Starting at USD 39.00
4 hours duration

Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.

This course focuses on overcoming common data problems, specifically the removal of duplicates, using the R programming language. Data scientists often spend a significant amount of time cleaning and manipulating data, as it is crucial for accurate analysis. By learning various techniques in this course, you will be able to effectively clean dirty data using R. The course begins by teaching you how to convert data types, apply range constraints, and handle both full and partial duplicates. These techniques will help you avoid double-counting and ensure the accuracy of your analysis. Once you have mastered the basics, the course progresses to more advanced challenges, such as maintaining consistency in measurements and dealing with missing data. Each new concept is reinforced through hands-on exercises, allowing you to solidify your understanding and gain practical experience. In the final chapter, you will learn about record linkage, a technique used to merge datasets with issues like typos or different spellings. You will explore the application of record linkage by joining two restaurant review datasets into a single dataset. By completing this course, you will acquire the skills necessary to overcome common data problems and enhance the quality of your analysis using R.

About Provider

Online Education Provider ยท 410 courses
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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