Data science with R: tidyverse
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Data science with R: tidyverse
★★★★★
$79.99
in stock
Udemy.com
as of December 21, 2024 6:44 am
R Programming Language, Data Analysis, Data Cleaning, Data Science, Data Wrangling, tidyverse, dplyr, ggplot2, RStudio
Created by:
Marko Intihar
Data Scientist, Researcher and Teacher
Data Scientist, Researcher and Teacher
Rating:4.6 (590reviews)
4365students enrolled
What Will I 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 ggplot2 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
Requirements
- R and RStudio already installed on your computer.
- Basic knowledge of statistics is a plus.
- Basic to intermediate R knowledge is a plus.
- Complete R beginners will find course more challenging.
- For complete R beginners I recommend first taking one of the R beginners courses.
- Interest in data science and data science related tasks.
- Interest in how to write efficient R code.
- Please update R or R's libraries if necessary. List of versions ( R and all R's libraries used in the exercises) provided at the beginning and at the end of course material.
Target audience
- 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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