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.
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.
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.
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。
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？