Mining Data to Extract and Visualize Insights in Python

4.3
54 個評分
提供方
Coursera Project Network
2,101 人已註冊
在此指導項目中,您將:

Learn how to clean and extract useful information from your dataset in Python

Learn how to create several different types of visualizations to identify patterns, outliers, and correlations in your dataset

Learn how to visualize a highly dimensional dataset using principal component analysis (PCA)

Clock1.5 hours
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

By the end of this project, you will learn how to load and extract useful information from your dataset using Python, a free, open-source program that you can download. You will then learn how to clean your data set by removing unwanted whitespaces, columns containing several empty values, rows containing empty column values and duplicated row entries. Next, you will create various visualizations to identify patterns and outliers in your dataset, and visualize correlations between different columns. Lastly, you will learn how to visualize a highly dimensional dataset using principal component analysis (PCA). These steps are part of exploratory data analysis that you will need to carry out for any data science project to help you understand your dataset. Note: 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.

您要培養的技能

Python ProgrammingInsightsData Visualization (DataViz)Data Mining

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Load a dataset and extract basic information using Python

  2. Learn various ways to clean your dataset

  3. Visualize patterns and outliers that may be present in your dataset

  4. Calculate and visualize the correlation between different numeric columns

  5. Cluster your dataset to identify similar groups

  6. Visualize your dataset using principal component analysis (PCA)

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心