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.
在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:
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
您的工作空间就是浏览器中的云桌面,无需下载
在分屏视频中,您的授课教师会为您提供分步指导
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.
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.
The course is a great course for a data scientist! Very practical and I like the way the instructor explains the concept and the interpretation of the data.
如果我购买指导项目,会得到什么?
购买指导项目后,您将获得完成指导项目所需的一切,包括通过 Web 浏览器访问云桌面工作空间,工作空间中包含您需要了解的文件和软件,以及特定领域的专家提供的分步视频说明。
指导项目可在台式设备和移动设备上学习吗?
由于您的工作空间包含适合笔记本电脑或台式计算机使用的云桌面,因此指导项目不在移动设备上提供。
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。
我能够退款吗?退款政策是如何规定的?
指导项目不符合退款条件。请查看我们完整的退款政策。
有助学金吗?
指导项目不提供助学金。
我能旁听指导项目并免费观看视频部分吗?
指导项目不支持旁听。
我需要具备多少经验才能做这个指导项目?
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?
您可以直接在浏览器中于分屏环境下完成任务,以此从做中学。在屏幕的左侧,您将在工作空间中完成任务。在屏幕的右侧,您将看到有讲师逐步指导您完成项目。
还有其他问题吗?请访问 学生帮助中心。