Identify and interpret inherent quantitative relationships in datasets
Produce and customize various chart types with Seaborn in Python
Apply graphical techniques in exploratory data analysis (EDA)
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.
Some experience in programming in Python.
Introduction and Importing Data
Separate Target from Features
Diagnosis Distribution Visualization
Visualizing Standardized Data with Seaborn
Violin Plots and Box Plots
Use Joint Plots for Feature Comparison
Observing Distributions and their Variance with Swarm Plots
Obtaining all Pairwise Correlations
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.
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.
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
This was my first guided project . It was a nice experience and the course material was truly helpful for me. The instructor's pace of teaching was absolutely stunning.
指导 项目 可在台式设备和移动设备上学习吗？
指导 项目 的讲师是谁？
指导 项目 讲师是特定领域的专家，他们在项目的技能、工具或领域方面经验丰富，并且热衷于分享自己的知识以影响全球数百万的学生。
我能在完成指导 项目 后从中下载作品吗？
您可以从指导 项目 中下载并保留您创建的任何文件。为此，您可以在访问云桌面时使用'文件浏览器'功能。
您可在页面顶部点按此指导 项目 的经验级别，查看任何知识先决条件。对于指导 项目 的每个级别，您的讲师会逐步为您提供指导。
我能直接通过 Web 浏览器来完成此指导 项目，而不必安装特殊软件吗？
是，您可以在浏览器的云桌面中获得完成指导 项目 所需的一切。
指导 项目 的学习体验如何？