Data Visualization: Data visualization is the graphical representation of data and information using visual elements such as charts, graphs, maps, and diagrams. The goal of data visualization is to present complex data in a clear, intuitive, and easily understandable format, allowing patterns, trends, and insights to be quickly identified. It aids in conveying information, making comparisons, and communicating findings to both technical and non-technical audiences.

Data Analysis: Data analysis involves the process of inspecting, cleaning, transforming, and interpreting data with the aim of discovering meaningful insights, drawing conclusions, and supporting decision-making. It encompasses various techniques such as statistical analysis, machine learning, and qualitative methods to extract valuable information from data sets. Effective data analysis helps researchers and analysts understand relationships, make predictions, and inform strategies based on evidence-driven insights.

Data visualization and analysis are crucial processes in the field of data science and analytics that involve the representation of data in graphical or visual formats to extract meaningful insights, patterns, and trends. These processes help individuals, organizations, and researchers make informed decisions, discover hidden relationships, and communicate complex information effectively.

Data Visualization:Data visualization involves creating visual representations of data using various graphical elements such as charts, graphs, maps, and diagrams. The main goal of data visualization is to present complex data in a more understandable and accessible way. By representing data visually, patterns, trends, and outliers become easier to identify, allowing for quicker and more intuitive understanding. Data visualization tools and techniques also enable the exploration of data from different angles, helping to uncover insights that might not be evident from the raw data alone.

Common types of data visualizations include:

Data Analysis:Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, make conclusions, and support decision-making. It involves a range of techniques and methods to extract insights from data, including statistical analysis, machine learning, and data mining. Data analysis can be broadly categorized into exploratory data analysis (EDA) and confirmatory data analysis (CDA):

The data analysis process typically includes steps like data collection, data cleaning and preprocessing, exploratory data analysis, feature engineering, model selection and building, validation, and interpretation of results. The choice of analysis techniques depends on the nature of the data, the questions being addressed, and the goals of the analysis.

In summary, data visualization and analysis are interconnected processes that help transform raw data into actionable insights by presenting information visually and extracting meaningful patterns through statistical and computational methods. These processes are integral to various fields, including business, research, healthcare, finance, and more.

Visualization is more general term in respect to data presentation than specifically related to a visual sense. Visualization means some form of manifestation of the data related parameter or attribute that allows to assess its value and changes. Mental visualization does not always require modality-specific imagery. It can be even modality-independent visualization based on interpretational sense (e.g., a moral/physical pain in a specific context/situation). Btw, a sense of critical analysis is also related to interpretational sense, therefore visualization of data as a consequences of catastrophe can be preseted as moral/physical pain of the whole society.

the different bitween visualization and analyse you can see here: http://md.c-europe.eu

On the one page you see the mathematical function in the fild of pulse.

On other page you see the image, the schema of this machine

some samples for interpretational senses

https://www.seeingwithsound.com/

Accessible graphing calculator https://www.seeingwithsound.com/winmath.htm

Free Pain Videos https://www.pexels.com/search/videos/pain/

Data visualization is a visual element used by professional to display data so that it can be interpreted easily, for instance; charts, graphs, maps, etc

While, Data analysis is the process of transforming, modeling, examining data, to be useful information, draw conclusions and support decision-making.

Data visualization is the graphical representation of data to help people understand the patterns, trends, and relationships within the data more easily. It involves creating visual elements such as charts, graphs, maps, and dashboards to present data in a visual format. Here are some key aspects of data visualization:

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non-technical audiences without confusion.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

https://www.tableau.com/learn/articles/data-visualization

Data Visualization refers to the different ways of presenting data in a visual way to tell a story or share insights which would make sense to an audience.

Data Analysis on the other represents the different ways of manipulating data using statistical techniques or programming to understand its characteristics .

recently has been presented an alternative mental visualization technique which soon may lead to novel algorithms for data compression as well as visualization. https://eanews.ru/news/uralskiy-matematik-dmitriy-borisov-moya-chislovaya-sistema-sdelala-menya-schastliveye-na-40_10-11-2021

youtube: "dmitry borisov ural mathematician" https://www.youtube.com/watch?v=Bykvyc527Oc

I like what I read in the comments and references of previous answer submitters. Thank you for that. However, nobody has yet pointed to the number 1 till number 100 read (that is a must) for data vizualization. Go out and find the following in the library at some place (as Michelin says, it is worth a journey, like a 3 star restaurant).

Edward R. Tufte (2001). The Visual Display of Quantitative Information. Graphics Press, Chesire, Connecticut.

It has wonderful examples used through the ages, and is truly inspiring.

The final quote from this book is: "What is to be sought in designs for the display of information is the clear portrayal of complexity.