Nice

GOOD

good

Good

GOOD

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Mostly negative reviews are because people didn't complete previous 4 courses. This project is a satellite for the previous course, and sure it is helpful, as teaches you skills, which you can actually share, let's say on Linkedin. I'll give 4 stars, because in general the course is well structured(as well as specialisation), packs you with all the necessary skills to strat Data Science journey... BUT the lab is in Jupyter Notebook, which is fine, but certain coding moments are whether outdated or pre-coded. That's the problem, I think. Isn't it better to make labs, which have materials you can recreate yourself in Jupyter Notebook from scratch? One of the solutions I see is to update the lab more frequently, and what's more important, review it by beginners, so that they can ask questions and spot the moments, which may be easy or too obvious for creators of the lab.

This part needs to be fixed: I am trying to paste this link into the Notebook URL box but the provided URL might not point to a notebook in a valid format. Should I go ahead and Create the notebook? https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%204/PY0101EN-4-3-LoadData.ipynb?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-wwwcourseraorg-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2022-01-01

The course is generally easy to understand. However, the course title suggests a comprehensive coverage of various aspects of Python and Data Science, rather than focusing solely on data scraping. Additionally, there is ambiguity in the requirements for the final project. While the provided websites include both annual and quarterly revenues for public companies, the assignment only mentions 'revenue' without specifying the type. This lack of clarification resulted in the need for me to redo my project, causing unnecessary time wastage.

There are parts of the final assignment that were frustrating because they required skills that were not thoroughly covered in the course material, so I ended up having to turn to other resources and look through documentation for pandas and beautifulsoup to find what I needed. This extra challenge made it more rewarding when I finally did figure out how to complete the tasks, though, and the process felt more analogous to how a data scientist would operate. Overall, I would recommend this class since it does provide helpful skills.

As part of IBM data science specialization,it is good so far. Instead of project,it is an exercise with a little challenge the disadventage in this course are:

1.IBM cloud is limit to 30 days if you enroll specialization it not enough

2.It different when you use your local Jupyterlab and Skill learn (IBM) Jupyterlab in the course

3.It not a perfect instruction and take time to search some solution on internet even if you learn previous course.LOL

I had a different expectation for this course. I actually expected a project where we would collect the data and analyze it. But it was too simple to be considered for a portfolio. We didn't really learn how to analyze the data, we don't know exactly what to do, we just study the notebooks and try to replicate them by copying and pasting. Besides, I couldn't make many of the commands run on the IBM Watson Studio for the final assignment.

The course gives a nice overview of Python, Pandas and Numpy libraries. It does not give a complete overview on what the libraries contain or are designed for, but it introduces selected examples and programming tasks that utilize the library functions. Programming tasks are done on Jupyter Notebooks, partially in IBM Cloud. There are some broken links in the course material, but it works.

The project a bit silly, there are some ussues in the final assignment description. Also all Python related notebook should be updated with most recent packages... or particular package versions shouldd be clearly stated. Note that not everyone uses online jupiter environment, some (as myself) use their own local setups.. so set of libraries and their versions can cause issues.

A good course overall that assumes you have a fairly good understanding of Python and Pandas. I wish the program was more interactive. The final assignment is very easy but instructive, so I enjoyed it, although the grading process is terrible (taking screenshots; really? couldn't we just view and grade the notebook directly?) Still an enjoyable finance-oriented case study.

Course is nice, web scraping is nice tool (and is used here nicely), if you never used it. However, eventhough the course has Data Science in its name, it contains almost no parts of DS itself. No work on data itself, no modelling etc. only print out some graphs and thats all. Its short and nice, but no DS. Lastly connecting to IBM servers is slow and time demanding.

It was an interesting project that helped familiarize you with some interesting tools and show the types of things possible, but I thought maybe there was a tad too much hand-holding. That being said, I peer-reviewed somebody who missed every question because they just didn't seem to really understand what was going on and tried to mindlessly follow the hand-holding.

The explanations were thorough, and it was fun to learn web scraping. However, Watson Studio was difficult to deal with. I was unable to open up my "final assignment" notebook after I exited out. The "Assets" tab did not seem to work for the Db2 Lite Plan. I had to redo parts of the final notebook and complete it in one sitting.

The course overall is great. My problem is with the graded assignment. Having to copy and paste all the pictures in, when you link the notebook directly is quite annoying. And then getting minus points on the assignment for being a noob and copying in the wrong picture.

Everything else is amazing though, so still 4 stars.