What is Data Science
Nowadays everyone is trying to understand the meaning of Data Science. There are various types of definitions. Based on my understanding, data science means anything related to data which is accessible for any kind of analysis and ready for evaluation to extract relevant insights from data.
In our digital world, purpose of every application for any business domain is to capture data in any format. Any application is data driven application which acquires values from the data itself and creates more data. Data science enables the creation of these types of data driven products.
At a glance, data science requires the knowledge from various areas such as mathematics, computer programming, statistical modelling, data engineering and visualization, pattern recognition and learning, uncertainty modelling, data warehousing, machine learning, and cloud computing.
From where Data comes from
Data is everywhere. From example, web data, online application data, government data, data from different business partners, survey data.
Types of Data
There are two types of data; public data and private data.
- A large amount of data collected for research by the government or other public agencies is made public. To use these types of data sets, we do not need any permission, that’s why it is called public data.
- On the other hand, private data is that which is sensitive to organisations and is not available in the public domain.
Usage area of Data Science
- Data science is used in Artificial Intelligence (AI) and machine learning.
- It solves complex data problems to extract insights which were unknown prior to applying it.
- It brings out unknown correlations between data that are extremely relevant and useful to a business.
Data Science vs. Business Intelligence vs. Statistics
Despite of similarities among data science, business intelligence and statistics, data science is quite different in terms of processes, domains and skills.