Data science is a field of study devoted to creating and using tools and techniques to extract information from raw data. In other words, data science makes use of complex machine learning algorithms to make sense of the massive volumes of unstructured data and derive meaningful insights from it.
Data science is used to create predictive data models that enable enterprise users to find connections and unseen patterns in the gigantic lakes of data and help them formulate actionable insights. These data-driven insights are responsible for major business transformation in the form of new ideas, products, services, and experiences.
Data Science Stages
There are 5 stages in the data science process:
- Capture: This stage involves capturing raw data in structured and unstructured forms. This includes data acquisition, data extraction, and data entry.
- Maintain: This stage involves the conversion of the raw data into a form that can be used for analysis. This includes processes like data cleaning, data warehousing, data staging, and data processing.
- Process: This stage involves the application of data science tools and techniques to the prepared data. This includes data modeling, data mining, data clustering, and data summarization. This stage reveals the hidden data patterns and trends and helps in analysis.
- Analyze: This stage involves the performance of varied analyses on the processed data. This includes predictive analysis, text mining, regression analysis, qualitative analysis, and exploratory analysis.
- Communicate: The last stage in the data science process involves communicating the derived insights through reports and dashboards, to the users. It includes data visualization, data reporting, and business intelligence and assists in decision making.
Applications of Data Science in Modern Enterprises
Data science has become detrimental to the growth of modern enterprises in every industry. Some of its applications can be found in:
- Healthcare: Data science helps healthcare organizations create sophisticated medical instruments for detection and treatment of complex diseases.
- Entertainment: Data science has made it easy for OTT service providers to leverage viewership and subscriber data for providing recommendations.
- Supply Chain and Logistics: With the help of data science, supply chain and logistics companies can find the best route and channel for delivery, increasing delivery speed and operational efficiency.
- Gaming: Using data science, major gaming giants have been able to create the next-gen gaming experience.
- Banking and Finance: Data science has made estimating risk, detecting fraud, and managing funds a piece of cake for banking and financial institutions.