Udemy - Data Visualization with R and ggDescription2

Category: Tutorial

Posted on 2021-09-22, by voska89.


MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 108 lectures (20h 40m) | Size: 8.1 GB
R Programming Language for Data Visualization. GGDescription2, Data Analysis, Data Preparation, Data Sciene Tools, RStudio

What you'll learn:
Visualize data
Foundations of data visualization (Grammar of Graphics and ggDescription2)
Transform data before visualization is applied (data wrangling libraries)
Apply exploratory data analysis techniques with R and ggDescription2
Wrap up analysis using RMarkdown reports
Use ggDescription2 for creating many different standard statistical Descriptions
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, to get proper foundations needed.
To be fond of data transformation libraries (dplyr, tidyr) is a plus. However course includes a quick overview of dplyr and tidyr.
Interest in data science and data visualization.
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 end of course material.
Today we live in a world where tons of data is generated every second. We need to analyze data to get some useful insight. One of the strongest weapons for data insight is data visualization. Probably you have heard this one before: "A picture tells more than a thousand words combined ". Therefore to tell stories from the data we need tools for producing adequate and amazing graphics. Here R as one of the most rapidly growing tools in the fields of data science and statistics provides needed assistance. If you combine R with its library ggDescription2 you get one of the deadliest tools for data visualization, which grows every day and is freely accessible to anyone.
This course is designed to first give you quick and proper theoretical foundations for creating statistical Descriptions. Then you dive into the world of exploratory data analysis where you are confronted with different datasets and creating a wide variety of statistical Descriptions.
If you take this course, you will learn a ton of new things. Here are just a few topics you will be engaged with:
The grammar of graphics (the idea behind statistical Descriptions, the foundation of ggDescription2)
Data transformation with dplyr and tidyr (crash course included)
Exploratory data analysis (EDA) (statistical Descriptions for exploring one continuous or one discrete variable)
EDA for exploring two or more variables (different statistical Descriptions)
Combine ggDescription2 with RMarkdown to wrap up your analysis and produce HTML reports
Create some additional types of Descriptions by combining ggDescription2 and supplementary libraries (word cloud, parallel coordinates Description, heat map, radar Description, ...)
Draw maps to show the spread of coronavirus disease
Customize the Description's theme
Create subDescriptions using cowDescription library
Highlight data on your Description with gghighlight library
and much more...
Course includes:
over 20 hours of lecture videos,
R scripts and additional data (provided in the course material),
engagement with assignments, where you have to test your skills,
assignments walkthrough videos (where you can check your results).
All being said this makes one of Udemy's most comprehensive courses for data visualization using R and ggDescription2.
Who this course is for
Anyone who is interested in data analysis or data visualization
Aspiring data scientists, statisticians or data (business) analysts
Anyone who would like to impress his/her boss or coworkers with amazing data visualizations
Anyone whose job, research or hobby is related to visualizing data
Anyone whose work is related with data presentation or extracting insights from the data
Students working with data

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