Udemy – Data Science with R: tidyverse 2021-8 – Download

Explanation

Data science in R: structured, Data Science skills remain one of the most in-demand skills in today’s job market. Many people see only the fun part of data science, activities such as: “search for data”, “reveal the hidden truth behind the data”, “build predictive models”, “apply machine learning algorithms”, and so on. The fact, known by most data scientists, is that, when dealing with real data, the most time-consuming operations in any data science project are: “data import”, “data cleaning”, “data warrangling”, “data exploration” and so on. So it is necessary to have enough tools to deal with data-related tasks. What if I say, there are tools that are freely available, which falling into the above description!

R is one of the required programming languages ​​when it comes to applied statistics, data science, data exploration, etc. If you add R and the collection of R libraries called tidyverse, you get one of the deadliest tools, which is designed for scientific tasks related to data. All systematic libraries share a common philosophy, grammar, and data types. The libraries can therefore be used side by side, and enable you to write R code more efficiently, which will help you finish projects faster.

What will you learn?

  • How to use systematic libraries in R for your data science projects
  • How to write useful R code for data science related tasks
  • What is clean data?
  • How to clean your R data
  • What is data grammar?
  • How to fight against dplyr and tidyr data
  • How to import data into R
  • How to properly categorize downloaded data
  • How to chain R functions in a pipeline
  • How to manage strings
  • What are Regular Expressions?
  • How to use the stringr library with regular expressions
  • How to use the forcats library to manipulate different variables
  • How to plot data with the ggplot2 library
  • What is a functional program?
  • How to use the purrr library for mapping functions, nesting data, editing lists, etc.
  • What is contact information?

Who is this course for?

  • Anyone interested in data science
  • Anyone interested in data analysis
  • Anyone interested in writing decent R code
  • Anyone whose work, research or hobby is related to data cleaning or data visualization
  • Aspiring data scientists, accountants or data analysts (business).
  • Anyone who deals with data modeling and often struggles with the data preparation/cleansing step
  • Data processing students

Specificatoin of Data Science with R: tidyverse

  • Publisher: Udemy
  • Teacher: Marco Intihar
  • Language : English
  • Level : All Levels
  • Number of Courses: 200
  • Duration: 30 hours and 7 minutes

Includes 2022-10

Data science with R: tidyverse

Requirements

  • R and RStudio are already installed on your computer.
  • Basic knowledge of statistics is a plus.
  • Basic to intermediate knowledge of R is a plus.
  • Complete R beginners will find the course very challenging.
  • For complete R beginners I recommend taking one of the introductory R courses.
  • Interest in data science and data science related activities.
  • I am interested in how to write efficient R code.
  • Please update the R or R libraries if necessary. A list of versions (R and all R libraries used in the exercises) provided at the beginning and end of the course materials.

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Data science with R: tidyverse

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Subtitle: English

Quality: 720p

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Download Part 1 – 2 GB

Download Part 2 – 2 GB

Download Part 3 – 2 GB

Download Part 4 – 2 GB

Download Episode 5 – 2 GB

Download Episode 6 – 45 MB

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