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学生对 Coursera Project Network 提供的 Exploratory Data Analysis with Seaborn 的评价和反馈

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402 个评分

课程概述

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....

热门审阅

HP

Sep 7, 2020

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

Oct 3, 2020

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.

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1 - Exploratory Data Analysis with Seaborn 的 25 个评论(共 67 个)

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