Data analysis typically involves multiple types of data to gain comprehensive insights. Typical data types include:
- Structured Data: Tables of data with defined rows and columns, such as database tables or data in spreadsheets. This data is easily stored and processed.
- Unstructured Data: Data without a predefined structure, such as text documents, emails, social media posts, images, and videos. Analysis of this data requires techniques like Natural Language Processing (NLP) and image processing.
- Time Series Data: Data arranged in chronological order, such as stock prices, sales data, weather records, etc. Time series analysis detects trends, periodicity, and seasonal patterns.
- Geospatial Data: Data containing spatial information like map coordinates, geographical boundaries, and features. Geospatial data analysis is used in Geographic Information Systems (GIS) applications and location-based services.
- Graph Data: Data describing relationships between objects, such as social network graphs, network topologies, etc. Graph data analysis is used in social network analysis, network security, and recommendation systems.
- Sensor Data: Real-time data collected by sensor devices, such as temperature, humidity, pressure, etc. Sensor data analysis is used in Internet of Things (IoT), smart manufacturing, and environmental monitoring.
- Financial Data: Includes stock market data, financial statements, profit and loss statements, balance sheets, etc. Financial data analysis is used in risk management, investment decisions, and trading strategy formulation.
These different types of data are often combined in data analysis projects to provide a more comprehensive and in-depth understanding, supporting various business decisions and strategy formulation.