Data science is a present-day technology world using a very common term. It is a multi-disciplinary entity that deals with data in a structured and unstructured manner. It uses scientific methods and mathematics to process data and to extract knowledge from it. It works on the same concept as Big Data and Data Mining. It requires powerful hardware along with an efficient algorithm and software programming to solve the data problems or to process the data for obtaining valuable knowledge from it.
The present information trends are providing us 80% of data in unstructured mannered while rest 20% structured in format for quick analyzing. The unstructured or semi-structured details require processing in order to make it useful for the present-day entrepreneur environment. Generally, this information or details are generated from the wide varieties of sources such as text files, financial logs, instruments and sensors and multimedia forms. Drawing meaningful and valuable insights from this information require advanced algorithms and tools. This Science is proposing a value proposition for this purpose and this is making it a valuable science for the present-day technological world.
Life Cycle of Data Science
- Capturing: The Science starts with the data acquisition, data entry, data extraction and signal reception.
- Processing: This science process the acquired data effectively using data mining, data clustering & classification, data modelling and data summary.
- Maintaining: The Science maintains the processed data using data warehousing, data cleansing, data staging, and data architecture.
- Communicating: This science communicates or serves data using data reporting, data visualization, business intelligence and decision-making models.
- Analyzing: This Science analyzes data using exploratory or confirmatory process, predictive analysis, regression, text mining and qualitative analysis.