Data science vs Data visualization
Data science vs Data visualization

Data Science VS Data Visualization

Learn and Understand the complete detail about the difference between Data Science vs Data Visualization

What is Data Visualization?

Data visualization is the representation of data or facts in a graph, chart, or another visual format. it communicates relationships of the information with pics. that is crucial as it lets in developments and styles to be more without difficulty visible.

How is Data Visualization use?

Data visualization has many uses. each type of data visualization may be utilized in exclusive methods. we’ll get into the different sorts in a moment, however for now, here are a number of the most not unusual ways data visualization is used.

  • Modifications over time
  • Determining frequency
  • Determining relationships (correlations)
  • Analyzing a network

What is Data Science?

data science presents meaningful facts based on huge quantities of complicated data or big data. data science, or data-driven technology, combines different fields of labor in information and computation to interpret information for decision-making functions.

How is Data Science used?

Data science makes use of techniques including machine learning and artificial intelligence to extract meaningful data and to predict future styles and behaviors.

The sector of data science is developing as technology advances and huge data series and evaluation strategies turn out to be greater sophisticated.

Data science vs Data visualization

Data science

Concept: Insights approximately the data. explanation of the facts.

Application: Next international cup prediction, Automated cars

Who does this? : information scientists, data analysts, mathematicians

Equipment: python, MATLAB, R

Process: Data Harvest, data mining, statistics munging, information modeling, measurement

How Significant: Many companies are relying on information science consequences for choice making.

Skills: Statistics, algorithms

Data visualization

Concept: Representation of the data prediction, facts

Application: key performance indicators, business enterprise metrics

Who does this? : data scientists, UI/UX

Equipment: tableau, SAS, energy bi, d3 JS,  python and R

Process: Represent it in any chart form or cleansing, graphs

How Significant: It enables records scientists in understanding the supply trouble how to solve offering hints.

Skills: data analysis, and plotting


There are many views in relation to data science. in a clean way to technique, it’s far the way to solve a problem in various cases being it a prediction, categorization, tips, sentiment evaluation. In a nutshell, some of these can be performed the usage of the data way of trouble-solving.

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