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Voltar para Data Visualization with Python

Comentários e feedback de alunos de Data Visualization with Python da instituição IBM

9,609 classificações
1,437 avaliações

Sobre o curso

"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....

Melhores avaliações

27 de Nov de 2018

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.

13 de Ago de 2020

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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876 — 900 de 1,430 Avaliações para o Data Visualization with Python

por Amy P

26 de Mai de 2019

Once again, quality hands-on labs were the highlight of this course (as has been the case throughout the IBM Data Science Certificate courses). The end-of-week quizzes were also a bit more difficult/involved, which was a good challenge. Still, I think there's room to increase the difficulty a bit further - after all, you can re-take the quizzes if at first you don't pass. I appreciated that the final project gave us the opportunity to apply a wide range of the skills that we learned.

That being said, I think there was quite a bit of fluff in the lectures. I would have preferred more content/exposure to other libraries rather than the redundant "data recaps" at the beginning of almost every video. I also would have appreciated more theory/recommendations for selecting the best visualization for a given application.

por Lena N

26 de Set de 2018

The best parts of the course were the labs and the final assignment. I spend a lot of time at the labs, paying extra attention to the details and often following the external links suggested by the instructor. I found the final assignment very interesting with good explanations step by step and I especially liked how the instructor were present at the discussion forums.

The weakest part of the course were the videos, I think I could have skipped them altogether. The information mentioned in them were elaborated much better at the labs. Also, for some reason, 1/3 of each video was exactly the same clip recalling the dataset. That felt a bit useless and loss of time! On the other hand, each video was a couple of minutes long so no big deal in the end.

por Erik A

30 de Jul de 2020

I have two minor issues. First, the final assignment called for a few things that were not directly discussed in the class. The class did recommend reading the documentation, and the needed information was available there. This is probably more realistic for a real world environment, where you won't know all the ins-and-outs of an API before using it, but a little warning at the top of the assignment would have been nice.

The second issue is that folium.choropleth() is now deprecated and produces a warning if you use it; the recommended alternative is to use the folium.Choropleth() object. Using the old method produces a warning, but works (for now). The labs and lecture should be updated to use the new API.

por Paul A

24 de Set de 2020

This course was a change of pace compared to the previous, absolutely stellar courses in the Applied Data Science specialization. The instructor is different and the methodology as well, the content was equally as digestible as the courses that preceded it, but at times the course had inconsistency on its quality. There was a lot repetition and it felt like padding at times. The difficulty and learning curve takes an abrupt spike on some of the assignments, but nothing you can't manage if you put in the work and leverage on the content of the previous courses.

por Jess M

27 de Fev de 2019

The videos are nice and clear, the visualizations are beautiful, and I'm sure that all of the libraries presented are extremely useful. But this course is not well-suited to students who have no prior background in Python before taking the Applied Data Science specialization. I look forward to coming back and maybe having a shot at understanding the code in the labs after I take a Python programming course. The long chunks of code presented here are mostly opaque if all you have are the previous courses in this specialization.

por Ajin

13 de Set de 2021

The course is amazing but the labs are really complicated as many of the things used in the lab are not been taught in the videos. It would be very helpful if after the labs a video explanation is given about each problem in the lab or the complicated ones. Even the peer graded assignment seemed little complicated because some was not taught. It was difficult to type out the code. Only this found as odd. Rest were absolutely amazing and teaching by IBM professionals. Thank you so much IBM and Coursera!

por Ruben G

28 de Nov de 2020

The content of the course is interesting, especially the last modules. However, there are some cons.

The content of the lectures (videos) is somehow redundant. Another "negative point" is that the final assignement is not 100% doable with the contents of the course. There are details that are not covered by the lectures.

It should be easier to complete the final assignment in our computer with a (local) notebook. It seems to me that the lecturer wanted us to use Jupyter

por Henry W

22 de Set de 2020

I Learned a lot in this course and the teaching assistants have been very helpful in the forums. This is very useful information that I learned and I highly suggest this course. The final assignment was quite a jump from the videos and labs and took a lot of work to figure out. The labs could have supported the final assignment better. Also, perhaps more work and examples in the labs would help to learn the material better. Thank you for the good learning.

por Azhan A

20 de Nov de 2019

The reason I'm giving it 4 stars is because the although the content was good, the labs were challenging but there are something which I found missing, for example, there should have been more information on libraries related to cholorpleth map. !wget was not working on my PC's jupyter notebook and looking it up on the internet was even harder because this extension or whatever it is big on its own. I don't know what to write to get the correct google search.

por Monali C

14 de Jun de 2020

It was a great learning experience with coursera.After Data Science course ,learning Data Visualization with Python was my next target to complete.I learned many basic and advance things about how to work with data using visualization.With every questionior and assignments it was interesting and challenging to learn from this course.Thank you coursera for this course it was really helpful to learn and know about data visualization more accurately.

por Joshua S

25 de Jul de 2021

Like every review I've written before: there was tons of good information in the videos/readings. the test/quizzes properly evaluated the information presented in the videos. and the labs reinforced the material presented from the videos through real world application. Just the final lab project is way more difficult than anything previously presented in the class and there is little to no help from the instructor, coursera, or anyone else.

por Will S

6 de Mai de 2020

I believe a more comprehensive review of the material discussed in the Final Assignment would be beneficial. Perhaps including a directory of other topics outside of course and under which courses to find the material. I have all the information from prior IBM courses to complete assignment, but I did spend a bit of time just looking for my old labs trying to find material that covered the Final Assignment questions.

por Katherine F

8 de Out de 2020

There is a lot of repetition regarding the data within the videos, but thankfully they are quite short (especially when played on double speed). Unfortunately there are some issues completing later modules and the assignment on any browser other than Chrome because of compatibility issues with Leaflet/Folium. Other than that, the course is pretty good. iPython notebooks do make learning a lot nicer than it can be.

por Francisco M

5 de Abr de 2020

The course is good but sometimes the exercise texts are not very clear and some of the lessons are very straightforward, leaving many doubts. The course should have a larger series of exercises and an automatic correction system that facilitates the review of the exercises. In addition, it would be interesting to have a module on how to use IBMDB2 without the online platform, but through Jupyter on the computer.

por Eugene B

23 de Set de 2019

The lectures make everything seem simple, but you really have to dive into the labs and make a point of studying on your own. You can easily get through most of this course just by running the Jupyter Notebooks that are provided then copy/pasting and editing for the final. If you really want to get something out of the course, you really have to motivate yourself to learn the material.

por Oriana R

1 de Nov de 2018

Honestly, out of all the courses I've taken so far, this one was the best, in terms of presentation. The instructor repeated a lot of the formatting for each code block and by the end, one could easily remember what code to use for the specific visualizations.

The only reason I did not give 5 stars was because I thought the final assignment deviated a bit, but otherwise, a good course.

por Ankur G

18 de Mai de 2020

A good course to learn know-how of Data Visualization using Python language so as to facilitate analysis and visualization of data to make effective decisions. I thank the professors to make this course interesting and worth it. Only thing is, videos can be made in a better way so as to facilitate people with non programming background. Maybe some basics of programming would help.

por Benjamin S

24 de Jan de 2020

This course has one advantage over the others in the series: practice time. The labs are more thorough and provide more practice problems. However, the overall quality in production of this course is lower than the others. Additionally, there were some points awarded on the final project for things simply not covered in the lectures or labs, which was frustrating to say the least.

por Cameron L

1 de Mar de 2020

The last third of the course was not much more than two Jupyter notebooks that I Shift-Entered through, with a few problems presented to work out on my own. These were usually able to be completed by copy and paste, I learned more in one question in the final quiz, which required me to to the Maplotlib documentation site and apply that to the question. I expected more.

por Chung M

21 de Jun de 2020

It is a very useful course for data visualization. It guides you through all the steps to create graphs. It is a difficult course compared to the previous Python courses because generation of graphs requires a substantial amount of input and can be hard to memorize. The instruction was useful in helping students practice, but some more instructions are recommended.

por Taha m

21 de Set de 2019

Course is very well taught, it would be better if they taught us Artist Layer a little bit in detail, also the Final assignment is little bit difficult from what we have learned from the course, it would be better if labs content taught us in a video because in video we see in realtime. Overall its a great course for learning Data Visualization in Python.

por Rodrigo J S

6 de Abr de 2020

Overall, the course is good, but some additional explanation on some parameters for the graphs (specially ar the Artist level) would be good. Apart from the platform issues (xlrd was almost never loaded and need to be loaded and imported, and some downtime issues), I would suggest to move the final assignment to a 4th week, as they do on other courses.

por Jianxu S

10 de Set de 2019

It is an excellent class in terms of practice and playing with tools. The weak part is that the course does not cover much the logic behind different choices of graphics. Often, we just create a plot and tweak it to make it more appealing. Overall, I would still recommend this course to people who are new to the visualization aspect of data science.

por Vi P

10 de Fev de 2021

It took me a lot of time to realize that I had to use Jupyter Notebook, that was not attached, to do final assignment. It would be great if we have an instruction at the beginning of the final assignment that tells students about this. Also, some parts in the last assignment aren't covered in lab sessions which may cause frustration or confusion.

por Brian B

9 de Dez de 2020

The videos get repetitive as they each walk through and explain the exact same dataset as if you've never seen it before, but after the first few times, you figure out you can skip past that part. The skills learned are quite cool and this class shows how to easily make several different kinds of charts and dynamic maps from a dataframe.