This is a great course for anyone willing to start exploring the field of Data Science. It starts with basic definitions with proper examples that helps one understand this field with a greater ease!

This is really an introductory course, and there is not much to be learned, not a single line of programming or a single chart generated. But it can all be done in a single day, so it is a necessary evil to reach the good stuff in the specialization, I guess.

Utter waste of time

Great introduction to Data Science!

The content is good and easy to follow.

What I hate about this course the most is all test, quiz and examimation.

Most of their questions are not fair. They require to recite inconsequencial minor detail, such as who or which book said what.

I expect the test to recall about main concept, such as "What is different between AI, ML, and deep learning?", "What is properties of big data?", "what is application of regression". These kind of questions recall things much more important than minor detail I mention above, but they are non existent.

This happen possibly because the questions emphasized too much on module articles, which is full with detail, rather than clip videos, which present important concepts.

I hope you to revise examination questions to be more appropriate. I feel frustrate when doing them because asking minor detail feel like you are cheating upon students.

Most of the transcripts of the videos were from old or different versions of the videoes. This fails the basic principle one of the core of the five Vs: Veracity. None done.

There were countless errors in the IBM voiced-over, animation videos. For example saying that data mining is "automated" when it was just explained that data priming - which is often highly manual at the outset - is an important part of the first steps of data mining. It is absolutely NOT inherently an automated process from end to end.

The final "capstone" assignment which was essentially regurgitation was graded incorrectly. Especially with respect to the final reading. Students were asked to list the "main" sections of what should constitute a report to be given to stakeholders following data science based research. Firstly, dictating sections is stupid as you need to customise to your audience and NO, doing it that way should never be prescribed as universal. Secondly, even adhering strictly to what the reading said and ONLY what the reading said, the grading criteria was WRONG. How on earth did you list Appendices, CLEARLY stated as OPTIONAL as one of the 10 main sections? Not only that, you listed sub-sections as whole sections. For students that got the answer correct, I graded them as such and commented that I'm doing this because the criteria was in fact erroneous.

It's one MOOC. How hard is it to get the basics right? What happened to the IBM culture that used to make software engineers write all their code without a compiler to MAKE SURE what they were building was as correct as possible before compiling because of a focus on quality?

Amateur hour over here. Not inspiring.

Was pretty upset that the answers on the final assignment were incorrect according to the course materials. I am an OCD person that is very by the book, who studies and seeks my answers directly from the materials. I am hear to learn and depend on you to have accurate learning materials and tests that follow the course materials.

I create procedure manuals for staff. One of the first things I do when I finish a new manual, is go through each thing, step by step, to make sure it is accurate. My manuals are for a handful of people, your learning materials are for thousands of people, many of which have language barriers, as English is not their first language. So this makes it even more important for your assignments/tests to be extremely clear in their questions and the answers correct according to the course material. When you have a tremendous amount of complaints about this on the discussion forums and no one of power is doing anything to correct this, this is a major issue.

I had started a specialization with Coursera a few months before and quit near the end of course two due to extreme frustration with these same issues. I love the idea of these specializations, and would love to take many of them. I hope and pray that the rest of this course will be a vast improvement over the last assignment in Course 1 week 3.

Honestly, I just expected too much from this course. It ended before I could even fully realise it had begun. Grading seemed to be less along the lines of "We want you to understand this" and more along the lines of "We want you to memorise a specific quote from a puzzlingly long video that you won't feel like watching throughout, and will follow up with a reading which is more or less a transcript of the video."

Take up the course if you've never come across the terms "data science" in your life. Otherwise, it's just time and cognitive effort down the drain. This course is basically clickbait that claims to need 3 weeks of your time, but can be completed in a single hour if you're a fast reader and have a long lunch break at work.

- Texts have poor quality so they are hard to read and the references are not available.

- No extra materials are available.

- The quiz are pointless: you can answer without understand the text or the videos. You just need to find the key words on the text, no need to comprehend it.

- The videos are very boring. They are sometimes contradictory. Some questions are not answered and others are answered over and over again.

Finally, I thought the course poorly structured, boring and with low quality material. I could find better material on the internet for free.

[Reviewing the entire IBM Data Science specialization but points are applicable for each course]

I signed up for the IBM Data Science specialization and I was genuinely excited to start it for some 4-5 weeks (I had a GCP exam coming up). I eventually started the specialization beginning of August `20 and started making my way though it and I was amazed … amazed of how much a pile of bullshit this specialization is. I made it though the first 4 courses and at the end of the SQL for data science I couldn’t take it anymore. Here’s why:

1. First and foremost, the entire specialization (all 4 courses I have taken at least) were full of typos and broken URLs which a lot of other students confirm as well. This does not speak professionalism to me but whatever, lets move on.

2. The in-video quizzes and following tests are simply ridiculous … you are expected to have memorized content word by word rather than understand thing for your own and be able to explain them. Some of the question were so far away from tech courses it is not even funny.

3. The final assignments are a total joke. We are asked to review each other which IMHO is a terrible idea since we are all just starting up. Nothing stops you from giving top marks to a bad assignments and vice versa.

4. We eventually got to the more techy part and even got code snippets and jupyter notebooks to look through but they were still bad. There was no proper order in which information was presented i.e. you would read python and seaborn code in the SQL course’s tasks even though python and matplotlib/seaborn are discussed in the following courses.

5. And my final and biggest problem with this whole specialization is that it all feel like an extended advertisement of this piece-of-dodo tech inbred excuse-of-a-software called IBM cloud. There are constants up-sells here and there how almighty IBM is and how great their cloud and IBM Watson Studio are … they are not. I had to spend 2+ hours fixing problems with jupyter notebooks and their cloud just to complete my assignments which both took me 30ish minutes. They mention open source and even though there are open source equivalents to jupyter they insist using IBM cloud. I kept having the feeling they are more focused on promoting IBM products than actually bringing quality content.

6. Now after finishing the SQL course there was a 1min survey which I gladly filled in basically letting them know their specialization if terrible and is doing more harm than good in my opinion. I even sent them a quick challenge because I do not think IBM maintains this course at all or even reads the reviews. You can see my challenge to IBM here: https://bit.ly/3geOyfb

I was very saddened by the quality of the specialization and the content and was wondering whether I should even try and finish the remaining courses but after reading some reviews on the remaining courses I figured out it was just more of the same. If you are in the same boat I would recommend the kaggle micro-courses which I will focus on starting next week.

In conclusion, I got this whole specialization for free via financial aid and I have to say even though I did not pay a dime I feel I need to be compensated by IBM and refunded real money for torturing myself with their courses.

Extremely basic introductory course. Unfortunately you don't learn much about actual data science methods. Quiz questions tend to require you to memorize word for word quotations of supplied text, as opposed to challenging you to think about concepts. I would recommend this course for someone completely new to the idea of data science, but not to people who already know a bit.

This was a great introduction to the field of data science. Having videos interspaced with readings made it easier to maintain focus. The speakers in the videos were super engaging and I liked the upfront warning that data science involves continuous learning, and a willingness to look up stuff and practice until you understand how to do new developments in field. As a researcher who writes reports for shareholders, I felt like students could have benefited from a warning that after you figure out 5 possible solutions to a problem, and detail them in your conclusion and recommendations section, few of the stakeholders will actually read or implement the recommendations. But, hey, at least you'll have fun doing the analysis.

Although the e-note format was great in theory, I found the traditional technique of writing stuff down while watching the videos and reading the material to be more useful, as I didn't need to be logged into the site to study my notes. It's great that both options are available and learners can use the option that best fits their learning style.

The course provides a good overview of data science in general. I particularly liked the definitions of a data scientist and data science. Mr. Haider's definitions are inclusive, broad and encouraging, He says one of the most important traits for a data scientist to possess is curiosity and that tools and techniques can be learnt.

The course also touches upon hot topic areas that people have heard of but most do not understand - i.e. Machine Learning, Neural Networks, Data Mining and Big Data. I have a much better idea what these terms mean now along with the tools of the trade. The course was quite short and concise. I found it the perfect pace for me. The quizzes matched the content and there was nothing extraneous.

I am looking forward to the other courses in the specialization. A quick glance has shown me that the difficulty level increases quite a lot in the other courses and I would definitely have to invest a lot more time in them. The start has been gentle and encouraging, thank you!

This course serves as an excellent primer for individuals embarking on a journey into data science. It effectively covers essential concepts such as data types, metadata, data repositories, and storage technologies. The lessons are well-structured and provide a comprehensive overview of the data ecosystem. Overall, Course 1 lays a solid groundwork for aspiring data scientists and adequately prepares them for more advanced topics in the field.

This course has very detailed information on data science and data scientists. The real-life examples and applications of data science presented by different data scientists are also amazing. Overall an excellent course for anyone who wants to venture into this amazing field.

After completing this course you can easily understand and define what is Data Science and clear your doubt about Data Science.I recommend this course to all beginners.

I have learnt about what the data science is and it's basic knowledge. I am glad I took the course. I will continue finishing the rest of the courses.

This is a very good introduction to what actually is data science! Lot of people gets really confused with the definitions and area!

Direct to the point. Increases one's passion to study Data Science by summarizing the main topics. Simple and brilliant

This course equips a person with all necessary knowledge required to get started in this field with confidence.

A very brief and complete introduction of Data Science from industry experts highly recommended course

It encompasses fine details to introduce data science and explore data scientists as a career.

Excelente curso, son indispensables las bases para comprender temas más específicos

Amazing course to a roadmap to data science

Great for introduction!