Advanced Data Fellowship Programme Overview
This innovative degree-level apprenticeship is designed to take data teams' analytical and programming skills to the next level. Learners will deepen their technical expertise in designing data storage solutions, utilising machine learning, 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 4-6 Degree apprenticeship, BSc (Hons) Digital and Technology Solutions (Data Analytics)
Duration: 3 years and 2 months delivery, plus 1 month assessment
Software Requirements: Advanced Data Fellowship Software Requirements
Advanced Data Fellowship Indicative Curriculum Breakdown
Year one: Data Analysis Essentials
Month 1: Foundations of a Data Analyst
Starting out with Python
Data analytics lifecycle
Loading and inspecting data
Data sources and their impact on analysis
Applying data analysis
Month 2: Foundations of data management
Data legislation and GDPR
Privacy by design
Organisational data policy
Principles of data accuracy and quality in Python
Month 3: Visualising and integrating data with Python
Stakeholder and project management
Defining customer requirements
Managing communication strategies
Principles of UX
Month 4: Integrating your data for business impact
Database designs
Entity relationship diagrams
Database manipulation
Using joins to expand the data landscape
Month 5: Data hackathon and end point assessment (EPA) preparation session
Working together on a challenge that allows apprentices to use the skills they’ve learned so far.
Working session to learn more about the EPA, practice for your interview, and work on your evidence.
Month 6: Levelling up data analysis with statistics & AI
Starting with SQL
AI for data analysis
Responsible AI
Integrating data within organisational data architecture in Python
Month 7: Advanced analytics and statistical methods
Useful statistics
Sampling
Standard deviation and standardising
Probability
Hypothesis testing with Python
Month 8: Statistics hackathon and end point assessment (EPA) preparation session
Working together on a challenge that allows you to use the skills you’ve learned so far.
Working session to learn more about the EPA, practice for your interview, and work on your evidence.
Month 9: Machine Learning and Predictive Analytics
Time series data
Identifying trends and patterns
Decompose
Training forecast models
Making and evaluating future predictions
Month 10: Introduction to machine learning
Supervised vs. unsupervised
Training models and clustering models
Interpreting clustering models
Creating insights
Month 11: Machine learning hackathon
Working together on a challenge that allows you to use the skills you’ve learned so far.
Month 12: End point assessment (EPA) preparation
Working session to learn more about the EPA, practice for your interview, and work on your evidence.
Year two: Data infrastructure, governance & 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 solutions to enable decision-making and quality analytics
Months 10-12: Advancing 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 three: 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 Indicative Delivery Model
Monthly delivery model, approx. 27 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 (~50% - 12 hours/month):
Asynchronous learning (6 hours): Online, self-paced content that sets the foundation of skills for the module.
Group learning (5 hours): Live, instructor-led, small-group 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 (~50% - 15 hours/month):
Work-based tasks (7 hours): Structured tasks to provide the opportunity to apply learnings in real work context.
Independent applied learning (8 hours): Application of learning to apprentices’ existing day-to-day activities.