MyPhatshala

Data Cleaning and Preprocessing

Real data is messy. Learn to fix it before you analyse it.

Beginner to Intermediate6 modules17 lessonsFree · no sign-up
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Syllabus

01 IntroductionWhy cleaning matters. 3 lessons
  1. 1.1What is data cleaning?Coming soon
  2. 1.2Types of dirty dataComing soon
  3. 1.3The data cleaning workflowComing soon
02 Missing DataHandling gaps. 3 lessons
  1. 2.1Detecting missing valuesComing soon
  2. 2.2Dropping vs imputingComing soon
  3. 2.3Imputation techniquesComing soon
03 Fixing ValuesMaking data consistent. 4 lessons
  1. 3.1Removing duplicatesComing soon
  2. 3.2Fixing data typesComing soon
  3. 3.3Standardising text and categoriesComing soon
  4. 3.4Handling dates and timesComing soon
04 OutliersUnusual values. 2 lessons
  1. 4.1Detecting outliers: IQR and z-scoreComing soon
  2. 4.2Treating outliersComing soon
05 TransformationGetting data ready for analysis. 4 lessons
  1. 5.1Scaling and normalisationComing soon
  2. 5.2Encoding categorical dataComing soon
  3. 5.3Feature engineering basicsComing soon
  4. 5.4Merging and reshaping datasetsComing soon
06 ProjectEnd to end. 1 lessons
  1. 6.1Cleaning a real-world dataset step by stepComing soon