Excellent

engaging

Brutal

best

good

Cool

best

nice

a

Positive: - The instructor is very clear in what he teaches and clearly explains when we're not supposed to know how to do things. - The parts that implement repetition in labs are extremely helpful and should be reinforced more. Having to convert things to datetime multiple times cements the structure a lot more easily. - Despite my criticisms (below) on the order information is presented at times, I do think I learned a lot. Negatives: - Once again you go into labs and are asked to use tools you haven't learned. Why am I introduced to things after having to find what to use in a lab? Parts of the reference guide for Python in Module 3 for "How to handle outliers" should have been presented earlier in the course. Some tools aren't taught at all and are expected to be found in the links to manuals provided throughout the lessons (which honestly are not very intuitive). - The labs are where I learn the most, but it takes me a lot longer than 1hr as a person who is new to Python and programming in general. There should be more emphasis on these. If you want people to figure out a solution with tools that haven't been presented, you should clearly outline that in the instructions during the different steps of the lab. It is deflating to wrack your brain trying to find a solution and then learn that it was not in the tools you've been taught so far, but not being told whether you should have explored outside of your taught knowledge. - During the Tableau presentation the instructor doesn't specify to change number of strikes to a dimension. It might be easy to miss the step.

The content is exceptional. Coding in Jupyter notebook is quite good. However, I do think a review of contents could be done. There are times instructor missed to emphasize on important things, had to find answers in discussion forum. There are multiple instances where the video slightly differs from follow along guide. At some places the indexing within modules doesn't make sense. I preferred the previous Course where everything was much more organized. Where needed video would have a prompt to open follow along guide and navigate to specific section. Makes it much more convenient. I hope someone from the team reviews the contents once and fix these issues

The videos in this course are longer and harder to follow than the previous ones. The purpose of some of the EDA processes is hard to understand. I think they will become clear in the machine learning state. The instructor used many analogies to help understanding.

This is a pretty good course. The quizzes are a bit easy. And even though the Specialization has "Advanced" in title, it is not all that advanced. But I recommend it to get started after completing the "Data Analytics" Specialization from Google.

Overall great materials, just a little bit too rushed and cramped, especially in python coding. As a beginner in python it's quite hard to follow along and the lab exemplar sometimes they're just too advanced.

The content is very thorough, and the presenter was very professional and clear. I wish the audio on the videos were 300% louder -- I could barely hear most of them.

Two thumbs up! An insightful and well constructed course. Authentically useful in furthering one's competence in the practice of Data Science.

The Course was good. Tableau was excellent. Only the Python coding was way too much and a chaos. The build up was confusing.

A new perspective; to clean the data, to data storytelling, to visualize and to handle outlier carefully

Its a great course with alot of details in it. You need alot of practice to cover this material.

Good work in the course, you always learn new things.

Great content but difficult to master.

Have fun using python

Good

d

I am a complete beginner at python, I struggled a lot. I think it is better to at least have some experience in python. But in overall the course is too informative and full of worthy to take