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Updated in [July 27th, 2023]

This course provides learners with an in-depth understanding of data cleaning techniques in Data Science & Machine Learning. It covers topics such as data reading, merging or splitting datasets, different visualization tools, locating or handling missing/absurd values, and hands-on sessions. Learners will gain an understanding of why data cleaning is important, and how it can improve decision making, efficiency, and productivity. The course also provides learners with the opportunity to practice their data cleaning skills with a dataset. Upon completion of this course, learners will have the knowledge and skills necessary to effectively clean data for Data Science & Machine Learning.

Course Syllabus

Introduction

Playing with the Data

Variables and Correlations

Missing Values and Outliers

Exercises