall, a useful introduction.

Good introduction into data science, particularly for those with little to no prior knowledge. There were some opinion based statements that somewhat surprisingly became part of assessment as if they were formal definitions - as if the course attempts to reinforce (instead of just acknowledge) the instructors' biases. It was useful that the videos have accompanying texts and occasional pop up quizzes but some of them need updating to better suit the newer videos. Apart from minor user experience issues, this was a good starting place for those wanting to explore data science.

The content was very good, but the assessments were subpar. Why does it matter if I remember who was quoted in a text as saying something about data science? How does that help me get a job as a data scientist? Too much of the graded work was based on insignificant parts of the readings rather than things that are helpful to know about data science. Plus the final assignment asked about 10 parts of a report which were never clearly defined in the reading (and actually there were 11 parts mentioned but somehow we were supposed to know which 10 they were asking about).

I found some information useful, and the sequence of modues is interesting. However I would invert the way the modules explain what is data science. I would place people giving their opinion in readings(instead of videos) and examples of applications of data science in readings in videos (which are much more interesting and kind of situate students on what data science is). The quiz is interesting, but I really didn´t like the question about the name of Google CFO. I think the overall target of this quiz have passed remembering names for tests a long time ago.

There were a few spots where the information in the labs did not match up with the actual site; I assume that they updated since the course was made. It was not hard to figure out. Additionally there were a number of videos that had problems with the transcription below. There were many that were about 3 lines ahead of the video and there were at least two that were for an entirely different video. I assume the videos were updated recently but the transcript was overlooked or something similar. Other than those minor troubles, I enjoyed the course.

Warning for those whose native language is NOT English:

These IBM Data Science courses are in DESPERATE need of review by a native English speaker. If English wasn't my first language, I can only imagine how much I would have struggled. To be fair, this particular course didn't contain anywhere near as many grammar, syntax, and audio transcription errors as some of the other courses in IBM's Data Science collection, but it is still pretty unbelievable that they expect us to pay money for courses that haven't been properly reviewed and proof-read.

Impressive contents and materials.

experienced facilitators from impressive backgrounds in data science and analytics.

I like the intermittent review questions, helps in maintaining focus.

Thank you for the introduction and access to the IBM Watson VR service, I love the hands on exercise, took some pictures from around the house and tested the VR, Impressive, and still growing. However, I feel IBM Watson VR can still improve in some areas of systemic biases, may be due to cultural gaps...

I will recommend this course for anyone interested.

I took this course as the first in the IBM Data Science certificate sequence. The content was good, but the certificate is billed as being for people new to data science and in much of this course the speakers suggest you should have a background in statistics and should have some programming experience to be a data scientist.

From what I can gather, I think the other courses in the certificate will prepare people even without those things in their background, but it did give me some concern about whether I'm mistaken about that.

Videos were thorough and concise, however I did not like that the transcript did not reflect everythign the speaker was saying in some parts and then in other parts had more articles (a, uh, the). To me this seems like a fix that can be completed during review. Additionally, it felt as though the rubric for the final assignment had more emphasis on specific words being added in a response than the concept or idea being realized throughout a students answer. Thanky you for your time and appreciate the introduction course.

The content is good but some of the topics are not explained but there were questions in the quiz like data mining. I found that most of the quizzes are testing memory instead of concepts. They are questions in quizzes related to the years when the article is published and all. There were some questions that literally shocked me like the answers should be "output" but has another option like "results", even though both are synonyms we should stick to the word that was mentioned in class, this is a complete memory test.

So far the experience has been worth every penny. The quality of content disseminated by lecturers, videos is awesome. There are instances where we a real person to guide us would make the process even more smooth and faster, viz., logging into IBM web, installing watson assisstant to name a few.

And i would really like to come in contact with a real mentor, a data scientist who is an industry professional who can give an inspirational insight to boost morale and motivate us, he/she can be a convenor of the program.

This Data Science Course hassupported my confidence building as a Business Analyst. I was able to take actionable knowledge from the level of data, collated by different individuals and works of life. I see Programing as a underused , new avenues are opning and generating new growth for Research and Development in the health, marketing and research sectors. Learning pace is encouraging and easy to follow. Being exposed to likely earnings' of a Data Analyst is also a game Changer for me.

Than you.

Olatunji O

Having absolutely no background in a tech field, I feel like this course gave an excellent description of what Data science is like.

There are some cons which I will list:

--> The quizzes mid video seemed pointless. The graded quizzes were even more so.

--> A lot of the videos made it seem that a prior knowledge of math and statistics were necessary, which may deter those hoping to make a career shift to tech.

--> The final assessment is peer reviewed. This can go either way depending on who your peer is.

Overall the course was enjoyable and provided good information from relevant sources familiar with the course topic.

I'm sure you here this a lot, but the peer-reviewed grading assignment is not a good approach and would recommend that this feature be removed. I know Coursera has good intentions with this and they are trying to promote positive interaction engage learners to communicate with one another, but individuals taking these courses are not interested in the forum's for the most part.

This was a good introduction to Data Science, and what a data scientist should expect to do. It also included a good description of what type of background might be required along with educational requirements for someone looking to get into Data Science. The only reason I didn't give it 5 stars is they tried to sell it a little too hard. I'm already taking the course and have demonstrated my interest. I don't need to be sold on why being a Data Scientist might be something great to be.

Self paced and easy without being boring. Interesting material. I felt the peer reviewed assignments and quiz portions promoted rote memorization vice actual understanding. All the ones i grades were clearly copy paste form the reading, yet by the grading rubric, they all scored perfect. The section requiring an account creation and use of the Watson AI application was a waste of time, and its rather annoying to have to create yet another account among the hundreds of others everyone has.