Exploratory Data Analysis with Seaborn

4.6
별점
308개의 평가
제공자:
Coursera Project Network
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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)

Clock1.5 hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Data ScienceMachine LearningPython ProgrammingData AnalysisData Visualization (DataViz)

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Introduction and Importing Data

  2. Separate Target from Features

  3. Diagnosis Distribution Visualization

  4. Visualizing Standardized Data with Seaborn

  5. Violin Plots and Box Plots

  6. Use Joint Plots for Feature Comparison

  7. Observing Distributions and their Variance with Swarm Plots

  8. Obtaining all Pairwise Correlations

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

EXPLORATORY DATA ANALYSIS WITH SEABORN의 최상위 리뷰

모든 리뷰 보기

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