Advanced Data Manipulation in R: Elevate Your Skills with Free Udemy Course

Enroll in this Free Udemy Course on R Programming to master data manipulation skills for impactful analysis!

In today’s data-driven world, mastering data manipulation is one of the most valuable skills you can acquire. This comprehensive and practical course in R programming focuses on advanced data manipulation techniques, guiding you from foundational concepts to sophisticated applications relevant in real-world scenarios. You will delve into the powerful Tidyverse packages, including dplyr and tidyr, as well as tackle the versatile data.table library. By comparing these modern approaches to Base R, you’ll understand their strengths and optimal use cases, enhancing your ability to work with diverse datasets.

Throughout the course, you will learn how to effectively import data from various sources, such as CSV and SQL databases, create new variables, and efficiently prepare your data for analysis. The hands-on exercises and real-case examples provided will solidify your understanding of transforming, filtering, ordering, and combining datasets. You will also engage with practical projects, including financial data analysis and social media data integration, culminating in the development of a comprehensive data cleaning and transformation pipeline designed for machine learning models.

This course is meant for both beginners and those with experience looking to refine their data wrangling skills. With a structured, up-to-date approach, you will gain the competencies needed to become an expert in R programming for data manipulation, ensuring you are well-equipped to handle any data challenges you may face.

What you will learn:

  • Understand the fundamentals of data manipulation in R and its relevance to data analysis.
  • Compare and apply different approaches: Base R, Tidyverse, and data.table.
  • Use functions like select(), filter(), mutate(), summarise(), pivotlonger(), and pivotwider() to transform datasets.

Course Content:

  • Sections: 9 sections
  • Lectures: 39 lectures
  • Duration: 5h 16m

Requirements:

  • No prior knowledge of R or statistics is required.
  • Basic programming knowledge or an interest in data analysis is preferred.
  • R and RStudio should be installed (installation explained in the course).
  • A desire to learn and practice with real datasets.

Who is it for?

  • Students, professionals, and self-learners interested in data science or statistical analysis.
  • Individuals working with large volumes of information looking to automate repetitive tasks.
  • Aspiring data scientists wishing to master R as a data manipulation tool.
  • Researchers, analysts, and developers needing to prepare data for machine learning or visualization projects.

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