Cleaning, Reshaping, and Expanding Datasets in Python

4.2
13 個評分
提供方
Coursera Project Network
在此指導項目中,您將:

Clean datasets by dropping features and variables that have low variance or single values or that are extraneous and removing outliers

Reshape Data in Python by reordering, combining, splitting, stacking, expanding, or squeezing dimensions

Implement Feature Scaling and Normalization

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

It has been said that obtaining and cleaning data constitutes 80% of a data scientists job. Whether it's correcting or replacing missing data, removing duplicate entries, or dealing with outliers, our datasets always require some level of cleaning and reshaping. Doing so improves the accuracy of our results immensely. In this 2 hour project-based course, we will examine a variety of methods to clean, and reshape any 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

Missing DataData ReshapingData Pre-ProcessingData CleansingData Preparation

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. Inspect and Diagnose a Dataset

  2. Dealing with Missing and Extraneous Data

  3. Reshaping, Scaling, and Normalizing Datasets

  4. Merging Datasets

  5. Joining and Concatenating datasets

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

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