It depends strongly on the language you prefer. As I am not using Python for data visualisation frequently I can only recommend you books relating to data visualisation in R. After writing this post I reread your question and Nr. 1, Nr. 2 and maybe Nr. 4 might be the most theoretical. Though Nr. 6 also explaines you theoretical aspects it is specialised on visualising unsupervised machine learning techniques.
The author Paul Murrell has a significant part in developing the graphics of the R language. He developed the "Grammar of Graphics" concept which is the concept underlying the ggplot2 library. The book is rather advanced although you do not need a lot of preknowledge necesarrily and pretty theoretical. It is the best book for people who genuinely want to understand the concepts of data visualisation in R, but I do not recommend it for beginners.
Is a must for interactive data visualisation. Various JavaScript libraries are translated into and adapted to R. You can include most Widgets in RShiny, Markdown (rendered as HTML) or in the console). My favorite HTML Widgets are
- Plotly (A library on interactive data visualisation which is also available for various other languages such as Python and Matlab)
- Leaflet (interactive visualisations with Maps)
- dygraph (which offers a broad variety for interactive time series visualisation)
- datatable (written by Yuhui Xe from RStudio who also wrote the knitR and the bookdown package. Prolific for showing tables))
This book is rather beginner friendly. Its examples are primarily shown in ggplot2. When I started learning advanced data visualisation techniques in R I primarily used this one and the official ggplot2 website.
Is the best starting point to learn ggplot2, but it can appear overwhelmingly if you are not willing to be passionate and if you don't have a lot of time. ggplot2 is awesome, but it can have a steep learning curve, e.g. you cannot write the "+" at the beginning of the line. All theoretical concepts are also explained.
Shiny is the most used R-library for building up apps with R. It can be substituted by BI tools like Tableau or Qlickview. shinyjs is a great extension of shiny which combines shiny with javascript, but you can also include HTML, CSS and JavaScript on your own.
This book comes from the same authors as the Guide to beautiful graphics (nr.3). It is a specialized book for visualising unsupervised machine learning techniques and particularly clustering.
In case you just start visualising and I overwhelmed you a little bit.