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學生對 IBM 技能网络 提供的 Data Visualization with Python 的評價和反饋

4.5
10,261 個評分

課程概述

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

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LS

2018年11月27日

The course with the IBM Lab is a very good way to learn and practice. The tools we've learned in this module can supply a good material to enrich all data work that need to be presented in a nice way.

AM

2020年8月13日

Great course, one of the best course to get hands-on learning for Data Visualization with Python. Particularly the lap exercise, it will make you think on every line of code you write. Excellent!!!

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101 - Data Visualization with Python 的 125 個評論(共 1,552 個)

創建者 Renier S

2020年4月24日

The course is very good. Intuitive and easy to follow. The real challenge is in the peer review exercises, where your patience is tested. You really have to work hard to get all the solutions to the questions. There are so many things that the course just can't teach you in the time constraints.

創建者 Atfy I Z

2020年4月21日

A great course for you to further understand the mechanics of data visualisation as well as providing a space for you to familiarise and test your understanding on the subject matter.

創建者 umair

2019年4月11日

this course should come before data analysis with python

創建者 Rodolpho P

2020年9月29日

Although I understand that learning doesn't take place at only one place, this course seemed very weak in terms of providing enough examples necessary to solve the problems in the final assignment.

All videos had a same part that was repeated, and no information was agregated by this repetition.

The contents of the labs are quite good, but a more detailed explanation could exist.

Some updates are needed: one of the labs uses MapBoxBright, which gives us a clean figure with no map because this is not available anymore.

The final assignment required us to look for solutions that were not present in the course, and in my opinion, they should be. The student should go to outside sources when it feels a need to understand something deeply or if the way presented by the instructors was not the best for the student to understand what's going on.

There's a lot of room for improvement: the videos should not be repetitive; the contents should be updated, anything that is required in the assignment should be presented throughout the course, if it's not in the labs it should at least be in the videos; the final assignment could provide a notebook with the requirements as the other courses in the specialization offer (in my case, I took it as part of Data Science Professional Certificate by IBM); if this is not the case, the student should be prompted to create a notebook with the questions and answers, which would estimulate even more the creativity around data visualization.

創建者 Baher

2020年8月23日

Hi,

In the final assignment, I had to explore the internet to get some codes to display the bar graph or the map. These codes were not covered in the class. The course needs to get improved by giving the keys of how to do things . For instance, the method .patches was never covered in the course. I do not know how to use it. It may be a part of panda library, but the method was critical to do the assignment. There are many other examples. I spent almost a night to finish the assignment because I took a long time to self learn these tasks. It is good at one side, but the course should help me.

Thanks

創建者 BISHAL C

2021年11月19日

The course content is great but the way it is being taught is not up to the mark..

the labs are good but that's not the way everyone can learn things..

Something can be done like some instructor should be there who will be teaching us about those libraries. In the videos, the instructors are just giving a brief idea about the libraries and asking us to go through the labs for better understanding.. How about giving more ideas where someone will guide us through the labs too. I hope you can understand..

創建者 Vimal O

2021年11月9日

On overall IBM data science professional certificate track: P​ros: 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.

創建者 Bob D

2021年12月16日

Some good material, but some was pretty niche and therefore less useful. The lessons were okay, but as usual the whole thing was riddled with typos and technical issues. Not good enough for a major organisation like IBM.

創建者 Sarah s

2019年1月14日

This course was nice but there were extra stressors that weren't included in the course.

創建者 Frank A I

2021年3月21日

Unfortunately, this has to be to worst Coursera course that I have taken. I only give it 2 stars for the first few weeks, otherwise this is more of a 0/5 star

While the beginning was a descent course, the final project was very much a left field task. While it did have some of the material from the course, there were several aspects that were not explained in depth or not at all. It also didnt help that there were some errors in the base code that were not explained and it took the students to resolve them.

Many people pinged the instructors for assistance, including myself, but aside from a few comments here and there, all their responses were basically "Review this thread that says to run the code a gain and give it time to load". When I asked for more detailed instruction beyond that thread, I never heard back from the instructors. With so many students opening threads and asking the same question, the instructors should take that as a hint that something is wrong and that they should take a more active roll to resolve the issue (a new lecture or assignment to help explains the errors rather than a thread provided).

The best part was that when I mentioned I had issues with this at work, a coworker of mine who had 20 years experience, at least 10 of which is with Python, offered to look over the code with me. He was confused with what the instructor was attempting to have us learn with the final assignment.

In short, I rate this course low because of the final assignment not being properly explained before or during it and that fact that there is little to no instructor support beyond repeating themselves and telling students to "toggle the dropdowns and wait"/ "rerun the code"

創建者 Yohann P

2022年4月4日

While the lab contents are useful and I will keep the code to come back to it in the future, I find the video lectures rather superficial and the assignments completely useless. The final assignment was basically copy and paste from the instructions to the code skeleton provided by the course. There were typos in the code, and it wasn't using the latest versions of the libraries being demoed. The assignment submission form seriously needs to be reworked with clearer instructions and the correct number of upload fields to match the number of files requested. Also the assignment asked me to prove that parts of the code I got from the instructors worked. I don't see the point in doing that, apart from wasting my time. I feel that the course was designed by multiple people under time pressure and there hasn't been sufficient reviewing across the content to check that things match up. In short, I recommend reading the syllabus before enrolling into the course and picking up with the documentation of the tools included in the course. Let's be honest, no one uses matplotlib. So skip that part and move on to the other libs in the course. Each on has a very detailed documentation with lots of tutorials and huge communities you can ask questions to. Save yourself the 15 or so hours it took me to trudge through the content and get the same amount of learning done in half the time by going directly to the source.

創建者 Gina A

2021年12月15日

The first few weeks of content was actually really useful for someone interested in data science, but the last 2 weeks were a bit of a trainwreck. It was clear that the assignments related to dash weren't well conceived--there were many issues, instructors were slow to address these issues in the forums in a concrete way, and honestly it felt more like a programming lesson than something practical in data visualization.

The final assignment grading also wasn't well conceived. The way the guidelines/questions were asked were very unclear, so sometimes you didn't see what you actually needed to upload until you had already taken the screenshot or submitted the assignment. This carried over into the grading--there were times the directions said to remove points if certain things weren't present on a student's answer, but then as a grader you actually didn't have the ability to remove points for that reason.

These types of mistakes are, I feel, pretty sloppy and shouldn't exist in a course that's part of an IBM-backed certification that people pay money for.

創建者 Thierry C

2021年7月31日

T​his course is the most disorganized I ever followed to date on Coursera. Up to the step where we learn about the Dashboards, the course is pretty well presented and the labs are working with good guidance but then, when reaching the dashboards, the guidances disappear, none of the dashboard lab work in JupyterLab. The final exam is a torture with a peer review submission so messy that you will have to spend more time assembling screenshots together than producing the dashboards themselves, and after so much struggle, you will have to answer sneaky questions with some answer that do not even make any sense in English. Be prepared for a lot of frustration and even though, they say that knowledge of HTML language is not required... well... most of the dashboard labs are actually based on HTML language. I wish the course was better prepared and that all labs were working, I have learned less than I could due to those broken labs.

創建者 Alasdair T

2020年6月22日

Course could benefit from a refresh - for instance, support for Mapbox Bright tiles has been dropped from Folium for 1+ year, but the course still tries to demonstrate their use. There are several posts from confused students wondering why this doesn't work. Surely it'd be better to just remove/update this section of the course rather than have to deal with so many bug reports in the forum?

Also the videos for this course are extremely repetitive and barely of any relevance, e.g. 1-2 mins of several of the videos is just the same footage of the data being imported to Pandas and cleaned. Once you've seen this once, you've more or less got the point. Add to this that the Final Assignment required knowledge of matplotlib which was *not* covered in the course, and had to be researched elsewhere, and it seems obvious that the quality and relevance of the video content could be improved significantly.

創建者 Carina D

2021年4月20日

Although the course was informative, the course components related to Dash applications need to be reworked or removed. For a course that should only require the use of a course-provided cloud-based Jupyter notebook (JupyterLab via IBM Skills Network Labs), the labs and final assignment should work via that service. The final assignment also should be reworked into a new assignment because of its incompatibility with the course-provided Jupyter notebook. A final assignment should not be debugged extensively and require the use of outside applications (one of them being an application that requires computer installation) to be completed. The course should explicitly state what resources are needed for the final assignment if it requires outside resources, and the debugging instructions should be listed in the final assignment instructions, not in a thread in a discussion board.

創建者 Ricardo S

2021年3月12日

I'm disappointed. I believe data visualization is a very important skill, but this course didn't teach the most valuable skills.

The videos feel like someone is merely reading the documentation. There is a difference between showing something and teaching something, and very little was thought in this course.

In the labs, the visualizations (1) Do not tell a message (2) Are not compelling (3) Do not teach you how to generalize the idea behind the chart.

The worst part: the course creators apparently know this. Some of the labs don't even have exercises, because clearly these "classes" are not enough to teach you how to do it on your own. And the final assignment has multiple posts explaining how to fix the many oversights in it.

This has honestly impacted my opinion on IBM (is this what you offer to your clients?) and Coursera (is this the average quality of a course?)

創建者 João R d C

2020年2月4日

Out of all the courses in the IBM Data Science Professional Certificate, this was the one I had the highest expectation for and unfortunately I was a bit disappointed. The course materials are lacking in information and the final assignment asks for customisations that weren't covered in the course materials, which leads to question: are these important things to know and the materials are lacking in information ? Or are these irrelevant and should be a part of the final assignment? Because if they're just there to make sure no one gets a 100% grade, then that's just sad.

創建者 Anoosh G

2021年2月6日

Final assignment was frustrating, its was difficult, It took more than a month to get my assignment reviewed. At the beginning i waited for a week, I did not get any peers to review, then soon after a week when i logged in, my assignment was gone and 4th week videos and new assignment were reset. I completed again all modules and new assignment finally and again waited for a week to get it done. I've spent more time in this course in the entire Data Science Professional certificate, I don't know whether this is a problem from the creator or coursera itself.

創建者 James H

2020年5月5日

This class could have been one of the best based on my interest, but it wasnt explained very well and I had to use outside sources to figure out what was going on in the labs and sections... Also some of the final project material wasnt covered in the class itself... It was more difficult than it needed to be... Once I used Google to find answers, the stuff I actually learned were useful...

創建者 Mehmood S

2020年4月29日

Much of what was tested, was not taught in the course. Therefore, the course requires individuals to do their own research online to answer the final assignment questions.

The purpose of paying for the course is to quickly learn fundamentals from the course, NOT to spend hours looking online for the right answers and waste time with trial and error experimentation.

創建者 Mark S

2020年8月19日

Very poor. As the course carries IBM's name, I expected a premium product, but I was disappointed. The training videos were brief and didn't go into the material in any great detail, and certainly didn't prepare the student for the lab sessions or the final assignment. I learnt more from Stack Overflow than I did from from the training videos.

創建者 Ana C T M

2020年1月21日

Links did not work for classes hands-on exercises, repetitive video explanations, and final project required content that was not explained in classes. Overall, it was a bad experience on a subject that should have received more thought and caring from instructors on lesson plans and class materials given its importance.

創建者 Sean H

2020年8月14日

Labs and lessons did not adequately prepare me for the peer review lab. A lot of information went through quickly or was hard to reread on account of the sheer volume of charts created. Minor gripe but when learning pie charts the videos mention pie charts being awful without properly explaining why they are awful.

創建者 Jeff S

2021年1月8日

Dislikes:

-Many videos repeated just over 1 minute of the exact same content reviewing the dataset.

-Videos were very brief and then exercises would be beyond concepts taught.

-lab contained thick code to prepare graphs but not explained.

Likes:

-Enjoyed creating the maps & learning about other visualisations.

創建者 Steve K

2019年10月20日

Almost all videos included the same bit about getting and reframing the data. This was a significant portion of the videos as well.

There seemed to be more confusion around the final assignment judging by the amount of questions in the forum. The assignment needs to be rewritten or made more clear.