Executive Summary: Do not spend time on this course if you have minimal common sense and have read at least one article on Data Science.

I finished the 11 week course "Machine Learning - Stanford University - by Andrew Ng" 2 months ago, which was such a great course! It kept me busy for about 20 days (at about 4-6 hours per day) and I learned so much!

This course "A Crash Course in Data Science - Johns Hopkins University" , Part of a 5-course series, the Executive Data Science Specialization, provided just some superficial obvious information, took me only 3 hours to complete and even this time I would could wasted time.

Finally coursera now starts to make courses smaller and smaller and them adding multiple of them up to "Specialization", so that at the end you have to pay much more to get a certain amount of information and course time. With this course they seem to have streched it to a new extreme. The price for the certificate for this superficial short mini course has reached the same price ($ 44) as what I payed for the extensive great Machine Learning course from Stanford University by Andrew Ng !

A light overview about Data Science that can be really useful as a first time on this topic.

All the course content is too basic to bother with. It’s just a bunch of common sense and a few definitions of vocabulary words that can already be found on Wikipedia. There is typically one page of reading stating basic definitions and intuitive ideas followed by a 10-15 minute video reiterating those basic ideas. For example, there is an entire unit just to explain that a report on a data science experiment should be clearly written, avoid unnecessary detail, and have concise conclusions. There is a page of reading to list these as bullet points and a 14 minute video to repeat these points.

This course is a waste of time. Thankfully, it’s short and free.

Great to do if, you want to dive into data science or machine learning. It is a must do, if you have no idea of the rationale behind statistics and data science as well as the need for it.

A LITTLE TOO THEORETICAL. THE INSTRUCTORS SEEM CHALLENGED TO PRESENT REAL LIFE EXAMPLES. Quoting favorite examples from historical pure play ideas (e.g. predicting heights of boys) are frankly a bit dated, not relevant for a business audience, and hint at real life incompetence of instructors. And what is with trying to promote books these guys have written?

Great starting point for any experienced professional with no data science background to understand this growing in-demand field, its purpose, the various components that comprise data science, in addition to overall objectives and outcomes of data science. Allows you to evaluate how much further one would like to dive into the field respective of your personal or professional objectives.

This course is the 2nd certificate course I've completed through Johns Hopkins and Coursera, and the content is always excellent. I suggest pausing the videos and take notes to grasp everything, and for reference if you're going to take other courses along this path. I am also going to complete the other Data Science certification offered by JHU

I was looking for a data science course which will enhance my skill and knowledge. This course from Jhons Hopkins University just right to learn about Data Science. The course structure is excellent and to the point.Trainers are excellent in the subject and explanation equally good. Thanks coursera for having this course.

A decent overview on data science with interesting tips and advise on how to conduct your research and experiment. Gives an outline of your research progress and knowledge on whether you are on the right track. Plus, nice insights to look into and encouraging to learn more about the field.

This Course was ideal to provide an overview to People that are not experts in statistics and data. In the future I know now what Questions to ask if someone wants to Show off. Thank you also for good visualization and reading material during the Course.

This is a great starter course for data science. My learning assessment is usually how well I can teach it to someone else. I know I have a better understanding now, than I did when I started.

The course was very informative and knowledge gained from Data science expertise is exceptional. I thank Johns Hopkins University for offering the course for Data science beginners.Thank you.

Very nice way to get introduced to the world of data science. Thanks a lot!

Nice and well taught .There is really alot to learn

The only reason this course is two stars is because the content could be useful to a beginner in the field. The course itself, however, is of poor quality with un-engaging video content and an unedited book with multiple sections that are clearly derived verbatim from the sub-par video lectures. It could be drastically improved with a little effort and would hopefully provide more value to learners with genuine interest.

This is a great course if you need a concise background in data science. The more you explore the resources and extra materials the more you will learn. The instructors are well versed in the field and certainly have their hands full constructing software and analysis for what is quite possibly the number one research medical hospital/college in the country, Johns Hopkins. Any certificate or credentials from this institution is indeed most valuable and highly recommended. The material can be a little dense since it is a "crash course", but if you retake your tests and use them as a review of the basic core components of each lesson, you will get what it takes to get a team off the ground working on a real time project with precise goals and applications. Use the variable speed on the videos to slow the lectures down when needed. A great introduction to the varying data sources you will use as a data scientist.

I think the course is apt to begin getting a feel of data science management and needs. I have realised during the course that it is a fairly complex discipline for a beginner with no exposure to statistics and the terminology.

Initially it might seem like over the top, but with some research and reading of suggested materials , one shall be able to get a hang of it in due time.

I also believe the instructors have had to work hard to make sure the upper level course can be delivered as close as possible to the level of a beginner.

My only suggestion is if they could add a list of sorts for statistical jargon , explained in layman terms, which would actually enable quicker absorbance.

A quick, efficient and effective introduction to the notions of Data Science. I liked the fact that it fused traditional analysis techniques with modern tools, infrastructures and approaches without taking sides (ie inferential statistics and hypothesis testing versus machine learning and deep learning algorithms). This helped towards getting an all-around view of the scientific field. Note that some experience with statistics will be required to assimilate information so I'd suggest that (if you have never taken a similar course) you have done some prep-work or you are kind of familiarised with the field.

For an introductory course, this was really fantastic. It's really high-level, but it gives you a framework to understand the world you are entering into (or considering entering into). This is the first course in the Executive Data Science specialization which is built around topics like hiring a team of data scientists, and managing data science projects. So, this would be a great first class for that. But, it's also a great start for someone moving on to getting elbows deep in R Studio, Python, Hadoop, etc. I think. This is a really friendly introduction to the world of data science.

Really should be retitled to "An Executive Overview of Data Science." The course covered all the critical Data Science components that Executives or Senior Management should be aware of. Corporate america still does not understand, nor recognize the critical components of properly funding, supporting and committing to a Data Science effort or program. This course outlines the needed core components for a successful Data Science journey that could impact the top and bottom line.

great for existence human and android based life-form simulation internal lifestyle...

The course improves life within the simulation 10 fold at least(when combined with the other specialization courses) ...

Perfect learning tool for those who have worked professionally in research field sin our simulation and yet now have a touch of "the turrings" or you know : CBI...

Special thanks to the designers of the course.

Top scores for coursera.org &John Hopkins ...

The course was very enlightening. Very well structured and examples very straight-forward and easy to understand. The only thing I would suggest, and I suspect this is more of a Coursera thing rather than from the course itself, is that when you are following along with the transcript in a lecture, the video window becomes unworkably small. If there was a better split between text and window, that would help immensely.

A Crash Course in Data Science by Brian Caffo, Jeff Leek, Roger Peng with Johns Hopkins University is amazing. I found this essential in understanding the role in successful organization along with building the structure, data science toolkit, defining success as we separate hype from value. I look forward to learning and exploring further with "Executive Data Science Specialization". Special thanks to Coursera.

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career.

Please, take a look at my LinkedIn profile: https://www.linkedin.com/in/joseantonio11

I did this course to get new knowledge about Big Data and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors.

Regards

Great, clear overview of the standards, principles, terms, and applications for data science - geared toward executives, managers, and professionals working with or directing colleagues and team members specializing in data science. A great snapshot for organizational leaders seeking to get a better grasp on how to make critical decisions about data science investments, resource allocations, applications, etc.