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DIGITAL TRANSFORMATION
CCP   SOACP   BDSCP  
NEXT-GEN IT   JUNIOR

Digital Transformation Courses

The Digital Transformation curriculum from Arcitura is comprised of 16 course modules. Course delivery options can include private on-site workshops, live virtual training, public workshops or self-paced training via printed and eLearning study kits. Each module is a one-day course when taught by a Certified Trainer or can take 10-14 hours to complete via self-study.

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Module 1: Fundamental Digital Transformation

This course introduces Digital Transformation and provides detailed coverage of associated practices, models and technologies, along with coverage of Digital Transformation benefits, challenges and business and technology drivers. Also explained are common Digital Transformation domains, digital capabilities and adoption considerations.

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Module 2: Digital Transformation in Practice

This course delves into the application of Digital Transformation by exploring a series of contemporary technologies associated with carrying out Digital Transformation projects and further demonstrating how the adoption of Digital Transformation practices and technologies can lead to business process improvements and optimization. Proven leadership and execution models are covered, along with a fundamental overview of digital trust and digital identities.

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Module 3: Fundamental Cloud Computing

This course provides end-to-end coverage of fundamental cloud computing topics relevant to Digital Transformation, including an exploration of technology-related topics that pertain to contemporary cloud computing platforms.

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Module 4: Fundamental Blockchain

This course provides a clear, end-to-end understanding of how blockchain works. It breaks down blockchain technology and architecture in easy-to-understand concepts, terms and building blocks. Industry drivers and impacts of blockchain are explained, followed by plain English descriptions of each primary part of a blockchain system and step-by-step descriptions of how these parts work together.

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Module 5: Fundamental IoT

This course covers the essentials of the field of Internet of Things (IoT) from both business and technical aspects. Fundamental IoT use cases, concepts, models and technologies are covered in plain English, along with introductory coverage of IoT architecture and IoT messaging with REST, HTTP and CoAp.

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Module 6: Cloud Architecture

This course provides a technical drill-down into the inner workings and mechanics of foundational cloud computing platforms. Private and public cloud environments are dissected into concrete, componentized building blocks (referred to as “patterns”) that individually represent platform feature-sets, functions and/or artifacts, and are collectively applied to establish distinct technology architecture layers. Building upon these foundations, Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) environments are further explored, along with elasticity, resiliency, multitenancy and associated containerization extensions as primary characteristics of cloud platforms.

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Module 7: Blockchain Architecture

This course delves into blockchain technology architecture and the inner workings of blockchains by exploring a series of key design patterns, techniques and related architectural models, along with common technology mechanisms used to customize and optimize blockchain application designs in support of fulfilling business requirements.

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Module 8: IoT Architecture

This course provides a drill-down into key areas of IoT technology architecture and enabling technologies by breaking down IoT environments into individual building blocks via design patterns and associated implementation mechanisms. Layered architectural models are covered, along with design techniques and feature-sets covering the processing of telemetry data, positioning of control logic, performance optimization, as well as addressing scalability and reliability concerns.

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Module 9: Fundamental Big Data

This foundational course provides an overview of essential Big Data science topics and explores a range of the most relevant contemporary analysis practices, technologies and tools for Big Data environments. Topics include common analysis functions and features offered by Big Data solutions, as well as an exploration of the Big Data analysis lifecycle.

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Module 10: Fundamental Machine Learning

This course provides an easy-to-understand overview of machine learning for anyone interested in how it works, what it can and cannot do and how it is commonly utilized in support of business goals. The course covers common algorithm types and further explains how machine learning systems work behind the scenes. The base course materials are accompanied with an informational supplement covering a range of common algorithms and practices.

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Module 11: Fundamental AI

This course provides essential coverage of artificial intelligence and neural networks in easy-to-understand, plain English. The course provides concrete coverage of the primary parts of AI, including learning approaches, functional areas that AI systems are used for and a thorough introduction to neural networks, how they exist, how they work and how they can be used to process information. The course further establishes a step-by-step process for assembling an AI system, thereby illustrating how and when different practices and components of AI systems with neural networks need to be defined and applied. Finally, the course provides a set of key principles and best practices for AI projects.

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Module 12: Advanced Big Data

This course provides an in-depth overview of essential and advanced topic areas pertaining to data science and analysis techniques relevant and unique to Big Data with an emphasis on how analysis and analytics need to be carried out individually and collectively in support of the distinct characteristics, requirements and challenges associated with Big Data datasets.

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Module 13: Advanced Machine Learning

This course delves into the many algorithms, methods and models of contemporary machine learning practices to explore how a range of different business problems can be solved by utilizing and combining proven machine learning techniques.

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Module 14: Advanced AI

This course covers a series of practices for preparing and working with data for training and running contemporary AI systems and neural networks. It further provides techniques for designing and optimizing neural networks, including approaches for measuring and tuning neural network model performance. The practices and techniques are documented as design patterns that can be applied individually or in different combinations to address a range of common AI system problems and requirements. The patterns are further mapped to the learning approaches, functional areas and neural network types that were introduced in Module 11: Fundamental Artificial Intelligence.

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Module 15: Fundamental Cybersecurity

This course covers essential for understanding and applying Cybersecurity technology and practices. The course provides a comparison of standard IT security with Cybersecurity and further explores how Cybersecurity can be applied to a range of contemporary technologies. Common roles, drivers, benefits and challenges are also covered.

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Module 16: Advanced Cybersecurity

This course delves into a number of advanced Cybersecurity topics, including digital forensics, Cyber intelligence, threat management and Cyber attack incident response and recovery. Numerous common Cyber attacks and threats are explained, along with information about how these threats are typically prevented and countered. Additional topics drill down into controls, mechanisms and practices used to apply Cybersecurity frameworks.

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Module 17: Fundamental RPA

This course covers basic robotic process automation (RPA) techniques and models and explores many RPA usage scenarios. RPA environments are discussed, along with how bots can be used for back-end and front-end integration.

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Module 18: Advanced RPA and Intelligent Automation

This course explores the relationship between artificial intelligence (AI) and RPA and describes how these technologies can be combined to establish intelligence automation (IA) environments.

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