By the end of this course, you will be able to:

• Define the core principles of software architecture and the role of a software architect in various system levels
• Assess the benefits of coaching and mentoring development teams in alignment with architectural goals
• Evaluate the impact of different architecture patterns on system performance, scalability, and flexibility
• Contrast long-term and short-term goals in software architecture
• Recognize the importance of effective documentation and communication in ensuring project success
• Apply design patterns and architectural principles to solve common software design challenges efficiently
• Distinguish between ACID and CAP Theorems
• Explain SOLID principles and how they enhance maintainability, scalability, & flexibility in object-oriented programming
• Summarize the responsibilities of application, solution, and enterprise architects and how they guide a project
• Describe the benefits and limitations of serverless architecture, microservices, and service-oriented architecture

By the end of this course, you will be able to:

• Define the functions and duties of a software architect within an organizational structure
• Summarize the career path for a software architect and the requisite skills and experiences at each career stage
• Discuss potential earnings for both entry-level and senior positions in the field of software architecture
• List recommended professional certifications for aspiring software architects
• Evaluate the relevance of formal education in pursuing a software architecture career
• Categorize the thought processes and core traits essential to software architecture
• Explain the critical contributions of software architecture to the technology sector
• Recognize the career advantages of pursuing a software architect role
• Analyze technical and interpersonal skills required for software architects and the strategies for their development

By the end of this course, you will be able to:

• Define the fundamental concepts of monolithic and microservices architectures
• Compare and contrast the benefits and challenges of monolithic versus microservices architectures
• Explain how microservices communicate using different methods such as REST APIs and message brokers
• Identify the key components of a CI/CD pipeline in microservices deployment
• Analyze why large-scale systems transition from monolithic to microservices architecture
• Describe how service meshes manage communication between microservices
• Recognize the scenarios where microservices architecture is most beneficial
• Outline the continuous integration, delivery, and deployment processes in microservices
• Distinguish between synchronous and asynchronous communication patterns in microservices
• Identify when to use monolithic architecture versus microservices based on system requirements

By the end of this course, you will be able to:

• Distinguish a software engineer from a hobbyist programmer
• List out the different types of software engineers and their responsibilities
• Recognize the general responsibilities of a software engineer
• Outline the hard and soft skills needed to become a software engineer
• List the different organizations that hire software engineers
• Discuss the degrees and certifications necessary to become a software engineer
• Explain the career progression for software engineers
• Discuss the best way to build your portfolio
• Describe the proper structure you need to build up your resume

By the end of this course, you will be able to:

• Define machine learning and distinguish it from traditional programming
• Identify the main types of machine learning: supervised, unsupervised, and reinforcement learning
• Describe the way algorithms learn from data to make predictions or decisions
• Explain the difference between AI, machine learning, and deep learning
• Illustrate real-world applications of machine learning in everyday technology
• Discuss the ethical concerns surrounding machine learning, including bias and accountability
• Summarize the way deep learning models use neural networks to recognize complex patterns