It is a good course, teaching about the general process and life cycle of a data science project. Excellent tips are provided. Overall, I feel it was lacking a bit in content for 3 weeks.

It was good to be able to learn the cycle of data science. The learning materials were helpful. This lecture is recommended for those who want to learn the whole flow of data science.

More real examples will be very useful to get more understanding of the methodolgy, but the course was so good that now i know how data scientist think and handle the problems they face

It is self paced giving a good grounding on the subject provided you are an inquisitive & enthusiastic learner. It does not provide a deep dive but gives a road map for self learning.

The course introduces us to the data science methodology. However, if the examples used for explaining the concepts have to be changed for better understanding of the concepts.

Although we can gather a lot of knowledge from this course, many of the lessons didn't have the transcript and many others only offered Turkish transcripts instead of English.

It is a very good course for the beginners as I got all the basic - basic information about the process in data science and machine learning which would be used in the future.

This course finally gets into the nuts and bolts of data science. The very specific examples with Python code shown make this the right introduction to the rest of the course.

It is great introduction for someone who has no idea about data science. The methodology is clearly explained. However, the example discussed is a little difficult to follow.

I'm very thankful to this course because it neatly explain the step followed in the data science project.Also the quizzes help me to avoid misunderstanding of the concepts.

A good introduction, but as you learn you also need to further explore and read more. I would have loved to see a list of readings providing more examples and case studies.

The methodology seems a bit arbitrarily devised to give 10 parts though the 10 are sometimes not meaningfully distinct.

Still a well structured approach to breaking it down.

This course compresses much material about approaches and modelling, and describes only a few of the concepts in any real detail. Though, what is presented is done well.

Case study isn't suitable for all categories of student ... i didn't understand the case study because i have no medical background plus there was hard expressions for me

It was exceptionally good, with case-study driven content and accurate explanation of the different terms and condition before moving to a data science specific problem.

This course is great except that it has problem of labs which are not working correctly too many times. It is really challenging because too often it seems it is offline

Interesting course, although some very "thick" notions. It looses 1 star because of the final assignment, which is very vague and open to all kind of interpretations.

Very informative. The case study discussed was not very interesting to me, but everything was very easy to follow. The interactive labs helped with hands-on learning.

Quite nice, really lets one follow the methodology - good mixture of videos, labs, tests. Final assignment is great to develop a full concept for a specific problem.

The content needs to be updated and better align with the lab work. I was expecting more functional lab tool examples like RStudio, Zeppelin, and Jupyter Notebooks.

This course assumes that the taker has taken statistics and remembers a variety of terminology from that course. A refresher/terminology reference would be helpful.

I 've learn a lot from the good course and resource, but too many medical terminology make the video difficult to understand for a non-native English speak learner.

The assessment and the review method for this course need to be improved. I would suggest the passing rate would need at least three person review instead of one.

The lab often dose not work properly, and the vedio of lecture is a little bit confusing to understand, it should be well oganised. The course is helpful anyway.

Good lectures on the data science methodology including an understandable case study. The peer review procedure is a bit strange, but it worked out at the end.