Difference Between Data Science and Data Visualization

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.