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

Visualising and integrating data with Python

_{Month 3}

Visualising your data for stakeholders

• 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.

Levelling up data analysis with statistics & AI

_{Month 6}

Data integration and analysis techniques

• 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.

Machine Learning and Predictive Analytics

_{Month 9}

Predicting the future with time series forecasting

• 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.

End point assessment preparation

_{Month 12}

End point assessment (EPA) preparation

• Working session to learn more about the EPA, practice for your interview, and work on your evidence.

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

Data strategy

_{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

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

Data science

_{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

Capstone project

_{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