Learner Reviews & Feedback for Data Visualization with Python Course | Coursera (2024)

By Karim C N

May 29, 2019

It was a good course that follows steps clearly and effectively. However, I cannot rate it higher that 2 stars for a very important reasons: Big Parts of the Final peer-reviewed assignment are not even covered in the course!!! I had to scour the internet and find my own solutions (and many others clearly had the same problem as seen in the discussions section). This is a big problem and needs to be addressed as we should be tested on the material actually learnt!

Also, almost every video repeats how the data is 'cleaned' which is good once or twice, but unnecessary the 15th time.

By Thomas M

Jun 5, 2019

The final assessment was not covered in class, and it was very difficult to figure out how to do.

By Jake L

Jan 24, 2019

I did not like that some assignments do no rely on the material that was given in the course. For example, data visualisation with Artist layer was not covered in details in the course and you have to spend tons of time on Internet digging out how to implement that. This is a waste of time, I need a course that gives me complete and structured info, not a course that sends me out to explore the Internet.

By Nils W

Mar 26, 2019

It is a strange course and the worst in this specialisation so far. In one week 50% of the video is how the data is prepared. That would be ok if it won´t be the same video snippet 4 times. Also the relation of video vs. reading is 1 to 6. In one week is only 6 minuites of Video and about 1 h of ungraded assignment.

The final assignment isn´t solvable with the given code or examples. It is ok that one had to google for code snippets, but this is far too much.

By Dan S N

Apr 23, 2019

Data could in most cases not be loaded, making the labs useless. Also, the videos have unnecessarily much redundancy. Really didn't learn much from this course.

By steven w

Apr 3, 2019

worst instructor I have ever seen,

very few instruction but the assignment is extremely hard!!

By Karel H

Sep 2, 2019

Final exam was frustrating. It took longer to complete than the rest of the course combined. Questions were included that were not part of the course including the need to reset keys. Peer review was almost impossible since I could not read the tiny screen shots very well.

By Ismael S

Jun 4, 2019

The course is very inconsistent, it repeats the same one minute in all of the videos, when reviewing the dataframe. Many times some things are asked without providing previous explanation, and the final assignment is also an example, I had to search all over internet to resolve it, because I couldn't find any reference in the content provided.

By Andrew T

Jul 8, 2020

Compared to other courses in the IBM Professional Certification catalog, this course has some noticeable deficiencies.

First, the overall content of the course rather confusing. The very first lecture focuses around efficient 'less-is-more' figure design, which I certainly agree with. However, much of the course (and most of the tested material) focuses on making extraneous graphics such as waffle charts and chloropleth maps in situations where a simple bar graph would be the most efficient way to present data. Meanwhile, the standard module Seaborn (which is EXTREMELY expansive in data visualization utility) is given only a single 2 minute lecture.

Second, unlike all other courses I have taken in the IBM certification, the assignments and workshop sections of this course are largely unhelpful. In addition to my point above, the workshops focus on manipulating aesthetics of simple graphics (i.e. changing colors in a bar graph) as opposed to showcasing the broad number of figures that Python is capable of generating. This left me disappointed with what I took away from the course in terms of usable knowledge.

Finally, the final assignment is arduous and poorly documented. There is no structured notebook that provides guidance on solving the problems, which is particularly troublesome when rendering uncommon figures such as chloropleth maps. I found that I spent >80% of my time on the assignment chasing down unintelligible error messages, as opposed to developing a real understanding of the logic behind generating graphics in Python.

The majority of other courses in the IBM certification have been very well designed and educational, I just feel that this one in particular has a lot of room for improvement.

By Baidi W

Jun 10, 2019

I would give zero if the system has. An empty course that you almost cannot learn anything especially when you're going to practice.

By Roger S P M

Dec 29, 2018

The course material is not sufficient for completing the final graded assignment. It required many hours of internet research to collect the details necessary for the final graded assignment.

By Yuanyuan J

Jan 23, 2019

The course materials are poorly structured. Labs are not well-designed and not friendly to students with little experience. This is not a very effective course.

I felt that the curriculum was not structured in a manner conducive to learn the material. Too much of the training and lab work was to execute already prepared commands without an explanation as to why we were doing using these commands. In addition, the training material could have used more detailed explanations and then lab work allowing the student to apply this knowledge. Instead, you watch a lot of abbreviated videos and then do a single lab exercise that has the student try out certain aspects of what was covered.

For the final exercise, there was so much that wasn't covered in the course material. This took many extra hours searching for how to do things. While this type of searching might have been helpful in preparing for use of Python/Data Science outside of the course, none of the course material to date trained us on how to interpret the reference material. The final lab should be challenging, but not to the extent where the material presented doesn't provide the method for solving these problems. The curriculum designer should take this into account when building these classes.

Lastly, the tooling used for the course did not work for over a month. This added hours to my training. It also meant that I was never able to complete some of the lab exercises, or use the completed material as a reference for the final exam work. The Coursera help desk was not at all helpful in informing students of the issue and when it would be resolved, and only gave advice to try to recreate the same exercises (without the supporting code) in other environments. This added many hours to the time that I needed to complete the exercise.

By Joshua W

May 21, 2019

A lot of the work in the final project was not in the course (in either the content or the lab work). There were plenty of topics covered that could have formed a challenging final project without asking us to do things that we weren't equipped to do by the course. Fortunately, I was able to find what I needed but after putting all of that work into the course and labs, I shouldn't have had to spend as many hours on the final project as I did. If those are things I needed to know, then the content was inadequate. If I didn't need to know those things, the project was poorly created.

By Clinton

Jun 21, 2019

so far ive spent the most time on this course . This course has around the shortest estimated time to complete. The number of discussions in week 3 is around 5 times more than the Python for Data Analysis course.... why we may ask?

The plotting of views are overly dependent on syntax. The information gain in trying to figure out that syntax is negative, i.e. up until this course i was enjoying my first experience with python. 12 hours later, i cant get a chloropleth chart to work because something as minor as column orders were incorrect. Very frustrating!

That said, perhaps im spoilt. Im a tableau user and its fairly straightforward to do data visualisation. It is rewarding. and flexible.

The bar is thus set, and so far data viz in python is frustrating.

By Nick

Jun 7, 2019

No mid-lecture quizzes

End of section quizzes test rote memorization

Narration is poorly done

By k b

Feb 8, 2021

Kindly revise the course content and match it with the final project. And definitely re-record videos without annoying voice of the instructor and repetitive sentences about the data.

And seaborn should be mentioned more than just 2 -3 minutes video.

Course does not reflect the quality of the IBM courses.

By Guillermo M

Aug 18, 2018

ZERO support from our teachers, assignments that have little to do with what they teach us (Videos don't even have any information explaining core concepts) Most of the learning was done by Google. Quite annoying to be honest.

By Sisir K

Apr 24, 2019

A lot of functions and lines of code weren't explained they were just left to be figured out by the learner. While some lines of code could be understood without much explanation, others were too complex for people new to programming (which most people taking this course are).

By Lena L

Jun 8, 2020

This is the only course so far where the videos have not been helpful. They were repetitive-- we do not need to learn how to do the same transformation on the dataframe 10 times. The videos didn't show or explain any of the code like in the previous courses. The final assignment covered code we didn't even look at at all during this course.

By Shannon R J

Nov 13, 2019

This is by far the least helpful course in the IBM Data Science Professional Certificate series. The videos contain mostly repeated info, so you really only learn much of anything from the labs. But even the labs are very basic compared to what you are expected to do for the final project. If I am paying for a course to teach me something, I shouldn't be teaching myself with help from Google. I can do that on my own, for free. If the "help" offered by the teaching assistant in the forum is code that doesn't look even vaguely familiar right after going through the course, doing all the labs, and getting a 100% on every quiz, then there is a big problem. I would absolutely not recommend this course

By pawar p

Jul 10, 2019

Need more detailed explaination of artist layer. Very confusing. Questions on topics which are not covered in syllabus.

By Yiannis E

Jun 12, 2020

This was not a course. This was a "go get them tiger": the labs are there, go do them and come back for the assignment. And then, in the assignment there were features that we had to include in a chart that were not even hinted - let alone explained - anywhere in the course. If we the idea is that we must search for everything on the web, then the course should at least include references to websites where we can find relevant information. Back in my student days we called that "suggested reading". Some of the Multiple Choice questions were really annoying: do we really have to remember the first name of the creator of Matlab to become data scientists? Great Material, but a very frustrating overall experience since there was no teaching.

By Rachel H

Mar 8, 2020

A lot of information that was required to complete the assignment was missing. I had to look up lots of other sources to be able to complete it. I understand this maybe was the course setter wanted but it felt like the material was overlooked.

By ACTraveler

Apr 21, 2019

The course labs had broken links which caused issues with several of the students. The quizzes also had several question choices where two of the answer choices were the exact same, leaving the student to guess. Not to be so critical, although the datacamp classes are much more effective when it comes to learning.

Learner Reviews & Feedback for Data Visualization with Python Course | Coursera (2024)

FAQs

Is Python good for data visualization? ›

While Python isn't considered to be the best option for data visualization, we recommend it because of the scalability and flexibility on offer. The open-source nature of the programming language allows developers to work on it and bring data to life through visualizations.

Which data visualization tool is best for Python? ›

Best Python data visualization libraries in 2024
  • Altair. Altair is a free and open source Python library that is commonly used for interactive and statistical visualizations. ...
  • Plotly Dash. ...
  • Bokeh. ...
  • Matplotlib. ...
  • GGplot. ...
  • GGplot2 (Plotnine) ...
  • Seaborn. ...
  • Geoplotlib.
Aug 2, 2024

What are the qualities of Python regarding data visualization? ›

Python offers several plotting libraries, namely Matplotlib, Seaborn and many other such data visualization packages with different features for creating informative, customized, and appealing plots to present data in the most simple and effective way.

How long does it take to learn data visualization? ›

On average, the time it will take you to learn to visualize data can be broken down into separate skill sets. It takes most people five to ten weeks to learn how to use Python for data visualization, between 18-20 hours to learn Excel, and between two and six months to become proficient with Tableau.

What is the salary of Python data visualization? ›

$100,500 is the 25th percentile. Salaries below this are outliers. $138,500 is the 75th percentile.

Is it better to learn Python or Tableau? ›

Data transformation and cleaning are vital elements of any analysis process, and Python takes over these processes like no other. A tableau is also an outstanding tool for data analysis, but it is not very efficient in performing complex and intricate processes.

Why is Python better than Excel for data visualization? ›

Scalability: Python is a programming language that is designed to handle large datasets and perform complex data manipulation tasks. It can easily handle large amounts of data without slowing down or becoming unresponsive, unlike Excel, which can become slow and unresponsive when working with large datasets.

What is the most popular data visualization tool? ›

The best data visualization tools available include Google Charts, Tableau, Power BI, FusionCharts, Datawrapper, Infogram, Sisense, and more. These tools are recognized for their ability to support a variety of visual styles, their user-friendly interfaces, and their capacity to handle large volumes of data.

Which programming language is best for data visualization? ›

JavaScript is well-suited for data visualizations and interactive capabilities. Its regular updates and versatility make it a popular choice for web app development. Scala combines functional and object-oriented programming, making it ideal for handling large datasets.

Is data visualization a good course? ›

Proficiency in Python and data visualization is a highly sought-after skill in the job market, opening up career opportunities in data science, analytics, and related fields.

What is the goal of data visualization? ›

Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting useful information.

What are the advantages of visualization in Python? ›

One of the benefits of data visualization with Python is that there are many visualization tools/libraries that provide excellent features and are easy to implement. It includes support for all types of visual, live, customized charts.

What are the 7 stages of data visualization? ›

  • 1 6.
  • Step 1: Define a clear purpose.
  • Step 2: Know your audience.
  • Step 3: Keep visualizations simple.
  • Step 4: Choose the right visual.
  • Step 5: Make sure your visualizations are inclusive.
  • Step 6: Provide context.
  • Step 7: Make it actionable.

What are the 5 steps in data visualization? ›

  • Step 1 — Be clear on the question. ...
  • Step 2 — Know your data and start with basic visualizations. ...
  • Step 3 — Identify messages of the visualization, and generate the most informative.
  • Step 4 — Choose the right chart type. ...
  • Step 5 — Use color, size, scale, shapes and labels to direct attention to the key.

Which is better for data visualization R or Python? ›

R: R is much better than Python in terms of data visualizations. R was designed to display statistical analysis results, with the fundamental graphics module making it simple to build basic charts and plots. ggplot2 may also be used to create more advanced plots, such as complex scatter plots with regression lines.

Is Python enough for data analysis? ›

Despite the vast range of programming languages, most data analysts choose to work with Python. While some data analysts use other programming languages like Javascript, Scala, and MATLAB; Python remains the popular choice due to its flexibility, scalability, and impressive range of libraries.

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