Course 1: DevOps Foundation Fast Track
Program Objectives
This training program is open to anyone interested in expanding their professional skills. The practical examples and exercises are chosen so they map well to the daily work of Key Account Managers; participation is expressly open to everyone, regardless of current professional role. The course teaches methods, tools, and content that can be applied in many different work contexts.
The following typical tasks from the daily work of Key Account Managers serve as worked examples in the exercises; they can be adapted to other fields of activity during the course:
- Strategy decks
- Executive briefings
- Renewal negotiations
- Joint business plans
- Sponsor relationships
The curriculum covers the fundamental principles and values of DevOps, including its relationship with Agile methodologies and service management. It explores DevOps culture, stakeholder collaboration, and the roles within a DevOps team. Key practices such as Continuous Integration, Continuous Delivery, and Continuous Deployment are detailed. The course also delves into testing methodologies, quality assurance, and the role of containers, microservices, and open-source technologies. Lean thinking, Lean production, and the DevOps tool landscape are examined. Performance measurement, metrics, and Key Performance Indicators (KPIs) are discussed, alongside change management, resilience, and automation strategies. Agile frameworks like Scrum and Kanban, as well as scaled agile frameworks, are presented. Innovative practices such as ChatOps, Dojos, and experimentation are introduced. Value Stream Mapping, Constraint Management, and continuous improvement techniques are explored. The course concludes with practical applications of DevOps, including DevSecOps, Site Reliability Engineering (SRE), and the application of APIs.
Learning Objectives- Understand the core concepts and value proposition of DevOps.
- Grasp the principles of Agile methodologies and their integration with DevOps.
- Identify and understand the importance of DevOps culture and collaboration.
- Comprehend key DevOps practices, including CI/CD and testing.
- Recognize the role of containers, microservices, and open-source tools.
- Apply Lean principles and performance measurement techniques within a DevOps context.
- Understand various Agile frameworks and their relation to DevOps.
- Gain insight into innovative DevOps practices and their implementation.
Career Prospects
This course is relevant for individuals seeking to advance their careers in IT roles that involve software development, IT operations, and system administration. It prepares participants for positions that require an understanding of efficient and collaborative IT workflows, contributing to roles in DevOps engineering, system analysis, and IT project management.
Course 2: AI-Powered Data AnalysisContent
The program begins with an introduction to an AI analysis cockpit, including the use of AI assistants. Participants will learn data preparation and visualization techniques. The course covers automating tasks with AI code accelerators, such as GitHub Copilot, and outlines the AI analysis pipeline from raw data to business strategy. Fundamentals of machine learning, including supervised and unsupervised learning concepts and applications, are taught. Participants will gain experience in building local AI models using tools like Knime and will work on a concluding project. Data cleaning, deep learning in data analysis, feature engineering, and dimensionality reduction are addressed. Model evaluation, performance metrics, and the interpretability and explainability of AI models are discussed. Ethical considerations and data privacy in AI-powered data analysis are covered. The course also includes an overview of AI-powered analysis tools and software solutions, culminating in a practical project to implement an AI-driven data analysis.
Learning Objectives- Utilize AI assistants and tools for data analysis.
- Perform effective data preparation and visualization.
- Automate coding and analysis tasks using AI accelerators.
- Understand and apply the AI analysis pipeline.
- Grasp the fundamentals of machine learning, including supervised and unsupervised learning.
- Develop and evaluate AI models using local AI tools.
- Apply techniques for data cleaning, feature engineering, and dimensionality reduction.
- Understand model evaluation, performance metrics, and AI model interpretability.
- Recognize ethical considerations and data privacy in AI applications.
- Implement an AI-powered data analysis project.
Career Prospects
The skills acquired in this course are applicable in various professional fields, including data science, business intelligence, and IT consulting. Typical roles include data analyst, AI specialist, and business analyst, focusing on extracting value from data through advanced analytical techniques.