DS

1. Data, Validation, and Relational Databases

Key Concepts:

• Data: Raw facts and numbers, unorganized and objective.

• Information: Processed data that is structured to be useful.

• Knowledge: Understanding and interpreting information.

• Wisdom: Applying knowledge in decision-making with reflection and ethical consideration.

Types of Data:

• Primary Data: Collected directly by researchers. (Flexible, reliable, time-consuming)

• Secondary Data: Pre-existing data, (not flexible, but efficient)

Relational Databases:

• Entity: The object or thing being stored in a database (e.g., students, books, movies).

• Attributes: Specific data points related to an entity (e.g., name, date of birth).

• Primary Key: A unique identifier for each record.

• Foreign Key: A field in one table that links to another table’s primary key.

Validation and Verification:

• Validation: Ensuring only appropriate data is entered into a database.

  • Field length restrictions, assigned data types

• Verification: Ensuring entered data matches the original source.

  • Double entry (e.g., re-entering a password for verification)

  • Visual checks (human verification of data accuracy)

Data Security & Encryption:

• Encryption: Converts data into an unreadable format to prevent unauthorized access.

  • Types:

    • Symmetric Key Encryption: Same key used for encryption and decryption.

    • Public Key Encryption (Asymmetric Encryption): Uses a private key for the sender and a public key for the receiver. Used in HTTPS, secure messaging apps, and email security.

Real-World Examples:

• Recuva (Data recovery tool) – Used to retrieve deleted files.

• Blockchain for Data Integrity – Provides transparency, security, and decentralization in databases.

2. Disaster Prediction and Model Software

Key Concepts:

• Big Data in Disaster Prediction:

  • The 5 Pillars of Big Data:

    • Volume, Velocity, Variety, Veracity, Value

  • Data collection from multiple sources enables accurate disaster prediction.

Real-World Examples:

• Weather Forecasting Using AI: Machine Learning is used to analyze patterns for better disaster prediction.

• NASA’s Earth Observing System (EOS): Satellites gather data for climate and environmental monitoring.

• Blockchain for Tracking Carbon Emissions: Helps predict climate change trends.

• Walmart using Blockchain: Tracks food supply chains to prevent food-related disasters.

3. Online Learning

Key Concepts:

• Types of Learning:

  • Synchronous Learning: Real-time classes with teachers.

  • Asynchronous Learning: Pre-recorded lessons, no real-time teacher.

  • Self-Guided Learning: Independent learning with digital resources.

• Digital Learning Models:

  • MOOC (Massive Open Online Courses): Scalable courses available to large audiences.

  • Competency Development: Focuses on skill-building rather than grades.

• Barriers to Online Learning:

  • Digital Divide: Unequal access to technology.

  • Paywall: Restricts access to educational content behind a payment barrier.

Real-World Examples:

• Mexico using VR to train remote doctors – Expands access to medical education.

• AI-powered Learning Systems: Helps in competency-based education by adjusting learning paths based on student progress.

4. Neural Networks and Surveillance

Key Concepts:

• Neural Networks: Algorithms inspired by the human brain that process vast amounts of data through layers of neurons.

• Deep Learning: Advanced Machine Learning (ML) that can analyze images, text, and speech.

• Pattern Recognition: Used in data analytics, stock market forecasting, and audience research.

• Sentiment Analysis: Determines user emotions and preferences for recommendations.

Surveillance Technologies:

• AI-Powered Surveillance: Pattern recognition, biometric authentication, and facial recognition for monitoring public spaces.

• Embedded Computers: Small hardware-software combinations for surveillance systems, smart home security, and public monitoring.

Real-World Examples:

• Chinese AI Surveillance: Uses big data and facial recognition for monitoring citizens.

• Tesla Vision: Uses Machine Learning, neural networks, and computer vision for self-driving cars.

• Blackbox Algorithms: AI-powered decision-making tools that lack transparency in how they reach conclusions.

• Uber AI Bias: AI verification systems allegedly discriminated against Black users.

5. AI in Medical Imaging

Key Concepts:

• AI in Radiology: Uses Deep Learning for analyzing medical images like X-rays, MRIs, and CT scans.

• Machine Learning in Diagnosis: AI assists in detecting tumors, fractures, and anomalies.

• Medical Data Processing: AI sorts through large datasets to assist doctors.

Real-World Examples:

• AI in Radiology in Mexico: AI being used to analyze medical scans and improve access to healthcare.

• Telemedicine Using VR in Remote Areas: Expands medical imaging access for doctors and paramedics.