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.
Is really hard to summarize the potential of Data Science and being clear, but I think that the instructors have done their best, so that we can achieve the most from the Course.\n\nGreat Job!
por Nimrod K•
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.
por Eric F•
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.
por Reinaldo B N•
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.
por Kelly F•
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.
por Dominique W•
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.
por Marcos A K•
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.
por Árvai M•
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.
por Lorenz F•
good, high level introduction what data science is all about. The section on the structure of a data science project could have gone more into details, maybe following the steps on a specific example. I also missed some insight into the step from the question to the search algorithm. Maybe that's part of the further courses.
por Alejandro R•
I learned a lot, the instructors know how to facilitate knowledge but it's not so friendly to people who don't know much about programming, statistics, etc., and there are some grammatical mistakes in the translation to English and some formatting mistakes in some tests. But overall, I'm satisfied with the course.
por Thorsteinn A•
Quick scope of data science. I particularly liked the discussion about fad versus fact towards the end of the course. Some questions in quizzes seemed a bit arbitrary. The course delivers what it promises; crash-course with the key ingredients for understanding more about what data science is about
por Kaustav S•
A pretty helpful course if one wants to get an idea about what Data Science is. But I was expecting a bit more hands on example in the end. Maybe not a graded part of the course, but still something to give the student a practical idea of how things look like when they are actually doing stuff.
por Shalini P•
Some of the content and coverage was excellent and others mediocre. There isn't a consistency in the availability of slides that can be downloaded (available for some, not others). Also, when a quiz answer was marked wrong, I would have liked to know what the right answer is for the question.
por Srinivasa L•
A good course to start on your journey to understand DS and Machine Learning
I am familar with most of terminology through my experience. My learning is limited because of that but some one new to the filed would definitely learn more. I have taken this as part of Executive Data Science Program
A good primer for aspiring or casual data scientists. I wish it were more technical so I could adopt some assignment solutions for my work, but for one week course the syllabus covered is perfect.
Wish I could save my progress for free to show only to myself that I've completed this course.
por Shardul N•
This course is an excellent starter course.
Just one thing, a few simpler examples for certain concepts may be better for understanding them, as some people (like me) who are not well versed with scientific terms and concepts find it a bit challenging and difficult to understand them.
por Rong-Rong C•
Crash course is the correct description; vast amount of material covered in a few segments. As a result, this course is fast-paced and does not go too deeply into any particular aspect of statistics. It is nice to have a little knowledge of the subject matter but it is not required.
por Sergey G•
This course could be really usefull even for experienced Data Scientist to correctly organize a workflow in his\her DS team. The described technologies and some examples a little bit outdated in 2020, but most of the information is quite general and relevant for any period of time,
por Peter E•
I am a PhD biologist leading a team of data analysts. I found this course to be quick and easy to follow, well presented, and extremely helpful. The instructors are truly professional and they excel in this medium, I was very impressed. Thanks Coursera and Johns Hopkins!
por Marc-Eric L•
It was very good. I am not a DS but have been exposed to their work. I would like to debate whether Hadoop is the best place to do the work today, but the point made still stands. The one thing that made me put a 4* instead of 5: I don't see how it is "executive" yet.
por Lucas B P•
I left with a good understanding of the reach and dynamics of the field of data science. Even with experience in using data to answer questions in the context of an organization this has been a good way of mapping out the areas where that experience can be extended.
por Seraphim A•
A very good intro to Data Science by three experts. Well-paced, with reasonable quiz questions and can be completed in less than a week. However, I would certainly not pay for it, because it is feels more like a well-designed ad for the spesialization that follows!
por Ben H•
As advertised. This is a basic introduction into thinking about data science problems and processes. No statistics or coding, but the discussion of tools and ideas will be valuable for anyone who is numerate but hasn't been exposed to data science practices.
por Priya S•
Course explanation/class can be a little more simple as this is a crash/basic course for data science. There were multiple situations where I had to google on few data science/statistics jargons, which I couldn't understand as I am a beginner in this field.
por Kevin W F•
While the course has excellent material, I would not send any of my executives to it. The information is to deep and technical for a business executive in my opinion. I would have liked more examples on how implementing this would benefit a business.
por Abraham E•
Good starting point if you have no idea about what data science is about. It could be more helpful if it made a clearer distinction between classic statistics and data science (it is briefly mentioned); and what are the pardigms of this discipline.