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DIGITAL TRANSFORMATION
CCP   SOACP   BDSCP  
NEXT-GEN IT   JUNIOR

Next-Gen IT Courses

The Next-Gen IT curriculum from Arcitura is comprised of a series of 3-module programs. Course delivery options include instructor-led virtual training, on-site training and self-paged self-paced training with eLearning and printed 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.

NGIT_BT_01

Business Technology Module 1
Business Automation Technology Overview

This course provides introductory, non-technical coverage of Cloud Computing, Robotic Process Automation (RPA) and the Internet of Things (IoT). The course content is intentionally limited to understanding the drivers, benefits, goals, risks and challenges of these technologies. This course is indented for non-technical managers and IT professionals that only require a general understanding of the topics.

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NGIT_BT_02

Business Technology Module 2
Data Science Technology Overview

This course provides introductory, non-technical coverage of Big Data, Machine Learning and Artificial Intelligence (AI). The course content is intentionally limited to understanding the drivers, benefits, goals, risks and challenges of these technologies. This course is indented for non-technical managers and IT professionals that only require a general understanding of the topics.

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NGIT_BT_03

Business Technology Module 3
Digital & Security Technology Overview

This course provides introductory, non-technical coverage of Digital Transformation, Blockchain and Cybersecurity. The course content is intentionally limited to understanding the drivers, benefits, goals, risks and challenges of these technologies. This course is indented for non-technical managers and IT professionals that only require a general understanding of the topics.

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NGIT_RPA_01

RPA Module 1
Fundamental RPA

This course establishes the components and models that comprise contemporary robotic process automation (RPA) environments. Different types of RPA bots are explained, along with different RPA architectures and bot utilization models. This course further provides detailed scenarios that demonstrate different deployments of RPA bots and other components in relation to different business automation requirements.

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NGIT_RPA_02

RPA Module 2
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. The course covers different types of autonomous decision-making and further extends the usage scenarios from Module 1 by incorporating Artificial Intelligence (AI) systems as part of intelligent automation solutions.

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NGIT_RPA_03

RPA Module 3
RPA Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in RPA models and practices as they are applied and combined to common usage scenarios.

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NGIT_CS_01

Cybersecurity Module 1
Fundamental Cybersecurity

This course covers essential topics for understanding and applying cybersecurity solutions and practices. The course begins by covering basic aspects of cybersecurity and then explains foundational parts of cybersecurity environments, such as frameworks, metrics and the relationship between cybersecurity and data science technology.

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NGIT_CS_02

Cybersecurity Module 2
Advanced Cybersecurity

This course delves into the building blocks of cybersecurity solution environments and further explores the range of cyber threats that cybersecurity solutions can be designed to protect organizations from. The course beings by establishing a set of cybersecurity technology mechanisms that represent the common components that comprise cybersecurity solutions. The course then explores a series of formal processes and procedures used to establish sound practices that utilize the mechanisms. The course concludes with comprehensive coverage of common cyber threats and attacks and further explains how each can be mitigated using the previously described mechanisms and processes.

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NGIT_CS_03

Cybersecurity Module 3
Cybersecurity Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will help prove proficiency in Cybersecurity technologies and practices, as they are utilized and combined to solve real-world problems.

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containerization_m01

Containerization Module 1
Fundamental Containerization

This course provides comprehensive coverage of containerization models, technologies, mechanisms and environments. How the utilization of containers impacts both the technology and business of an organization are covered, along with many technical features, characteristics and deployment environments.

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containerization_m02

Containerization Module 2
Containerization Technology & Architecture

This course provides a deep-dive into containerization architectures, hosting models, deployment models and utilization by services and applications. Numerous advanced topics are covered, including high performance requirements, clustering, security and lifecycle management.

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containerization_m03

Containerization Module 3
Containerization Technology & Architecture Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will help prove hands-on proficiency in containerization concepts, technologies, architecture models and pattern application, as they are utilized and combined to solve real-world problems.

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iot_m01

Internet of Things Module 1
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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iot_m02

Internet of Things Module 2
IoT Technology & 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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iot_m03

Internet of Things Module 3
IoT Technology & Architecture Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will help prove hands-on proficiency in IoT concepts, technologies, architecture models and devices, as they are applied and combined to solve real-world problems.

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artificialintelligence_m01

Artificial Intelligence Module 1
Fundamental Artificial Intelligence

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 establishes the five primary business requirements AI systems and neural networks are used for, and then maps individual practices, learning approaches, functionalities and neural network types to these business categories and to each other, so that there is a clear understanding of the purpose and role of each topic covered. 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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artificialintelligence_m02

Artificial Intelligence Module 2
Advanced Artificial Intelligence

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 1: Fundamental Artificial Intelligence.

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artificialintelligence_m03

Artificial Intelligence Module 3
Artificial Intelligence Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in AI, machine learning and deep learning systems and neural network architectures, as they are applied and combined to solve real-world problems.

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machinelearning_m01

Machine Learning Module 1
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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machinelearning_m02

Machine Learning Module 2
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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machinelearning_m03

Machine Learning Module 3
Machine Learning Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in machine learning systems and techniques, as they are applied and combined to solve real-world problems.

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blockchain_m01

Blockchain Module 1
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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blockchain_m02

Blockchain Module 2
Blockchain Technology & 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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blockchain_m03

Blockchain Module 3
Blockchain Technology & Architecture Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove hands-on proficiency in blockchain technologies, mechanisms and security controls as they are applied and combined to solve real-world problems.

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devops_m01

DevOps Module 1
Fundamental DevOps

A comprehensive overview of DevOps practices, models and techniques, along with coverage of DevOps benefits, challenges and business and technology drivers. Also explained is how DevOps compares to traditional solution development and release approaches and how the application of DevOps can be monitored and measured for concrete business value.

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devops_m02

DevOps Module 2
DevOps in Practice

A course that delves into the application of DevOps practices and models by exploring how the DevOps lifecycle and its associated stages can be carried out and further identifying related challenges and considerations. In-depth coverage is provided for the application of Continuous Integration (CI) and Continuous Delivery (CD) approaches, along with an exploration of creating deployment pipelines and managing data flow, solution versions and tracking solution dependencies.

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devops_m03

DevOps Module 3
DevOps Lab

A lab during which participants apply the concepts, processes, techniques and metrics previously covered in order to complete a set of exercises. Specifically, participants are required to study case study backgrounds and carry out a series of exercises to establish DevOps processes and carry out DevOps stages and related techniques to address requirements and solve problems.

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