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Advanced Software Engineering

Multiverse Advanced Software Engineering: upskill, innovation, productivity, aligned to Level 6 BSc Software Engineering apprenticeship.

Updated over a week ago

Advanced Software Engineering Programme Overview

This programme is designed to upskill software engineers to mid-senior level, enabling them to power business innovation and productivity. Learners will master essential advanced skills in areas like cloud engineering, cybersecurity, and artificial intelligence. It is part of our Software Engineering Academy Programmes.

Key Skills Gained:

  • AI

  • Cybersecurity

  • Cloud

  • Data engineering

  • Data governance

Business Outcomes:

  • Fill in-demand mid-senior roles from within: Develop your existing talent and save on expensive hiring costs.

  • Improved productivity and team capacity: Reduce time to ramp junior software engineers up to full senior-level productivity. Increase output accuracy and decrease quality assurance time, freeing up capacity for senior engineers.

  • Faster innovation: Empower teams to leverage new technologies, drive transformation initiatives, and bring new products to market faster by leveraging productivity and efficiency gains.

Apprenticeship Qualification Achieved: Level 6 Degree apprenticeship, BSc Hons Digital and Technology Solutions (Software Engineering)

Duration: 20 months delivery, plus 4 months assessment


Advanced Software Engineering Indicative Curriculum Breakdown

AI, data strategy, and cyber security

  • Module 1: Cyber security and software development

    • Network design and structure

    • Security awareness and threat prevention

    • Cybersecurity tools and defences

    • Secure coding and programming best practices

    • Identity and access management

    • Security policies, procedures, and regulations

  • Module 2: Driving business value with data engineering

    • Database creation and management

    • Database design and structure

    • Data storage and pipelines

    • Data analysis and insights

  • Module 3: Advancing your data strategy and governance

    • Organisational strategy

    • Data strategy

    • Data governance

    • Ethical, diverse, and sustainable data solutions

    • Leadership for strategy implementation

  • Module 4: Integrating machine learning and AI to drive business value

    • Fundamentals of ML and AI

    • Machine learning algorithms and models

    • Data preprocessing and cleaning

    • Deep learning

    • Privacy, legal, and ethical considerations

    • Applications of ML and AI in business

    • Integrating ML and AI into software products

Advanced software transformation and final project

  • Module 5: Managing software transformation projects

    • Approaches to project management

    • Business modelling and project initiation techniques

    • Project budgeting and cost controls

    • Project risk management and contingency

  • Module 6: Cloud computing and scalable architectures

    • Cloud computing models and serverless computing

    • Cloud infrastructure services (IaaS)

    • Cloud platform services (PaaS)

    • Scalability, fault tolerance, and distributed systems

    • Integrating cloud technologies into software applications

  • Module 7: Software testing and design patterns

    • Design principles

    • Unit, integration, system, acceptance, and non-functional testing

    • Designing effective test cases

    • Software testing methodologies

    • Test automation frameworks and popular testing tools

Assessment

  • Module 8: Capstone project

    • Critically evaluate the objectives, challenges, and outcomes of a specific software project using appropriate research methodologies, tools, and techniques to collect and analyse relevant data. Produce a final report that presents key findings, insights, and recommendations.

  • Module 9: End point assessment

    • Assessment to obtain L6 certification and BSc (Hons) Degree.

Note: This is an example curriculum, and specific details may vary per cohort.


Advanced Software Engineering Indicative Delivery Model

Monthly delivery model, approx. 25 hours per month total commitment. The exact time commitment will be outlined in the training plan that apprentices will receive at the start of their apprenticeship.

Structured Learning:

  • Asynchronous learning (6 hours): Online, self-paced content that sets the foundation of skills for the module.

  • Group learning (12 hours): Live, instructor-led, small-group interactive learning that dives deeper and reinforces the asynchronous content.

  • Coach support (4 hours): Includes tutoring, progress reviews, & other individual/group support.

  • Peer support (2 hours): Peer support and feedback on project deliverables.


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