Before jumping direct into the difference between data science and data visualization, we first need to know about data science and data visualization and why this is growing and trending technology in this era.
What is Data Science?
We know that data is the collection of
some facts, figures and numbers, measures and observations or we can say that
it is description of some things which exists in this world. Similarly science
is the application of knowledge and understanding of the thing by using some
methodology.
Source of Data
Data comes from different sources and
the major source of data now a day, are social media, mobile technology and
e-commerce websites etc. And the processing of collecting data from different
sources is called data mining (Collect useful information from bulk of data).
So data science mean, take some data
and understand the flow and structure of data by analyzing it and perform some
market strategy. In simple word take a lot of data, analyze it and take
concrete decision to solve your problem, your market plan and much more.
Now we can say that “Data is Oil”,
because data is everywhere in raw form means we have mostly unstructured data
and to decision making using that data we need to process that data.
Another definition is to making decision making strategies we combine different fields like statistics and computation (mathematics) to interpret data, and the whole process is called data science.
Data Visualization
As we know that data is useful information, and visualization means represent some information (data) in graphical, pictorial representation.
Data visualization is the process of
taking raw data and transforming it into the graphs, charts and image, or even
into videos to gain more and more information from that.
So in simple words data visualization
mean represent the data in graphical representation. And in this process our
input to the visualization is data. So whatever we input to the visualization
in the form of data in the result we have pictorial or graphical form of that
data. We can use data visualization for:
- Decision Making
- Understanding the data clarity
- Finding solution to given problem
- To analysis of something using that data
- We can say that “A picture is worth a thousand words.
- Difference between Data Science and Data Visualization
| Data Science | Data Visualization |
|---|---|
| Figure out how to use data | Figure out how to represent data |
| Data science is taking large sets of raw data, and determining some overall meaning to the data. | Data visualization is taking this data, and putting it into a visual representation so a wider audience will pay attention to it. |
| In data science we take raw data | In data visualization we use input as data which we have finalized in the process of data science |
| Apply a data science algorithm on raw data to get output data | We use finished or processed data for data visualization |
| To manage and store tons of data we can use Apache Hadoop, Data Robot, and Rapid Miner. | For data visualization we can use QlikView Tableau. |
| By using above tools we can perform Data Storage, Data Analysis, Data Mining | Data Visualization in the form of Charts (Pie-Chart, Line etc) and Graphs |
| All about interpreting data and getting useful information from the collected data in the process of data mining. | It involves the representation of useful data in the form of images, charts and graphs. |
| Data Science and Data Visualization are bound together. | Same is here |
| Example of data science is the recommendation of Amazon’s product or Face Book friend suggestions. | To provide recommendations, data scientist uses data visualization techniques (which generally include user’s web activity) |
| We say that Data Science is Core activity before Data Visualization | To perform Data Visualization, Data Science process or activity is prerequisites. |
| Data Science belongs to tell the story verbally | A picture is worth a thousand words |
| Prerequisites to Data Visualization | Helps data scientist to provide efficient solution with various techniques |
| Explanation of data, predations and facts | Representation of Data |
| For example prediction of next Olympic winner | Key performance indicator or metrics |
| Data Science techniques performed by data analysts and mathematician. | Performed by data scientist (UX/UI) |
| Tool to get data from different sources are python and its libraries (like pandas, numpy etc), matlab, | Power BI, SaaS and python libraries to generate graphs and plots |
| Steps involve Data Mining, Data Harvesting, Data Cleansing and Modeling | Represent the finished data into graphs and charts by using above tools and libraries |
| Used for decision making | Provide recommendation (how to solve particular problem) |
| Skills are required to perform data science techniques Statistics and Algorithm | Data analysis and graph and plotting techniques to represent data |
| Algorithm in the sense, that train the model to simulate as human perceives | Choose best model based on representation |
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