Data analysis essentials

_{Month 1}

Foundations of a Data Analyst

• Learn the basics of data analysis and key statistical concepts, and gain familiarity with various data types and structures.
• Develop an understanding and skills in exploratory data analysis to filter, sort and calculate descriptive statistics.

_{Month 2}

Foundations of data management

• Understand principles of data management and governance, developing skills in ensuring data accuracy and quality.
• Maintain data integrity and compliance with regulations by developing and applying robust data governance frameworks.

Visualising and integrating data with a BI tool

_{Month 3}

Visualising data for stakeholders

• Design impactful data visualisations and present insights effectively. Tailor data stories to different audiences and improve the clarity of data for informed decision-making.
• Develop communication skills and enhance collaboration with stakeholders.

_{Month 4}

Integrating data for business impact

• Understand database design principles and data modeling.
• Learn techniques for expanding and integrating datasets, building efficient and scalable database systems with data accessibility and usability at its core.

_{Month 5}

Data analysis hackathon and EPA prep session

• Working together on a challenge that allows apprentices to use the skills learned so far.
• Working session to learn more about the end point assessment (EPA), practice for interviews, and work on evidence.

Levelling up data analysis with statistics and AI

_{Month 6}

Data integration and analysis techniques

• Develop skills in SQL and data integration techniques, combining and manipulating data from various sources.
• Enable seamless data integration across platforms and support greater granularity in strategic decision-making with comprehensive datasets.

_{Month 7}

Advanced analytics and statistical methods

• Understand and apply advanced statistical techniques, deriving deeper insights from data.
• Uncover hidden trends and patterns for precise decision-making and evaluate data for use in predictive analytics.

_{Month 8}

Statistics hackathon and EPA prep session

• Working together on a challenge that allows apprentices to use the skills learned so far.
• Working session to learn more about the end point assessment (EPA), practice for interviews, and work on evidence.

Machine learning and predictive analytics

_{Month 9}

Predicting the future with time series forecasting

• Analyse time series data and build forecasting models.
• Evaluate and improve forecasting accuracy for strategic planning and anticipate trends and patterns for more effective forecasts and planning.

_{Month 10}

Introduction to machine learning

• Learn the basics of machine learning and model implementation and how to develop, train and optimise models.
• Incorporate machine learning for more effective automation and drive innovation with predictive analytics.

_{Month 11}

Machine learning hackathon

• Working together on a challenge that allows apprentices to use the skills learned so far.

EPA prep

_{Month 12}

End point assessment (EPA) preparation

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