Next-Gen Data
Science Program

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.

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.

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.

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.

RPA Module 2 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.