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Applied Plotting, Charting & Data Representation in Python

This course is a part of Applied Data Science with Python, a 5-course Specialization series from Coursera.

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

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University of Michigan

Rating 4.1 based on 277 ratings
Length 5 weeks
Starts Apr 29 (in 2 days)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science Art & Design
Tags Computer Science Data Science Data Analysis Design And Product

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

data science in 19 reviews

I didn't learn nearly as much as I hoped and will end up reviewing Udemy's Python for Data Science and Machine Learning Bootcamp for more material on charting in Python.

I love the way prof. Christofer Brooks teach Data Science.

I completed the first course in the Applied Data Science series and found it useful.

Besides, the design of practice is not very good as well as first class 'Introduction to Data Science in Python'.

In my work I have plenty of opportunity to apply data science and very little knowledge on how to.

If you do love data science, and apply what you learn on the course, with some grit and perseverance, you'll prevail.

The previous course in this specialization (intro to data science in python) by comparison has many more guided practice exercises, and I am disappointed that this course does not live up to the standard set by the first course.

The lecturer is amazing and I learned tons of information about publication quality charts and data cleaning.It takes plenty of time though.I recommend this course to anyone serious in learning data science.

peer review in 11 reviews

The homeworks are all peer reviewed with the grading criteria so broad it doesn't take much to get full credit.

Assignments were good, but peer reviewing was not always great; I wish to not be graded by peers who do not follow instructions and give poor grade because they do not understand the course content and/or just out of spite.

Good but I feel not comfortable with peer reviewing...

I don't really like the peer review aspects but I think they are appropriate for these sort of graphical courses It is a nice course !

I'm okay with the peer review system.

Also, I enjoy the peer review assignment.

If you enjoy reading academic papers and peer reviews then this course is for you.

I also really like the peer reviewed assignments.

too much in 8 reviews

Far too much time was spent on the philosophy of visualizations--what makes a visualization interesting/useful.

It relies too much in peer grades assignments but the overall content is good.

But the reference to Alberto Cairo's work doesn't help too much to enhance the quality of the course , since it's difficult to understand how he can compare journalism at Spain and at Venezuela.

On the other hand, it required perhaps too much work.

I really dislike the peer-graded assignmentsToo much of the course is unstructuredI dislike being assigned a region and topic for the final projectI would prefer to dive less into interactivity and focus more on practicing essential plotting skills over and over again.

The course left too much of the work to the student.

However, there was a bit too much theory (Cairos principles etc) for my taste.

I think there there is too much time given to the esoteric of what makes plots pretty rather than the nuts and bolts of how to do it and the limitations of using Pandas and Matplotlib for real world data I found the lectures interesting and thorough yet short and to the point.

my work in 5 reviews

I learned something that helps with my work.

The people that graded my work were great but I don't expect them to engage my work in a very meaningful way.

Didn't like peer grading as it introduces delays in grade and peers don't have competency to judge my work.Also, the course should be more focused on technical matters.

nor did I get much in the way of helpful info from the people who peer reviewed my work.

worth it in 5 reviews

It is worth it tho, so keep going!

If you are taking the complete specialization, as I am, then I guess it's worth it and I hope the next courses in the series have more material.

I would say its worth it to do this course if you have not had any exposure to Matplotlib or seaborn, but if you've done any significant using those then this course will feel a bit underwhelming.

Requires a lot of work but well worth it.

But overall, very well worth it!

pretty good in 5 reviews

Assignments are pretty good, but I would suggest extracting useful info from discussion forum to the assignment page.

This course is pretty good.

useful The video guide is pretty good, it shows you a lot of thing that you need to learn.

I think the professors can elaborate better the course pretty good touch on the fundamentals of charting using matplotlib~great course in the series as usual... thank you!

This a pretty good introduction to plotting libraries in python.

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

AD, Data Science $47k

Associate Data Science Supervisor $55k

Science writer / data analyst $63k

Genomic Data Science Programmer $75k

Volunteer Director of Data Science $78k

Expert Data Science Supervisor $79k

Supervisor 1 Data Science Supervisor $91k

Guest Director of Data Science $101k

Data Science Architect $105k

Head of Data Science $131k

Assistant Director 1 of Data Science $133k

Owner Director of Data Science $149k

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Coursera

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University of Michigan

Rating 4.1 based on 277 ratings
Length 5 weeks
Starts Apr 29 (in 2 days)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science Art & Design
Tags Computer Science Data Science Data Analysis Design And Product

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