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For someone who has no clue on Data Science it's a great overview and it gives you a sense of the field in general. I have not acquired a deep knowledge on the field, not even on the basics, just a vague summary of what it has to do with. So I suppose the title is fulfilled, as a crash course. However there are some points where I was pretty disappointed. First of all, some of the lecturers always seemed to read a script, which did not help pass down the knowledge at all. They did not give me the sense of what they were talking about and they seemed lost at some points. I had to watch with subtitles (in english) to understand the meaning of their saying. There was a lot of terminology as well... When you give a crash course, it generally means that you address an audience that has little background on the matter. A brief introduction to the terms would be really helpful. In general I think it has good potential for a short course, should the feedback be taken in serious consideration. However thank you for the opportunity.

This course has helped me to figure out what it takes to become a Data Scientist, the tools that you might need, the core values of the project and the fundamentals of Data Science. To be honest, it's not easy to dive in into Data Science without proper knowledge or guidance but with this course, you will at least know what is needed to understand more about data science, the required tools to excel the task, the structure of a good data science project, the programming language you need to know and many more. I would recommend this course for beginner who has absolute zero knowledge about data science but wanted to explore and curious on what's data science is all about.

A high level introduction.

Potentially tying it with some business concepts may be would have made it a bit more useful. While there are some great examples for funded research projects in the medical field, it doesn't quite go into use of data science in gaining business results; One example would be defining success module, where I found myself making a decision tree myself to reflect what those three criteria would translate to in business outcome terms.

Good course still overall. It is free after all so I can understand how may be more in depth conversations could be reserved for later parts of the programs.

The course content was fine, but I faced some issues with the Quiz content. I don't know if it was browser issue or the website itself, but on my various attempts, the same correct answers were reported as incorrect, occasionally. I had a hard time completing the Data Scientist's Toolbox quiz - despite providing correct answer in the first attempt, it was reported as wrong, and so, I never chose it again. This way, it is frustrating as well as confusing, breaking my confidence, and making it hard for my brain to memorize and recall the correct concepts.

This course is a great introduction to the field of data science! The instructors offered some great insight into the underpinnings of data science as a profession without including a plethora of unnecessary detail. Unfortunately, the assessment quizzes were not great (hence the 4-star review)... Other than this, I thought the depth and length of the class was exactly what I needed to begin my pursuit into better understanding (and hopefully eventually working in) this field.

Very informative in a way that I could grasp as a newcomer to Data Science. I had to review a couple of videos to really understand the information, but that was both necessary and worthwhile for a new topic. Relevant examples were also very key especially in the "Defining Success" section, to understand real-world applications. This course is very relevant for my career progression, knowing the nuances and limitations of DS and how to apply in discussion with DS colleagues.

I found the course informational and well presented. Given this is a beginner course, I would recommend re-addressing the definition of linear regression (or use more simplistic terms) in the Software Engineering section. I would also recommend changing the back ground in the 4 Secrets video section as I found the code to be distracting. The code also could cause the beginner to feel that the course content is more difficult, when it is not particularly difficult.

Some very good concepts for newer folks is in place. A good understanding of supervised and unsupervised learning with examples is of help. The trade off between statistics and Data science was interesting to know. More emphasis on tools will allow us to gather a good view of the execution path. But a good insight on the existing tools and what they can do was helpful to know

Very quick and easy to complete. Doesn't go quite as in-depth as I was hoping, but it does say "crash course" right in the title so you can only expect so much. I will be taking more courses on this subject. Overall, even though the information wasn't very detailed, what I learned was very useful, and my research work at my job will improve as a result of taking this course.

Good fundamentals and big picture approach, the discussion of specific software tools would be useful to some but not of real interest to me. I do mechanical design and consume data that is developed analyzed and presented by others and this class allows me a deeper appreciation of the challenges of analyzing and presenting data from experiments on the hardware I design.

The course touches upon most important topics in Data Science but doesn't go deep into the topics:

- Covers most important important topics in Data Science on high level.

- Course works as a "refresher" to follow the course you should know the basics in statistics and modelling. Concepts like parsimonious model would be used with expectation that student already knows it.

Easy, relaxing overview of data science project management. As this is the first class of Executive data science specialization, I give it 4 stars because I believe (or should I say "hope") that someone who is planning to be executive data science manager should know what is statistics or machine learning, or what is the aim of exploratory data analysis.

Quite basic material... If you have some technical background you might fund this course not so useful.

However, I think that it does provide the right information for non-technical managers in a simple and comprehensive way.

Personally, I wish it was a bit longer and deeper to feel like I acquired more knowledge to take it to the next level independently.

Pretty thorough for an overview, and it touched upon most concepts that you'd need to approach Data Science in any meaningful capacity.

My only gripe is with the literal last quiz, wherein no questions were asked based upon the materials, but upon additional PDFs attached to the quiz itself.

You cannot link me 4 PDFs and then claim it's a 4 minute quiz.

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.

Great course. Lot of complicated detail segmented and described in a way that was easy to digest. Thoughts for improvement are with the first few segments. The lessons didn't start with the "why". Why machine learning is used or what problem it solved. I had to google that in order to understand before the course which started with "what" it was.

I liked the course, but the quizzes were annoying because there are few questions and they include multiple choice question, but because there are few questions and you need to get 80% correct, you aren't allowed to get one question missed. It would be nicer to have more questions so the 80% correct would allow you to miss one question at least.

The course is correct. I would like to go a little deeper in each and every aspect of the course. For example, they explain clearly the difference between statistical analysis and machine learning, although, a more detailed examples of when you only can use statistical analysis and when you only can use machine learning is missed.

I really enjoyed this course, it gives me lot of new, interesting knowledge about data science, But there are some mistake about good (clean) programming. Please do not use comments, the comments show strange code, do not write big function etc... Clean code book from Uncle Bob helps me a lot of to write clean, maintainable code.