Course Summary

This apprenticeship programme aims to equip learners with the skills and knowledge required of a successful Data Analyst. Our training underpins the knowledge, skills and behaviours relevant to the role, including data architecture, analytical tools & techniques, and data migration, manipulation and presentation. Over the course of the programme, apprentices will learn through a combination of technical training, competency-based workshops led by a Baltic Learning Mentor, and practical work-based assignments.



Programme Duration

18 Months

Training Duration

5 Weeks

Training Method

Smart Classroom

Training Schedule

8-12 Weeks


Over the course of the apprenticeship we will equip learners with the following skills and competencies:

  • Data Architecture
  • Collecting and Compiling Data
  • Data Migration
  • Data Manipulation
  • Data Analysis Security
  • Data Quality
  • Performing Queries
  • Analytical Tools & Techniques
  • Statistical Analysis
  • Reporting Data
  • Presenting Data

This unit is delivered over two separate weeks through the SMART Classroom, and will cover the following:

  • Types of data
  • The data lifecycle
  • Structured and unstructured data
  • Requirements for data analysis
  • Quality issues for data analysis
  • Data analysis tasks

There will be an exam at the end of this unit.


Data Analysis Tools

This unit is delivered over two separate weeks through the SMART Classroom. It will cover the following content:

  • Processes and tools used for data integration
  • Purpose and outputs of data integration activities
  • Programming languages for statistical computing can be applied to data integration activities to filter and prepare data for analysis
  • Nature and challenges of data volumes and types being processed through data integration activities
  • Testing strategies to ensure that unified data sets are correct, complete and up to date.
  • Industry standard tools and methods for data analysis
  • Data manipulating, processing, cleaning and analysis capabilities of statistical programming languages and proprietary software tools
  • Applying statistical programming languages in preparing data for analysis and conducting analysis projects

    There will be an exam for this unit at the end of the second week.
Implementing Data Analysis Concepts & Tools

Taking the theory from the previous training units, apprentices will implement what they have learned, completing a Data Analysis project. This instructor-led course helps learners to put their theory into practice and is excellent preparation for the Synoptic Project element of their End Point Assessment. This unit takes place over one week.


As an added extra, apprentices can choose to complete one of the following vendor qualifications:

  • Designing an Azure Data Solution  
  • Designing and implementing a Data Science Solution on Azure
  • Implementing an Azure Data Solution
Functional Skills
  • Level 2 - Maths
  • Level 2 - English

Learners who require Functional Skills, will be supported via SMART Classroom, support sessions will last half a day each. Exams will be held in a local test centre.

End Point Assessment

The End Point Assessment is completed in the last few months of the apprenticeship. It includes an Employer Reference, Summative Portfolio, Synoptic Project and an Interview.

Smart Classroom

Our apprentices come together from all over the country and work together in the smart learning environment, interacting, communicating and engaging with the best technical trainers in IT, Software and Digital Marketing. Our SMART Classrooms are technology enhanced, cloud-based classrooms that deliver training by integrating learning technology.

Workplace Curriculum

On this programme, an apprentice is required to evidence the following activities in the workplace:

  • 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 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
  • Works with the organisation's data architecture

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