🎬 Video playlist
🤯 Mindmap
🧠 Flashcards
📝 Notes
- Data: Raw, unorganized facts and figures.
- Information: Data that has been processed, organized, and given context.
- Knowledge: Understanding gained through experience, learning, and insights derived from information.
- Wisdom: The ability to apply knowledge and understanding to make sound judgments and decisions.
- DIKW Pyramid: A hierarchical model that represents the relationships between Data, Information, Knowledge, and Wisdom.
Types of Data
- Quantitative and Qualitative: Numerical data and descriptive/categorical data.
- Cultural, Financial, Geographical, Medical: Data related to specific domains or industries.
- Meteorological, Transport, Scientific, Statistical: Data related to weather, transportation, scientific research, and statistics.
- Metadata: Data that describes other data.
Uses of Data 🎬
- Identify Trends: Recognizing patterns and changes in data over time.
- Identify Patterns: Discovering recurring themes or regularities in data.
- Identify Connections: Uncovering relationships and links between different data points.
- Identify Relationships: Determining how different data points or variables influence each other.
- Collect and Organize Facts about People and Communities: Gathering data to better understand individuals and groups.
Data Life Cycle
- Create/Collect/Extract: Generating or acquiring data from various sources.
- Store: Keeping data in a secure and accessible location.
- Process: Transforming raw data into a usable format.
- Analyse: Examining data to derive insights and knowledge.
- Access: Making data available to authorized users.
- Preserve: Ensuring data remains intact and usable over time.
- Reuse: Utilizing data for multiple purposes or projects.
Ways to Collect and Organize Data
- Primary and Secondary Data Collection:
- Primary: Gathering data directly from the source.
- Secondary: Using existing data sources collected by others.
- Databases: Structured collections of data stored electronically.
- Data Classifications and Relationships: Categorizing data and defining connections between data points.
Ways of Representing Data
- Charts, Tables, Reports: Visual representations of data for easy understanding.
- Infographics, Visualizations: Creative and engaging visual representations of data.
Data Security
- Encryption: Converting data into a coded format to prevent unauthorized access.
- Symmetric Encryption: Using the same key for encryption and decryption.
- Asymmetric Encryption: Using different keys for encryption and decryption.
- Data Masking: Obscuring sensitive data to protect privacy.
- Static Data Masking: Permanently replacing sensitive data with fictitious but realistic data.
- Dynamic Data Masking: Masking sensitive data in real-time as it is accessed.
- Data Erasure: Securely removing data from storage devices to prevent recovery.
- Overwriting: Writing new data over the existing data to make it unrecoverable.
- Degaussing: Using strong magnetic fields to erase data from magnetic storage devices.
- Blockchain: A decentralized, secure ledger technology for storing and sharing data.
Characteristics and Uses of Big Data and Data Analytics
- Characteristics: Volume, Variety, Velocity, Veracity: The 4 V's that define Big Data.
- Uses: Predictive Analysis, Modelling, Understanding Human Behaviour: Applications of Big Data and Data Analytics.
Data Dilemmas
- Control, Ownership, Access to Data: Issues surrounding who has control over, owns, and can access data.
- Privacy, Anonymity, Surveillance:
- Privacy: Protecting an individual's right to control their personal information.
- Anonymity: Ensuring that data cannot be traced back to a specific individual.
- Surveillance: Monitoring individuals or groups, often without their knowledge or consent.
- Personally Identifiable Information: Data that can be used to identify a specific individual, raising privacy concerns.