What you'll learn
How to use R's tidyverse libraries in your data science projects
How to write efficient R code for data science related tasks
What is clean data
How to clean your data with R
What is grammar of data wrangling
How to wrangle data with dplyr and tidyr
How to import data into R
How to properly parse imported data
How to chain R's functions into a pipeline
How to manipulate strings
What are Regular Expressions
How to use stringr library with Regular Expressions
How to use forcats library to manipulate categorical variables
What is Grammar of Graphics
How to visualize data with ggDescription2 library
What is functional programing
How to use purrr library for mapping functions, nesting data, manipulating lists, etc.
What is relational data
How to use dplyr library for relational data
What is tidy evaluation
How to use tidyverse tools to finish a practical project

Description
Data Science skills are still one of the most in-demand skills on the job market today. Many people see only the fun part of data science, tasks like: "search for data insight", "reveal the hidden truth behind the data", "build predictive models", "apply machine learning algorithms", and so on. The reality, which is known to most data scientists, is, that when you deal with real data, the most time-consuming operations of any data science project are: "data importing", "data cleaning", "data wrangling", "data exploring" and so on. So it is necessary to have an adequate tool for addressing given data-related tasks. What if I say, there is a freely accessible tool, that falls into the provided description above!

R is one of the most in-demand programming languages when it comes to applied statistics, data science, data exploration, etc. If you combine R with R's collection of libraries called tidyverse, you get one of the deadliest tools, which was designed for data science-related tasks. All tidyverse libraries share a unique philosophy, grammar, and data types. Therefore libraries can be used side by side, and enable you to write efficient and more optimized R code, which will help you finish projects faster.

This course includes several chapters, each chapter introduces different aspects of data-related tasks, with the proper tidyverse tool to help you deal with a given task. Also, the course brings to the table theory related to the topic, and practical examples, which are covered in R. If you dive into the course, you will be engaged with many different data science challenges, here are just a few of them from the course:

Tidy data, how to clean your data with tidyverse?

Grammar of data wrangling.

How to wrangle data with dplyr and tidyr.

Create table-like objects called tibble.

Import and parse data with readr and other libraries.

Deal with strings in R using stringr.

Apply Regular Expressions concepts when dealing with strings.

Deal with categorical variables using forcats.

Grammar of Data Visualization.

Explore data and draw statistical Descriptions using ggDescription2.

Use concepts of functional programming, and map functions using purrr.

Efficiently deal with lists with the help of purrr.

Practical applications of relational data.

Use dplyr for relational data.

Tidy evaluation inside tidyverse.

Apply tidyverse tools for the final practical data science project.

Course includes:

over 25 hours of lecture videos,

R scripts and additional data (provided in the course material),

engagement with assignments at the end of each chapter,

assignments walkthrough videos (where you can check your results).

All being said this makes one of Udemy's most comprehensive courses for data science-related tasks using R and tidyverse.

Enroll today and become the master of R's tidyverse!!!

Who this course is for:
Anyone who is interested in data science
Anyone who is interested in data analysis
Anyone who is interested in writing efficient R code
Anyone whose job, research or hobby is related to data cleaning or data visualizing
Aspiring data scientists, statisticians or data (business) analysts
Anyone who deals with data modeling and is usually struggling with data preparation / cleaning step
Students working with data

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