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Coursera Project Network による Exploratory Data Analysis with Seaborn の受講者のレビューおよびフィードバック

4.6
384件の評価
66件のレビュー

コースについて

Producing visualizations is an important first step in exploring and analyzing real-world data sets. As such, visualization is an indispensable method in any data scientist's toolbox. It is also a powerful tool to identify problems in analyses and for illustrating results.In this project-based course, we will employ the statistical data visualization library, Seaborn, to discover and explore the relationships in the Breast Cancer Wisconsin (Diagnostic) Data Set. We will cover key concepts in exploratory data analysis (EDA) using visualizations to identify and interpret inherent relationships in the data set, produce various chart types including histograms, violin plots, box plots, joint plots, pair grids, and heatmaps, customize plot aesthetics and apply faceting methods to visualize higher dimensional data. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

人気のレビュー

HP

2020年9月7日

This project is great for people go want to advances her career exploring new viz techniques. The instructor is great, clear and easy to follow. I will definitely recommend to take this project.

PG

2020年10月3日

As a beginner, this was a very good insight into EDA for me. You will however, have to read the documentation and more articles to go in-depth. However, this is a very good introductory course.

フィルター:

Exploratory Data Analysis with Seaborn: 51 - 66 / 66 レビュー

by Gilsiley D

2020年8月3日

A basic course. For me show a good idea about exploratory data analysis and some important insights about using some graphics like violin, swarm and heatmap. I felt absence a conclusion, like show a final features can be selected.

by Divya R

2020年6月30日

I find this project a way better means to explore data analysis than the month long courses. This was a good quick refresher for me. Beautiful project, i hope to see many such more projects from the Mentor!

by Johan R

2021年4月29日

I think it would be nice if I could play the video while using jupyter on my computer, it was a bit annoying to use the virtual machine, since if it was not on the page the video stopped

by Rui L

2020年5月19日

A good tutorial for starters in Data Science. All knowledge taught in it is some basic.

by ajinkya a

2022年7月30日

Understanding Seaborn plots was helpful

by Vishnu N S

2020年12月9日

GOOD start.. more api could be added

by Anil S

2020年5月30日

Good Course with clear instructions

by Anirudha S

2021年5月23日

Thank you. It was great.

by Raj v

2020年7月14日

Explanations could have been more detailed. Parameters should be explained.

by Nikhil A

2020年7月3日

Should have used more plots only 5 were there,but it was good

by Andrea C

2020年11月20日

Good course for beginners, not for intermediate learners

by Amrendra P S

2020年8月30日

Didn't learn much in this course. Just write the same code as explained in the videos and also the Instructor was not explaining thing in deep. This course I found worthless. It will be better if I had done some other courses rather than devoting my time for this

by Linyu W

2020年8月10日

Not specific or even systematic. There're supposed to be more function in seaborn usefel for EDA

by Amay S K

2022年7月30日

No complementary course

by Alireza R

2021年4月17日

Very basic!

by Fuat A

2020年6月1日

Not worth the money.