Today we have data everywhere which is in peta bytes and data science is used to deal with huge amount of data. Data might be structured or unstructured. Data science is a field of big data that deals with getting meaningful information from large volumes of data. Other field work is carried out in order to make data look meaningful. Data mining is subordinate of data science, which deals with getting information from previaous data. Let’s read about data science and data mining and how it’s performed on large volumes of data.
Data Science and Data Mining Definitions
- Data science is a data driven field that uses statistics, various processes to extract information from various resources.
- Data science deals with extracting helpful amount of information from large volumes of data.
- Its focuses on present and future patterns for decision making from extracted information.
- Gathered information would be in any form. Organizing gathered information is necessary to carry out analysis to get data insight.
- Getting hidden insight enable companies to make smarter decisions.
Facts about Data Science
- Data is not clean – While extracting information from large volumes of data, we might come across information which is not clean. Lot of chopping has to be done to make useful for analysis.
- Data science is a time consuming process and it takes huge amount of time to get information and prepare it for analysis process (where meaningful info would be extracted).
- Process of data science is not automated, you need to dig deep to get desired information for better decision making.
- Data scientists use various methods for getting data insights. For ex statistical, computational programs and other algorithms.
- Information presentation is very important. End users or decision makers don’t understand the complexity behind analysis process. Thus well presented information leads to better decision making.
- Data mining is the process of discovering patterns in large volumes o data. While working with big data analysis data mining helps in relationship among objects and data sets.
- Data mining is a subordinate of data science which help is predictive analysis for better decision making.
- Data mining process involves various processes like data cleaning, data integration, data transformation, data mining, pattern evolution and presentation.
- Data mining is used for predicting market trends by analyzing past information.
- Various tools of Data mining are available that could be used for market analysis, fraud detection, customer retention, production control etc.
Characteristics of data mining
- Data mining served the purpose of gathering useful information from various resources. Lots of procedures are carried out to improve data quality.
- Large volumes of data are gathered before mining process to make it clean and usable for better decision making.
- Data gathered is very complex and it’s not understandable. Complex nature of data makes data mining process a bit difficult.
Data mining is a subordinate of data science, both of these are used to analyze gathered information for decision making. There many , algorithm, tools and techniques are used for getting insight of data. Many organizations are using data science to analyze market trends and behavior to come up with new business strategies.