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Data Visualization with Python

Applied Data Science,

One of the most important skills of successful data scientists and data analysts is the ability to tell a compelling story by visualizing data and findings in an approachable and stimulating way. In this course you will learn many ways to effectively visualize both small and large-scale data. You will be able to take data that at first glance has little meaning and present that data in a form that conveys insights. This course will teach you to work with many Data Visualization tools and techniques. You will learn to create various types of basic and advanced graphs and charts like: Waffle Charts, Area Plots, Histograms, Bar Charts, Pie Charts, Scatter Plots, Word Clouds, Choropleth Maps, and many more! You will also create interactive dashboards that allow even those without any Data Science experience to better understand data, and make more effective and informed decisions. You will learn hands-on by completing numerous labs and a final project to practice and apply the many aspects and techniques of Data Visualization using Jupyter Notebooks and a Cloud-based IDE. You will use several data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly & Dash.

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Rating 4.1 based on 627 ratings
Length 6 weeks
Effort 3 weeks of study, 4-5 hours/week
Starts Jun 26 (51 weeks ago)
Cost $38
From IBM, IBM Skills Network via Coursera
Instructors Alex Aklson, Saishruthi Swaminathan
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science
Tags Data Science Data Analysis

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What people are saying

ibm data science

It is quite difficult and challenging course but useful and important one Amazing course with good level of problems to solve Once again, quality hands-on labs were the highlight of this course (as has been the case throughout the IBM Data Science Certificate courses).

This was the most challenging course thus far in the IBM Data Science concentration.

Poor study material compared to the assignment i took a long break and still managed to come back and finish 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.

I didn't want to give one star to the course, because all of the other courses from IBM Data Science certification are so good, but unfortunately this course definitively doesn't scores higher than that, because I had to study on matplotlib and other libraries documentations in order to do the exercises.

Many technical issues with the final project and no one cares... Thankyou Coursera The most exciting part yet it was from the IBM Data Science Professional Certification.

some of the exercises were not able to load for me great Although the idea of this course is good, it didn't have the same flow as the other IBM courses in the IBM Data Science Professional Certificate.

This is by far the least helpful course in the IBM Data Science Professional Certificate series.

I think the teaching in the other IBM data science courses is far better than this one, hopefully they improve this one.

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lot of time

Really, I spent a lot of time on them.

It did cost me a lot of time.The content and lessons and exercises and the lab itself is very good and interesting.

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.

Great course.I struggled a lot on the bar chart assignement and spent a lot of time reading documentatation.

Like others, I spent lot of time searching online, and it took me a full afternoon to complete that part of the final assignment !To summarize: it's a very important and interesting course, but video lessons should be re-recorded with deleting all parts repeating the initial database processing, and adding more topics such as artist layers, etc.

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figure out

The information gain in trying to figure out that syntax is negative, i.e.

Course is really helpful for indulging someone into data visualization but sometimes in the lab some stuff is just present for you to figure out yourself.

Also a lot of the things you had to know you had to figure out on your own versus finding it in the material presented.

Since students weren't able to submit code, this made it extremely difficult to answer the final project (which I couldn't figure out how to finish).

While the course was very good and showed lots of different data visualizations available in Python, the final assignment required topics that were just briefly touched upon and required quite a bit of outside research to figure out the syntax most of the material explains bits and pieces.

Although I will say for me, it took me hours to figure out the box plot, the little no-line nuances, etc.

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artist layer

1) Quite a lot of contents of the final assignment are not covered in labs/videos2) Huge portion of videos is redundancy and repitition3) Labs cannot be exported to HTML Need more detailed explaination of artist layer.

It did not get that into the Artist Layer of Matplotlib but the final project requires the student to use it.

Maybe it would be useful to re-organize the labs ?...-Week 3: as everyone mentions, the "artist layer" method is only briefly covered in one of the lab.

Few more sample exercises on the features of map, artist layer could have been useful.

Great hands-on experience 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.

The part of using the artist layer is a little ambiguous.

Now after I finished the course I don't feel that I know clearly the difference between using the artist layer or using the scripting layer.

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lab sessions

I would recommend that there should be more contents in the lecture videos and the lab sessions.

Which is fine, difficult is fine, as long as the knowledge on how to solve it is provided by you in the lecture notes\videos\lab sessions.

The exercises proposed are not trivial Very wisely chosen content of ungraded lab assignments Better to read matplotlib and folium documentation and look for answers from stackoverflow.This course didn't offer enough components of matplotlib and folium.This offers lab sessions but they are not main contents.Final assignments are impossible for someone who just takes only this course.

Do practice all ungraded lab sessions.

Extremely Good The final assignment is more difficult compared to what is taught in the videos and lab sessions.

Great lectures and lab sessions!!

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data analysis

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.

this course should come before data analysis with python great course Really Nice Course.

After struggling with the lab of week 3, I found out and took the Data Analysis Module.

lol This course and the following course "Data Analysis with Python" should be switched.

It's mentioned that "Data Analysis with Python" should be completed before this one but they are in the reverse order.

Will be good if in video will be some quick quiz as in "Data Analysis with Python" and "Python for Data Science and AI" NICE MENTORED COURSE This was a lot of fun learning to plot crime statistics of districts over a map.

The Data Analysis Python course is much better and explains similar concepts.

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learning experience

Some of the notebooks given did not function correctly, overall did not ruin the learning experience thought.

Notebooks are very practical A great learning experience!

Wonderful contents and learning experience, especially creating/annotating Earth map and making word cloud.

Does the job of a good introduction.Very limited and restrictive practice and assinments.For a true learning experience one needs to do a lot of external research and work to show a measureable benifit.

Had a great learning experience!

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well done

Well done labs.

I have a long way to go but this class was very well done.

Nice Overall a good course, especially the final assignment is well done.

Practice and learning deep A well done course that really gives a decent overview and exposure to the python tools for data visualization.

One of the best courses on data visualisation I have ever taken The project was well done.

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rather than

I would have preferred more content/exposure to other libraries rather than the redundant "data recaps" at the beginning of almost every video.

The instructor focused on providing very advanced content rather than simplifying the conpects in order to make it easy for the student to digest, and follow.

Course is great but the part with Folium should be separate course (some Advanced Visualization) rather than graded part of this corse.

Others and I ended up using other sources as a way to get the results Coursera was wanting, rather than what Coursera taught us.

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previous courses

The long chunks of code presented here are mostly opaque if all you have are the previous courses in this specialization.

Excellent course, especially the labs were very useful and served as a recap of what was learnt in the previous courses of this certification course.

PRO:Content CONrecurring technical issues (data on final assignment not available )description of final assignment not clear / inconsistent to previous courses, no jupyter notebook provided This course helps every data enthusiasts to upgrade their data visualization and data story telling skills.

Keep in mind I've done previous courses in this IBM professional certification.

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little bit

However, I feel the video lectures were a bit too brief and could have tried to explain the technical concepts a little bit more.

Just mentioned everything without going a little bit deeper.

excelente nice course but little bit of complex course.

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each video

Also, for some reason, 1/3 of each video was exactly the same clip recalling the dataset.

On the other hand, each video was a couple of minutes long so no big deal in the end.

I agree with the feedback from other people, reminding us one or two times is fine, but in each video...

In each video we transform dataset and it take more 1 minute for each video.

But there're some replication for each video about "recap of the data".

The videos were extremely short & repetitive explaining the same dataset in each video.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Data Visualization Scientist $51k

Information & Data Visualization Designer $59k

Visualization Designer $63k

Data Visualization $64k

3D Visualization Specialist $67k

Business Analyst - Data Visualization $70k

Data Visualization Specialist $73k

Data Visualization Architect $81k

Visualization Developer $99k

Data Scientist / Data Visualization $107k

Senior Data Visualization Analyst $123k

Principal Data Visualization Engineer $167k

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Rating 4.1 based on 627 ratings
Length 6 weeks
Effort 3 weeks of study, 4-5 hours/week
Starts Jun 26 (51 weeks ago)
Cost $38
From IBM, IBM Skills Network via Coursera
Instructors Alex Aklson, Saishruthi Swaminathan
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science
Tags Data Science Data Analysis

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