Dealing With Missing Data in R

Social Sciences

Short Description

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Long Description

Missing data is a common occurrence in real-world data analysis, often appearing in unexpected areas and complicating the understanding of analyses. This course aims to equip you with the skills to effectively handle missing values using tidyverse tools and the naniar R package. Through visualization techniques, you will learn to identify and analyze missing values, uncovering potential biases within the data. Additionally, you will explore underlying patterns of missingness. Furthermore, you will gain knowledge on imputation models, enabling you to fill in missing values and make informed decisions based on these imputed datasets.

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Course Details

Duration: 4 hours
Format: Short Course
Price: USD39.00
Course Link

DataCamp

DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.

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