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Advanced Data Fellowship Top-Up

Multiverse Advanced Data Fellowship Top Up: data infrastructure, strategy, technical skills, aligned to Level 6 BSc Data Analytics app.

Updated this week

Advanced Data Fellowship Top Up Programme Overview

This programme provides data analysts with degree-level skills in designing data storage solutions, utilising machine learning and other tools to drive efficiency, and automating data flows. It is part of our Data Academy Programmes.

Key Skills Gained:

  • Python

  • Machine learning

  • Data strategy and governance

  • Data infrastructure

Business Outcomes:

  • Increase efficiencies and drive cost savings: Enable teams to design, develop and integrate technological software and data storage solutions to deliver against business needs.

  • Power data-driven decision making: Help teams leverage advanced applied statistics and machine learning algorithms to provide actionable insights.

  • Support digital and data transformation initiatives: Build highly strategic data teams, who can ensure data strategy is developed in line with business strategy and objectives.

Apprenticeship Qualification Achieved: Level 6 Degree apprenticeship, BSC (Hons)

  • Digital and Technology

  • Solutions Professional

  • Apprenticeship Standard

Duration: 2 years, plus 1 month assessment


Advanced Data Fellowship Top Up Indicative Curriculum Breakdown

Year one: Data engineering

  • Months 1-3: Creating efficient and secure data infrastructure

    • Design simple data solutions

    • Considerations of networking and security in storage and flow of data

    • Evaluate data storage solutions, including SQL and NoSQL

    • Save costs through additional efficiency and security

  • Months 4-6: Accelerating data solutions with DevOps principles

    • Deepen understanding of a software system frequently used

    • Create software

    • Deploy software

    • Use software to add efficiency to data analysis or processing

  • Months 7-9: Driving business value with data engineering

    • Design and implement data solutions

    • Role of data storage in automation and analytics

    • Design a data engineering solution

    • Use solution to enable decision-making and quality analytics

  • Months 10-12: Advancing your data strategy and governance

    • Align data projects and technology to strategic goals

    • Understanding governance for data strategy and analytics

    • How technology can more efficiently and effectively drive business value

Year two: Data strategy

  • Months 1-3: Managing data transformation projects

    • Plan a project

    • Refine your approach to managing risk, stakeholders and budgets

    • Refine your communication skills

    • Gaining buy-in to kick off and independently lead on value-add projects

  • Months 4-6: Enhancing decision making with statistics

    • Design an experiment

    • Test a hypothesis

    • Effectively communicate results

    • Communication and visualisation techniques

    • Insight for more robust decision-making and risk mitigation

  • Months 7-9: Leveraging machine learning to improve efficiency

    • Find a problem that machine learning could solve

    • Develop your knowledge of machine learning methodology and algorithms

    • Train a machine learning model

    • Produce insight at larger scales, handling big data for more efficient and effective decision-making

  • Months 10-12: Capstone project

    • Produce a work-based portfolio combining prior learning

    • Create (or significantly improve) a data product and write it up throughout these final 3 months

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


Advanced Data Fellowship Top Up Indicative Delivery Model

Monthly delivery model, approx. 32 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 (60% - 20 hours/month):

    • Asynchronous learning (9 hours): Online, self-paced content that sets the foundation of skills for the module.

    • Group learning (10 hours): Live, instructor-led, interactive learning that dives deeper and reinforces the asynchronous content.

    • Coach and peer support (1 hour): Includes tutoring, progress reviews, & other individual/group/peer support.

  • Working in Existing Role (~40% - 12 hours/month):

    • Work-based tasks (5 hours): Structured tasks to provide the opportunity to apply learnings in real work context.

    • Independent applied learning (7 hours): Application of learning to apprentices’ existing day to day activities.


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