Difference between Big Data and Data Science
Big Data
- Hugh volumes of data which cannot be handled using traditional database programming.
- Characterized by volume, variety, and velocity.
Data Science
- A data-focused on scientific activity.
- Approaches to process big data.
- Harnesses the potential of big data for business decisions.
- Similar to data mining.
Concept
Big Data
- Diverse data types generated from multiple data sources.
- Includes all types and formats of data.
Data Science
- A specialized area involving scientific programming tools, models and techniques to process big data.
- Provides techniques to extract insights and information from large datasets.
- Supports organizations in decision making.
Basic of formation
Big Data
- Internet users/traffic
- Electronic devices (sensors, RFID, etc.)
- Audio/video streams including live feeds.
- Online discussion forums.
- Data generated in organizations (transactions, DB, spreadsheets, emails, etc.)
- Data generated from system logs.
Data Science
- Applies scientific methods to extract knowledge from big data.
- Related to data filtering, preparation, and analysis.
- Capture complex patterns from big data and develop models.
- Working apps are created by programming developed models.
Application areas
Big Data
- Financial services
- Telecommunications
- Optimizing business process
- Performance optimization
- Health and sports
- Improving commerce
- Research and development
- Security and law enforcement
Data Science
- Internet search
- Digital advertisement
- Search recommenders
- Image/speech recognition
- Fraud, risk detection
- Web development
- Other miscellaneous areas/utilities
Approach
Big Data
- To develop business agility
- To gain competitiveness
- Leverage datasets for business advantage
- Establish realistic metrics and ROI
- To achieve sustainability
- To understand markets and gain new customers
Data Science
- Involves extensive use of mathematics, statistics, and other tools
- State-of-the-art technique/algorithms for data mining
- Programming skills (SQL, NoSQL), Hadoop platforms
- Data acquisition, preparation, processing, publishing, preserve or destroy
- Data visualization, prediction
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