The course is easy to follow, well organized, and assumes very little background. It effectively demonstrates the power of Python in large data applications and provides insights and guidance on which tools are best used.

I learned a good deal from the course. I am satisfied with the content of the course.

The problem I encountered with this course is on the grading of the final project. The format is by using peer-review. But you need to have 3-peers to review your submission. I submitted my 3 weeks ahead of the final deadline of submission but still it was not reviewed by 3 peers. So there was no score on my final project. That does not seem fair.

This course enables students to learn intermediate level skills in data wrangling, data exploration, and visualization. The final project requires selecting a topic of personal interest and constructing a complete project work flow. By doing this, areas of weakness in data wrangling, cleaning/QA, data exploration, and visualization may to uncovered and addressed. The result is to build greater skills and confidence.

Great course to start with programming for business analytics.

Excellent to start your career in machine learning!!!

Great one! Made me learn from scratch.

Great course for an absolute beginner!

Over all a great course for beginner

Very Good to start learning Python

Great first class in this series.

Excellent and interesting course

Perfect Course

Great!

This course is pretty good. Both instructors explains concepts well and the Python demonstrations show that they use Python a lot in their everyday lives, but some of the lectures videos have a lot of repetition because the instructors misread a line or forgot to bring up a concept, so it slows the momentum and flow of the explanation. Was a retake of the video really difficult? There are also long periods of silence that can be rather weird, why was this not edited out? The rating should be a 3.5 stars out of 5 but there isn't 3.5 so I give it a 4 here.

A really good course to learn data preprocessing before implementing the machine learning module.

Pretty easy to start with, especially with a background in CS.

nice basic python course

The positives: I liked the design of the final project, and how users were encouraged to 'get out there' and find some interesting open source data sets. The lectures were well structured with good narratives and good examples.

The negatives: I would have liked a bit more focus on actual visualization libraries like matplotlib and maybe seaborn. When covering the data types (date, string, boolean etc.), it might be worth adding an extra week or so were these things are done with the help of the standard library pandas. I feel like this is what people will end up doing anyway bc there are so little alternatives in python to do processing, so a course on data processing should ideally cover that library.

The information learned in this course is very useful, for a beginner in data science. It is a very good introduction in working with python, extracting data-sets, defining features and plotting graphics.

What I didn't like at all is the engagement. Finishing the course was not satisfactory at all for me - even if I submitted my project on time, I didn't receive 3 reviews and I found the grading system very subjective. Knowing this, I would think twice about paying for this experience - what I learned can be found in free tutorials too, and only for the interaction with other users I don't think it is worth the price.

I have to say this is a great course. I should rate like 5 stars. But the coursera way to assess the final projects is not correct. Your final score can't be decreased if you don't have any feedback about the reason. This is not a fair scoring system.

Quick intro to data processing. More material on numpy and pandas would have been nice. Im still trying to figure out why the specialization mentions "Design Thinking". At least in this course...not a single design thinking concept was mentioned.

very subjective assignments

This is not a Python introduction, but the authors approach it as if it were. Even the most basic data scientist will not calculate averages in the way described here. We'd use pandas or similar to get not just means, but other summary stats as well. For a Python course, I could understand doing it the way shown here. But not for data science.

it's not a intermediate level course, it's a really basic one

The test scripts make no sense.