L4 Data Analyst
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This occupation is found in any employer in any sector that uses data to make business decisions. Data analysts may work in various departments within a single employer, (for example finance, sales, HR, manufacturing, or marketing), and in any employment sector, public or private, including retail, distribution, defence, banking, logistics, media, local government etc.
The broad purpose of the occupation is to ascertain how data can be used in order to answer questions and solve problems. Data analysis is a process of requirement-gathering, inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names. In today's world, data analysis plays a crucial role in making decisions more evidence-based and helping organisations operate more effectively
In their daily work, an employee in this occupation interacts with internal or external clients. Internally, the data analyst may work with many people within their organisation, at different levels. Externally a data analyst may provide data analysis services to other organisations on behalf of their employer. Data analysts would normally be office based and work normal business hours.
An employee in this occupation will be responsible for the creation and delivery of their own work, to meet business objectives. The data analyst will be responsible for working within the data architecture of the company and ensuring that the data is handled in a compliant, safe and appropriately secure manner, understanding and adhering to company data policy and legislation. Data analysis is a fast-moving and changing environment, and data analysts need to continue to stay abreast of, and engaged with, changes and trends in the wider industry; including data languages, tools and software, and lessons learnt elsewhere.
Data Analyst Knowledge & Skills Taught:
As with all of our level 3 & 4 apprenticeships, you are not required to have any pre-requisite grades or skills. The purposes is to upskill the individual to the point of industry level competency within the given time period, whilst working within the field. Apprenticeships are a fusion of learning theory and applied experience, to produce applicable competency within a given field.
- Use data systems securely to meet requirements and in line with organisational procedures and legislation, including principles of Privacy by Design
- Implement the stages of the data analysis lifecycle
- Apply principles of data classification within data analysis activity
- Analyse data sets taking account of different data structures and database designs
- Assess the impact on user experience and domain context on the data analysis activity
- Identify and escalate quality risks in data analysis with suggested mitigation/resolutions as appropriate.
- Undertake customer requirements analysis and implement findings in data analytics planning and outputs
- Identify data sources and the risks, challenges to combination within data analysis activity
- Apply organizational architecture requirements to data analysis activities
- Apply statistical methodologies to data analysis tasks
- Apply predictive analytics in the collation and use of data
- Collaborate and communicate with a range of internal and external stakeholders using appropriate styles and behaviours to suit the audience
- 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
- collate and interpret qualitative and quantitative data and convert into infographics, reports, tables, dashboards and graphs
- Select and apply the most appropriate data tools to achieve the best outcome
- Current relevant legislation and its application to the safe use of data
- Organisational data and information security standards, policies and procedures relevant to data management activities
- Principles of the data life cycle and the steps involved in carrying out routine data analysis tasks.
- Principles of data, including open and public data, administrative data, and research data
- The differences between structured and unstructured data
- The fundamentals of data structures, database system design, implementation and maintenance
- Principles of user experience and domain context for data analytics
- Quality risks inherent in data and how to mitigate/resolve these
- Principal approaches to defining customer requirements for data analysis
- Approaches to combining data from different sources
- Approaches to organisational tools and methods for data analysis
- Organisational data architecture
- Principles of statistics for analysing datasets
- The principles of descriptive, predictive and prescriptive analytics
- The ethical aspects associated with the use of and collation of data
- Maintain productive, professional and secure working environment
- Shows initiative, being resourceful when faced with a problem and taking responsibility for solving problems within their own remit
- Works independently and collaboratively
- Logical and analytical
- Identifies issues quickly, enjoys investigating and solving complex problems and applies appropriate solutions. Has a strong desire to push to ensure the true root cause of any problem is found and a solution is identified which prevents recurrence.
- Demonstrates resilience by viewing obstacles as challenges and learning from failure.
- Demonstrates an ability to adapt to changing contexts within the scope of a project, direction of the organisation or Data Analyst role.
- Data Analyst
- Data Manager
- Data Scientist
- Data Modeller
- Data Architect
- Data Engineer
These are the courses roadmaps. It is a portion of the work which will follow through the entirity of the apprenticeship course. With on the job experience, and off-the-job learning rounding out in the rest of the training.
All relevant courses within your selected pathway, for yourself or your employee, can be found below.
Relational Databases and Data Modelling Overview (AWS Only)
The GKA Way
AWS Data Analytics Fundamentals (AWS Only)
Communicating and Storytelling with Data (Microsoft Only)
Python for Data Analysts (AWS Only)
Statistics for Data Analysts
Legislation, Regulation & Ethics
The EPA Way
Getting Started - Data Analyst
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