A few more hands-on for using IBM Watson studio efficiently would have been helpful. Still it was an amazing course.

The videos are good. The level of assignments can be improved.They are quite easy and straight forward.

The IBM section at the end is depending on the cloud services that sometimes have connectivity issues.

The final assignment needs some editing. The instructions were vague and grammar needs some work.

Python basics are taught well but at the end sessions got too hard all of a sudden.

Gives insight into the language of Python, but not much application practice.

well structured courses and assignments the quizzes could be more challenging

some parts were simple with difficult assignments following immediately

Peer to Peer assignment was confusing and misleading.

it covers the essential basics for data science

Explanations too cumbersome around APIs

This course was of intermediate level.

Good

mmm

The course does give a good overview of Python. While the instructor seem very skilled at Python, they are not well trained in how to teach. Concepts are introduced before they were explained. Some items are explained later, others are never reviewed. Some of the scenarios used to explain the code do not really work well or are not real-world way that anyone would do something. Some exercises in the lab are unclear of what they are asking for and/or not shown in the examples preceding it. While it is necessary to do some advanced coding to setup the labs, these do not include any explanation of what the code is doing in detail as if we are supposed to just ignore that rather than learning how that code works and why it was done that way. As a deaf person, it was very disappointing that the majority of videos are not captioned and, therefore, also did not have transcripts. Both of these are essential for a deaf learning. Having to rely on Chrome for captions makes this course far less accessible than other courses on Coursera.

This section is an intro to python basically and it trys to cover a lot of ground with very short videos. And as you can guess it's not great for conveying more complex aspect to python or data analysis with pandas.

I have taken multiple python tutorials/courses along with data science for python and sql course. I have to say that it wasn't worth it for me on this course, but it might be useful if you are a complete beginner or taken a course or two and need the refresher.

However, given how complex python and pandas/other packages can get, this water down version is just what you would expect from a course trying to teach bare minium to get you up and running. I suspect the next few course sections that also deal with python will help supplement/reinforce what you learned in this section so I gave the course a 3/5 rating.

I am rating this low, because the content can be improved a lot. Especially the Week 5 Content.

Also, in the Webscraping , some some Libraries were used without telling anything about them, like 'pyfetch'. And, they keep using a custom Download() Function, with await and async keywords. These should be introduced first, otherwise do not use such.

And, the append() method of Dataframes seems to be deprecated. i hope you would soon change in to concat().

And, the read_html() method produces error, even though 'html5lib', 'lxml' were installed and imported. I was unable to resolve this issue, neither in the SkillsNetwork Online nor locally.

I knew Python already, however i have now learned the Numpy, Pandas, and APIs very well. I hope i will Develop a good Project using these Tools.

It starts easy but finishes ver complex. A beginner can follow easier in the first part but will have a lot of trouble in the end because it get vey complex. I had a hard time but it's not difficult to complete the course, however, for person that doesn't know programming, the course finishes with the person not knowing how to use properly half of the content. Besides, the labs are not so intuitive as the exercises have a huge gap of difficulty. The videos are short and don't have a clear explanation, they leave the explanation for the labs that have the same text from the videos. The labs are a little confusing and the hard coding is just showed to you with no explanation, you just accept it.

Module 5 is absolute garbage. Everything is ordered the wrong way. Things NEED to be reordered. Damn! How tf did they drop in quality just like that from m4 to m5? Until m5 everything was ok. They never teach or talk about pandas.read_html and yet in m5 “Practice Project: GDP Data Extraction and Processing”, they ask you to get a table from a URL. Only in the solution do you see that you need to know about pandas.read_html to be able to solve this. Later they talk about it. Like what? Why not teach it first then exercise? This is insane. Never in m1 to m4 have I faced this. But in those module 1 to module 4, I learned a lot. My basic in Python is complete. And now I go make PROJECTS> BYE!!!

I found the premise of this course effective and the introduction to python and everything it encompasses was great. However, the section on numpy the labs didn't work, it wasn't explained why numpy is so useful in terms of matrix multiplications (am sure we will find out but even just a quick blurb / insight to link understanding), there were no case studies in the latter half which made it just information dumping. And the final section on API also didn't clearly explain why they were so useful. There was a lot that this course could have done - fingers crossed the content improves in terms of explaining larger picture too.

The first 2 weeks were good. Challenging but exciting at the same time (although the quizzes were really easy compared to the actual material). But. from week 3, the course just doubled in speed and, by week 5, I couldn't keep up with all the new things they were introducing. In the end it was hard to feel like I learned enough. I had grasped the content from week 1 and 2, but from week 3 many important topics are not explained deeply enough for a beginner to understand. If you're already knowleadgeable then take this course . don't know if I'd recommend it to another complete beginner like me though.

A lot of passive exposure to basic structures of Python , but desperately needs more practice examples and more explicit exercises using code. The instructions for the final activity make a leap several steps past what is presented in the videos and asks that you figure out how to do multiple steps of code without any actual practice coding prior to that. Says its for beginners, but does not teach for beginners. Coursera needs something between Python for Everybody, which is super slow, and something like this, that assumes you intuitively get it.

Although I think the material is really good, I think the content of the quizzes in the videos and the quizzes in Coursera does not prepare you enough for the Lab works in the course. Those quizzes seem too simplistic and then the jump in difficulty in the labs can be frustrating for many. I suggest that the quizzes provide a little more comprehensive detail in a way that tests your understanding but also guides you to doing better jobs at the labs

On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.

Weeks one to four are good. Week 5 is full of mistakes. You lose a lot of time for correcting stuff instead of actually learning them. The assignment takes double time just because of the corrections in the given instructions. The data that you have to load in order to complete the assignment are in the wrong address! More examples and coding should be included in week 5.