The Data Analyst Apprenticeship

In just 1 hour, you'll get an overview of what it means to be a Data Analyst apprentice, and why as an employer you want to hire a Data Analyst for your team. 

Data Analyst apprentices are taught how to collect, organise and study data to provide business insight. They are typically involved with managing, cleansing, abstracting and aggregating data, and conducting a range of analytical studies on that data.

They'll understand data structures, database systems and procedures and the range of analytical tools used to undertake a range of different types of analyses.

Technical Competencies

Upon completion of their Data Analyst apprenticeship, individuals will be able to:

• identify, collect and migrate data to/from a range of internal and external systems
• manipulate and link different data sets as required
• interpret and apply the organisation's data and information security standards, policies and procedures to data management activities
• collect and compile data from different sources
• perform database queries across multiple tables to extract data for analysis
• perform routine statistical analyses and ad-hoc queries
• use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data
• assist production of performance dashboards and reports
• assist with data quality checking and cleansing
• apply the tools and techniques for data analysis, data visualisation and presentation
• assist with the production of a range of ad-hoc and standard data analysis reports
• summarise and present the results of data analysis to a range of stakeholders making recommendations
• work with the organisation's data architecture

Technical Knowledge and Understanding

Upon completion of their Data Analyst apprenticeship, individuals will understand the:

• range of data protection and legal issues
• data life cycle
• different types of data, including open and public data, administrative data, and research data
• differences between structured and unstructured data
• fundamentals of data structures, database system design, implementation and maintenance
• importance of the domain context for data analytics
• quality issues that can arise with data and how to avoid and/or resolve these
• importance of clearly defining customer requirements for data analysis
• processes and tools used for data integration
• steps involved in carrying out routine data analysis tasks
• industry standard tools and methods for data analysis

Underpinning Skills, Attitudes and Behaviours

• logical and creative thinking skills
• analytical and problem solving skills
• ability to work independently and to take responsibility
• can use own initiative
• a thorough and organised approach
• ability to work with a range of internal and external people
• ability to communicate effectively in a variety of situations
• maintain productive, professional and secure working environment