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Questions and Answers
Which of the following best describes the use of big data analytics in the healthcare industry?
Which of the following best describes the use of big data analytics in the healthcare industry?
- Reducing Cost of Care (correct)
- Preventing Pandemics
- Unstructured Data Analysis
- Genomic Mapping
What is a significant application of big data in the public services sector?
What is a significant application of big data in the public services sector?
- Unstructured Data Analysis
- Preventing Pandemics (correct)
- Genomic Mapping
- Social Media for Professionals
Which industry example for big data analytics focuses on genomic mapping?
Which industry example for big data analytics focuses on genomic mapping?
- Life Sciences (correct)
- Health Care
- IT Infrastructure
- Public Services
In the context of online services, which use case is directly associated with big data analytics?
In the context of online services, which use case is directly associated with big data analytics?
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Which aspect of big data analytics is typically applicable to IT infrastructures?
Which aspect of big data analytics is typically applicable to IT infrastructures?
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Study Notes
Internet of Things (IoT)
- IoT is the network of physical objects embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data.
- IoT is everywhere and has various applications.
Smart Home
- A smart home is a house with appliances that are connected to the internet and can be controlled via smartphone.
- Features of smart home technology include:
- Control: lighting, TV, thermostat, appliances, door locks, and more
- Convenience: automatically reduces TV volume when receiving a land-line call
- Security: door locks and security systems
- Health: monitoring and responding to health needs
Big Data
- Big data is high-volume, high-velocity, and high-variety information assets that require new forms of processing to enable enhanced decision making and insight discovery.
- Characteristics of big data:
- Volume: exceeds limits of traditional relational databases and grows constantly
- Velocity: arrives rapidly and often in real-time
- Variety: does not have a standard structure (e.g., text, images, audio)
Data Structures
- Four main types of data structures:
- Structured data
- Quasi-structured data
- Semi-structured data
- Unstructured data
Generation of Big Data
- Sensors gathering information (e.g., climate, traffic)
- Social media: posts, pictures, and videos
- Digital satellite images
- Purchase transaction records
- Mobile phone GPS signals
- High-volume administrative and transactional records
Data Analytics Lifecycle
- Discovery: identify problem or opportunity
- Planning: define problem, determine objectives, and identify data needs
- Data prep: collect, clean, and transform data
- Model building: develop and refine models
- Operationalize: deploy and monitor models
- Communicate results: share insights and recommendations
Industry Examples for Big Data Analytics
- Healthcare: reducing cost of care, preventing pandemics
- Life Sciences: genomic mapping, unstructured data analysis
- IT Infrastructure: phone/TV, online services, retail, financial services
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