184/5000

Conferences of very good quality, and the platform for practices is really useful to put the theory into practice. I recommend this course if you want to start in data science.

I would highly recommend the course to those who have no background in Data Science. I started without any knowledge about Python and upgraded it with the help of this course. Videos are short and informative. Assignments are short and related to the videos discussed before. It's easy to finish the course before deadlines.

The only drawback is that the course doesn't have any Financial Aid.

A well presented and interesting course. It would have been good to have some more complex examples with the thinking behind them - the exploratory bit/intelligent bit of the process.

The course was well until the last week.

The python tutorials were really good and helpful but the statistics videos didn't really add any information or explanations to the slides.

My main issue was with the peer review process. After submitting assignments you are at the mercy of others reviewing them correctly (or even at all). Equally, in reviewing others work I struggled with rubrics like 'is there an explanation'. In some cases the student had included an explanation that I thought was wrong. By the rubric this scores a tick but there is no check on the quality of the explanation. I didn't want to mark examples like that as nil in case it is me that is wrong, not the submitter. Not a good system.

i am giving this note because they read theirs textes. it is not a teaching method. ı could read myself as well. i don't' understand why they do like this. in addition, the project is not well planned.

Excellent and very well designed course. The way the course exponentially takes you from the very basics of the topic to a certain level of mastery is commendable. If you know the basics of data science and Python programming, this course can easily be completed in less than a week. The peer reviews can be more productive if fellow learners actively participate more often and leave valuable feedback, rather than only responding to mandatory radio buttons for feedback.

I felt that the instructors were passionate about the subject and it made me want to learn more. The course assumes that you don't know any python, which was good for me as that was exactly my situation when I started. However, if students did have a more advanced knowledge of data science concepts and python they could show this off in the assignments.

Very good course, even for someone who isn't a beginner at data science. Filled in some holes I had in machine learning, plotting and statistics. I like the way a few mistakes by lecturers were left in the material and then a point was made to talk about the errors and why they were wrong, etc., which added to the learning.

K Mean algorimth need to be explained in more detail with 2 to 3 examples

It's a good course but data cleaning should also be included...

Not much details were covered

This course is suitable for individuals interested in gaining insights into the field of data science or as a refresher for those with existing data analysis knowledge. While some tutors may not excel in verbal presentations, the availability of transcripts allows for better comprehension of the learning materials at a later stage.

Based on my experience with grading assignments, the course participants exhibit a diverse range of knowledge, with a majority being new learners in data analysis. Occasionally, frustration may arise when fellow learners subjectively assess assignments due to limited understanding of the subject matter, language barriers, personal biases related to names or preconceived notions, and so on.

To address the potential biases mentioned in the latest part, I would recommend Coursera to consider anonymizing assignment submissions by displaying only initials instead of full names, just like for the individuals assessing the assignments. This measure would help mitigate any potential biases that could influence the grading process.

Started from scratch and excellent progression! The sad part is that we only learnt K-means, would like to learn more topics via this structure :) Discussion forum commmunty was also super helpful! But, of course, you might have a different experience. Had to find some code on my own, yes, that's how it is in real life, but I'm lucky that I had generous and communicative peers who were open to sharing their code! I shared too - here's my link: https://www.coursera.org/learn/data-science-k-means-clustering-python/discussions/weeks/5/threads/MtHzBf6_EeqUdwo2TDNrdw Also, took a while for my assignments to get graded, I don't know why, but you can trade peer reviews with your peers in the forums (i.e. grade their submission in return for them reviewing yours) :)

Learned much from this course thanks to all great instructors. It will be better if learners have some basic Python knowledge otherwise may have some difficulty in the coding of the assignments. As with all MOOC there are always rooms for improvement. For this course my thinking is that some sections need to be revised for better clarity e.g. the Mathematical explanation on Euclidean distance where it can be overwhelming and the learners may find it difficult to relate it relevance to K-Means amongst all the mathematical jargon. Overall this course provides good insight for beginners into understanding K-Means using Python, and an overview of performing proper data science project.

This course was excellent. Though I already knew the concepts in this course and the programing skills, it has inspired me to 100% take the MSC provided. Excellent lectures. Just Excellent.

This course gives us a good balance between theory and practice. I wish there was an intermediate or advanced level to continue.

Excellent course! It was well distributed, videos and theorical content, and then, practical videos and cases. Recommended!

It is a very apt course for beginners. All the concepts have been taught and discussed properly

It is a very detailed and well planned course. However, there could have been a few lectures at the end on training set, testing set etc.