Data science with R: tidyverse

Data Science skills are highly sought after – but what does it really take? While the exciting aspects of data science often get the spotlight – like uncovering data insights, building predictive models, and applying machine learning algorithms – the core of most projects involves time-consuming data preparation. Think data importing, data cleaning, data wrangling, and data exploration. That's where powerful tools become essential.

Enter R, a leading programming language for applied statistics and data science. Combined with the tidyverse collection of R libraries, you unlock a potent toolkit specifically designed for efficient data manipulation and analysis. The tidyverse libraries share a consistent philosophy, grammar, and data types, allowing for seamless integration and optimized R code. This means you can tackle data challenges faster and more effectively.

This comprehensive course dives deep into practical data science tasks using R and the tidyverse. You'll gain both theoretical understanding and hands-on experience with real-world data challenges. Here's a glimpse of what you'll learn:

• Data Cleaning with Tidyverse: Master data cleaning techniques using the tidyverse suite.
• Data Wrangling with dplyr and tidyr: Learn the grammar of data wrangling and efficiently manipulate data frames.
• Tibbles: Create and work with tibbles, modern table-like objects in R.
• Data Import & Parsing: Import and parse data from various sources using readr and other libraries.
• String Manipulation: Effectively handle strings in R with stringr, including regular expressions.
• Categorical Variables: Work with categorical data using forcats.
• Data Visualization with ggplot2: Explore data and create compelling statistical plots.
• Functional Programming with purrr: Utilize functional programming concepts and map functions for efficient data processing.
• Working with Lists: Manage and manipulate lists effectively using purrr.
• Relational Data: Apply dplyr techniques to work with relational data.
• Tidy Evaluation: Understand and utilize tidy evaluation within the tidyverse.
• Final Data Science Project: Apply all learned skills to a comprehensive practical data science project.

Course Features:

• 25+ hours of engaging lecture videos.
• Complete R scripts and supplementary data for practical application.
• Chapter-end assignments to reinforce learning.
• Assignment walkthrough videos to guide you through the solutions.

This course is designed to equip you with the skills to become proficient in R and the tidyverse for tackling real-world data science problems. Enroll today and become a master of R's tidyverse!