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 4, 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 20, 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 6, 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 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 7, 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 20, 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

Learner Reviews & Feedback for Data Visualization with Python Course | Coursera? ›

Good course, very well structured and with interesting assignments. Some (especially first) lessons are more of a general culture but most are very helpful and allow to learn a lot of things. I found the class to be very informative.

Is python good for data visualization? ›

In this field, Python enthusiasts continue to advocate that Python offers some of the best data visualization libraries available, making data analysis quicker and easier than ever before.

How long does it take to learn data visualization? ›

The journey to becoming proficient in Data Visualization can vary, but typically it takes 1-3 years to develop strong foundational skills. This includes learning data visualization tools and principles, which can be achieved through dedicated study and practice.

How do I learn data visualization in python? ›

The process of finding trends and correlations in our data by representing it pictorially is called Data Visualization. To perform data visualization in python, we can use various python data visualization modules such as Matplotlib, Seaborn, Plotly, etc.

How do I learn data analytics and visualization? ›

3 Practice your skills

The best way to learn new data analysis and visualization skills is to apply them to real or simulated data sets and projects. This will help you consolidate your learning, test your understanding, and discover new insights and challenges.

What is the salary of Python data visualization? ›

Python Data Analysis Salary
Annual SalaryMonthly Pay
Top Earners$160,000$13,333
75th Percentile$138,500$11,541
Average$121,932$10,161
25th Percentile$100,500$8,375

Is it worth learning Python for data analysis? ›

Python and R are both excellent languages for data. They're also both appropriate for beginners with no previous coding experience. Luckily, no matter which language you choose to pursue first, you'll find a wide range of resources and materials to help you along the way.

Is data visualization a good course? ›

Data visualization skills are highly valued in various industries, including finance, healthcare, marketing, e-commerce, government, and journalism. These industries use effective data communication to drive insights, make informed decisions, and improve business performance.

Is data visualization hard to learn? ›

The ability to create stunning data visualizations requires time and training. Data visualization is a field that requires proficiency with various tools and applications like Excel and Tableau, each of which takes the average person weeks or months to learn.

Does data visualization pay well? ›

How much does a Data Visualization make? As of Mar 31, 2024, the average annual pay for a Data Visualization in the United States is $109,451 a year. Just in case you need a simple salary calculator, that works out to be approximately $52.62 an hour. This is the equivalent of $2,104/week or $9,120/month.

What is the best tool for data visualization in Python? ›

Below you will find 15 best examples of them.
  • Matplotlib. According to preliminary statistics, Matplotlib is currently the most frequently used data visualization library. ...
  • Seaborn. To work with static visualization, the library called Seaborn is an excellent choice. ...
  • Plotnine (ggplot) ...
  • Bokeh. ...
  • Pygal. ...
  • Plotly. ...
  • Geoplotlib. ...
  • Gleam.
Nov 2, 2023

What is the purpose of data visualization in Python? ›

Visualising those data is an essential part of understanding what the data say, as every scientist, data scientist, and anyone who works with data will confirm. Displaying data visually is important for those studying the data and also for those to whom the data is presented.

Why is data visualization important in Python? ›

In the world of data science, visualizing data is like creating a map that helps scientists find patterns, spot problems, and tell a story about the data to others. It turns rows of numbers into images that can quickly tell us what's happening. It's very important because it helps in understanding the data easily.

What is the best data visualization course? ›

  • IBM. Data Visualization with Python. ...
  • University of California, Davis. Data Visualization with Tableau. ...
  • Google. Share Data Through the Art of Visualization. ...
  • Coursera Project Network. Overview of Data Visualization. ...
  • Google. Google Data Analytics. ...
  • University of Illinois at Urbana-Champaign. Data Visualization. ...
  • IBM. ...
  • PwC.

What is the best way to learn data visualization? ›

Key Insights

Some common skills individuals may choose to learn before embarking on data visualization study are data analytics, design, and storytelling. Enrolling in one of Noble Desktop's in-person or live online data visualization classes is a great way to learn more about data visualization.

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 Tableau or Python? ›

Tableau works best with simple tables and charts like single tables or multiple tables with diverse combinations. It is the best choice for quick and straightforward data visualizations that help analyze and rectify the issues. Python, although it is the best when it comes to dealing with streaming data.

Why use Python instead of 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.

Which programming language is best for data visualization? ›

Data Analysts typically need a language that's intuitive to learn, easy to work with, has interactive capabilities, and includes libraries that are suited to creating dynamic data visualizations. Five of the most popular programming languages in 2021 for Data Analysts are Python, SQL, R, JavaScript, and Scala.

Why is Python better than Excel for data visualization? ›

Python code is reproducible and compatible, which makes it suitable for further manipulation by other contributors who are running independent projects. Unlike the VBA language used in Excel, data analysis using Python is cleaner and provides better version control.

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