Unit 3447: Emerging Technologies in Computing

1. Cloud Computing

1.1 Concept and Overview

• Definition and evolution of cloud computing
• Importance in modern IT infrastructure

1.2 Types of Cloud Services

• Software as a Service (SaaS)
• Platform as a Service (PaaS)
• Infrastructure as a Service (IaaS)

1.3 Deployment Models

• Public cloud
• Private cloud
• Hybrid cloud

1.4 Benefits and Challenges

• Scalability, cost efficiency, accessibility
• Security, compliance, downtime risks

1.5 Real-world Applications

• Examples from businesses, healthcare, education

2. Internet of Things (IoT)

2.1 IoT Technology Overview

• Definition and ecosystem
• Types of IoT devices and sensors

2.2 Connectivity Protocols

• Wi-Fi, Bluetooth, Zigbee, LoRaWAN, NB-IoT

2.3 IoT Platforms

• Cloud integration
• Data management

2.4 Security Considerations

• Vulnerabilities and risks
• Best practices for securing IoT networks

2.5 Industry Impact

• Smart homes, healthcare, agriculture, manufacturing

3. Artificial Intelligence (AI) and Machine Learning

3.1 Introduction to AI and ML

• Definitions and distinctions
• Historical context

3.2 Core Algorithms

• Supervised, unsupervised, reinforcement learning
• Neural networks and deep learning basics

3.3 Applications of AI/ML

• Natural language processing, computer vision, robotics

3.4 Ethical Considerations

• Bias, privacy, decision transparency

3.5 Future Trends

• AI in autonomous systems, AI democratization

4. Cybersecurity and Data Privacy

4.1 Cybersecurity Threats

• Malware, phishing, ransomware, insider threats

4.2 Data Breaches

• Case studies and impacts

4.3 Encryption Techniques

• Symmetric and asymmetric encryption
• Public Key Infrastructure (PKI)

4.4 Security Best Practices

• Firewalls, multi-factor authentication, incident response

4.5 Compliance Regulations

• GDPR, HIPAA, CCPA

4.6 Importance of Data Privacy

• User rights, data protection strategies

5. Big Data and Analytics

5.1 Introduction to Big Data

• Characteristics: volume, velocity, variety, veracity

5.2 Data Analytics Tools

• Hadoop, Spark, data warehouses

5.3 Data Visualization Techniques

• Dashboards, charts, storytelling with data

5.4 Predictive Analytics and Data Mining

• Techniques and use cases

5.5 Role in Decision-Making

• Business intelligence and strategic planning

6. Blockchain Technology

6.1 Blockchain Concepts

• Distributed ledger technology
• Decentralization and immutability

6.2 Smart Contracts

• Definition and functionality
• Use cases

6.3 Cryptocurrencies

• Bitcoin, Ethereum overview

6.4 Applications Beyond Finance

• Supply chain, healthcare, voting systems

6.5 Industry Impact and Challenges

• Adoption barriers, scalability issues

7. Virtual Reality (VR) and Augmented Reality (AR)

7.1 Definitions and Differences

• VR vs AR: immersive vs overlay technologies

7.2 Applications

• Gaming, education, healthcare, architecture

7.3 Technology Components

• Hardware, software, sensors

7.4 Future Trends

• Mixed reality, haptics, broader adoption

8. Quantum Computing

8.1 Introduction

• Quantum computing basics
• Qubits and quantum states

8.2 Quantum Gates and Algorithms

• Key quantum gates
• Shor’s and Grover’s algorithms

8.3 Quantum Supremacy

• Concept and recent milestones

8.4 Current Challenges

• Error rates, qubit coherence, hardware limitations

8.5 Potential Applications

• Cryptography, optimization, drug discovery