Difference Between Data Science and Data Visualization Last Updated : 15 May, 2020 Comments Improve Suggest changes Like Article Like Report Data Science: Data science is study of data. It involves developing methods of recording, storing, and analyzing data to extract useful information. The goal of data science is to gain knowledge from any type of data both structured and unstructured. Data science is a term for set of fields that are focused on mining big data sets and discovering trends, methods, new insights, and processes. It works on any size of data. Some of the applications of data science are E-commerce, Manufacturing, banking, health care, transport, finance, etc. Data science is a "concept to data analysis, machine learning, and unifies statistics" in order to understand actual phenomena with data. Data Visualization: Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, patterns in data, and outliers. Data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. The concept of using pictures to understand data has been used for centuries. General types of data visualizations are Charts, Tables, Graphs, Maps, Dashboards. Below is a table of differences between Data Science and Data Visualization: Based on Data Science Data Visualization Definition Data science is study of data. It involves developing methods of recording, storing, and analyzing data to extract useful information. Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). Process Data cleansing, Modeling, Measurement, Data harvest, Data mining, Data munging. Represent it in any chart form or graphs. Concept Insights about the data. Explanation of the data. Predictions. Representation of the data. Application Predictions. Predictions like next I-Phone release model or next World cup Winners. Organization metrics, Key performance indicators Tools Python, R, Matlab Tableau, SAS, Power BI, d3 js Who does this? Data scientists, Mathematicians, Data analysts Data scientists, UI/UX Significance Many organizations are depending on data science for decision making. It helps data scientists in understanding the source and how to solve the problem Skills Statistics, algorithms Data analysis, and plotting techniques. Comment More infoAdvertise with us Next Article Difference Between Data Science and Data Visualization S simranssonu19 Follow Improve Article Tags : Data Science Difference Between Data Visualization AI-ML-DS Similar Reads Difference Between Data Mining and Data Visualization Data mining: Data mining is the method of analyzing expansive sums of data in an exertion to discover relationships, designs, and insights. These designs, concurring to Witten and Eibemust be "meaningful in that they lead to a few advantages, more often than not a financial advantage." Data in data 2 min read Difference Between Data Visualization and Data Analytics Data Visualization: Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, patterns in data and outliers. 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