This course provides a great opportunity for new learners to learn how to clean data in R with Tidyverse, Dplyr, Data.table, Tidyr and other packages. Learners will gain an understanding of how to identify outliers, replace missing data, and use machine learning algorithms to clean data. At the end of the course, learners will be able to apply their knowledge to a data cleaning project and receive a course certificate from Udemy. This course is perfect for those who want to learn the basics of data cleaning and gain the skills to apply it in their own projects.
Data Cleaning in Python
4.6
Udemy2,269 learners
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This course on Data Cleaning in Python is perfect for beginners who want to learn the preprocessing steps to improve the validity, accuracy, completeness, consistency and uniformity of data. It covers common problems with data such as missing values, noise values, outliers, data duplication, standardizing and normalizing data, and dealing with categorical features. The course provides theoretical explanation, mathematical evaluation and code for each concept, all written in Python using Jupyter Notebook. Click now to learn the data cleaning skills and make useful analysis with your business data!
Practical Data Cleaning
3.8
Udemy2,444 learners
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This Practical Data Cleaning video course is perfect for new learners who want to understand the basics of data cleaning. It covers topics such as data collection, data cleaning, data classification, data integrity, and how to organize datasets. Learners will gain tips and tricks to make data cleaning processes simpler, faster, and more effective. By the end of the course, they will have the knowledge and skills to become more productive and get their results faster. So, if you're looking to learn the basics of data cleaning, this course is for you!
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