About Advanced Technology & Architecture

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The Digital Transformation: Advanced Technology & Architecture course explores both the practical and strategic dimensions of digital transformation. It provides in-depth coverage of advanced digital solution technologies, with a focus on the architectural foundations of cloud computing, blockchain-based data storage, and sensor-driven IoT systems, equipping professionals to design and implement modern, scalable solutions.

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Program Structure  

• 100% online  

• Self-paced  

• 8 modules (80 hours total)  

• Includes video lessons and quizzes  

• Certificate of Completion provided  

 

Modules

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Fundamental Digital Transformation    

• Key drivers and challenges of digital transformation  

• Shifting from product-centric to customer-centric strategies  

• Customer journeys, omni-channel experiences, and data intelligence  

• Data sources, collection methods, and smart data use  

• Intelligent and automated decision-making in business processes  

 

Digital Transformation in Practice   

• Core concepts of distributed solution design and data integration  

• Key automation technologies: cloud computing, blockchain, IoT, and RPA  

• Introduction to data science in digital transformation: big data, machine learning, and AI  

• Benefits, risks, and challenges of emerging technologies  

• Designing and mapping end-to-end, customer-centric digital solutions  

• How data intelligence is collected, processed, and applied in transformation initiatives.

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Fundamental Cloud Computing 

• Core cloud computing concepts, terminology, and defining characteristics

• Benefits, challenges, and risks of modern cloud platforms

• Key cloud service models: SaaS, PaaS, and IaaS

• Virtualization, containerization, and architectural building blocks

• Scaling, resiliency, and resource optimization in cloud systems

• Cloud balancing, bursting, and practical deployment strategies

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Fundamental Blockchain

• Blockchain fundamentals: decentralized models, value propositions, and business/technology drivers

• Applications across industries, including distributed ledgers and relational database comparisons

• Core blockchain architecture: blocks, transactions, nodes, and consensus mechanisms

• Key technologies: cryptography, hashing, coins, tokens, and smart contracts

• Blockchain types and activities: public/private, on-chain/off-chain, cross-chain

• Advanced concepts: Merkle trees, forks, metrics, and lifecycle processes

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Fundamental IoT

• IoT foundations: business drivers, benefits, challenges, and core domains (personal, enterprise, utilities, mobile)

• Devices and connectivity: sensors, microcontrollers, RFID, LPWANs, and power sources

• Data and commands: telemetry, real-time contextual data, and data serialization methods (JSON, Protocol Buffers)

• IoT architecture: layers, gateways, platforms, and key design considerations

• Communication protocols: REST, HTTP, CoAP, MQTT, and radio transport options

• Advanced approaches: edge and fog computing for efficiency and scalability

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***Participants may specialize in either Fundamental IoT or Fundamental Blockchain according to their interest.

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Cloud Architecture

• Technology architecture of private and public clouds, including SaaS, PaaS, and IaaS environments

• Automated, centralized, and self-provisioning administration

• Containerization strategies, including sidecars, chains, and logical pod containers

• Resource management, pooling, and monitoring for efficient utilization

• Workload distribution, rapid provisioning, and dynamic scalability

• Non-disruptive service relocation and real-time resource availability

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Blockchain Architecture

• Core blockchain components: nodes, ledgers, wallets, and identity verification

• Consensus, block creation, and validation mechanisms

• Integrity and validation design patterns, including sidechains and block singletons

• Scalability and reliability patterns, including auto-scaling and geo-scaling nodes

• Security and privacy patterns, including forced on-chain transactions and user data protection

• Utility patterns, including transaction tagging and lightweight node management

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IoT Architecture

• Core IoT device components: sensors, actuators, modems, and control logic

• IoT platforms, gateways, and publish-subscribe systems

• Device management: shadows, registries, and trusted modules

• Telemetry and data processing: modeling, encoding, and minimalized data formats

• Performance optimization patterns: messaging, traffic, reconnection, and workload regulation

• Security, reliability, and utility patterns: encryption, firmware integrity, multimode communication, and positioning

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***Participants may specialize in either IoT Architecture or Blockchain Architecture according to their interest.

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Fundamental AI Architecture

• Comparing AI architecture and engineering, including product vs. custom designs

• System scopes and solution types: monolithic, modular, and hybrid architectures

• Data management approaches: storage models, ingestion, preprocessing, and feature engineering

• Core system components: inference engines, model repositories, and decision-making logic

• Monitoring and performance: operations, data, model evaluation, and bias detection

• Supporting mechanisms: explainability, robustness, security, and data quality controls

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Advanced AI architecture

• Distributed data processing, caching, partitioning, and sharding

• Incremental processing and hardware acceleration

• Autoscaling, load balancing, and continuous learning

• Performance optimization: parallelism, concurrency, edge caching, vectorization, and data compression

• Lazy loading and efficiency mechanisms

• Resiliency patterns: fault tolerance, graceful degradation, and chaos engineering