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Exploratory Data Analysis with Seaborn(으)로 돌아가기

Coursera Project Network의 Exploratory Data Analysis with Seaborn 학습자 리뷰 및 피드백

362개의 평가
62개의 리뷰

강좌 소개

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....

최상위 리뷰


2020년 9월 7일

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.


2020년 10월 3일

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.

필터링 기준:

Exploratory Data Analysis with Seaborn의 62개 리뷰 중 26~50

교육 기관: Alex K

2020년 12월 7일

Well explained, hands-on course that is very practical for anyone wanting to get better at EDA.

교육 기관: ahmet s

2020년 12월 16일

Clean, simple and quick. I recommend this course to anyone who needs an insight from seaborn.

교육 기관: Shubham K K

2020년 9월 17일

Nice course telling the basic functionality of data visualization with python .

교육 기관: Adolf Y M

2020년 10월 10일

all is explained very well so i can get a good understanding of EDA

교육 기관: Minhaj A A

2020년 6월 1일

Good project to practice your visualisation skills using seaborn

교육 기관: Marwa A E

2020년 6월 13일

More than perfect course, amazing and simple. Thank you :)

교육 기관: DEEPA K

2020년 7월 28일

An easy to understand project with good explanation.

교육 기관: C S S

2020년 6월 8일

this was a beautiful learning experience!

thank you !

교육 기관: Cheikh B

2020년 7월 14일

very helpfull project to learn easier seaborn

교육 기관: Arnab B

2020년 9월 19일

Good instructions for absolute beginners

교육 기관: Serafeim L

2020년 10월 1일

Great overview of EDA with Seaborn

교육 기관: Hitesh J

2020년 7월 19일

optimal course for beginner

교육 기관: MRS. S D A

2020년 5월 30일

Hands on was very useful

교육 기관: Gangone R

2020년 6월 28일

very useful course

교육 기관: Asha N

2020년 6월 28일

It was helpful

교육 기관: XAVIER S M

2020년 6월 2일

Very Helpful !

교육 기관: Sachin

2020년 10월 26일


교육 기관: Mason L

2021년 12월 27일


교육 기관: Jitin S

2020년 9월 8일


교육 기관: Deepak K

2020년 11월 1일

Thank You


2020년 5월 24일

Good one

교육 기관: tale p

2020년 6월 17일


교육 기관: Abhishek P G

2020년 6월 14일


교육 기관: Gilsiley D

2020년 8월 3일

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.

교육 기관: Divya R

2020년 6월 30일

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!