L 3 Data Technician

 Often seen as the entry point for new data professionals, or, currently employed staff looking to move into a more data, analytics and technical based career.

A data technician is used in all sectors where data is generated or processed including but not limited to finance, retail, education, health, media, manufacturing and hospitality.

The broad purpose of the occupation is to source, format and present data securely in a relevant way for analysis using many different methods; to communicate outcomes appropriate to the audience; analyse structured and unstructured data to support business outcomes; blend data from multiple sources as directed and apply legal and ethical principles when manipulating data.

This apprenticeship instils a data-driven mindset in employees, teaching them the essential skills and concepts needed to integrate data into their daily tasks.

The programme covers essential tools and techniques for data analysis and visualization, offering options to learn Power BI, Tableau, and Qlik. Participants engage with real-world datasets, gaining a solid foundation in data-driven decision-making. This fully funded, government-approved apprenticeship can transform relatively novice learners by providing practical experience, coaching, and instruction, equipping employees with a data-driven mindset and the core skills needed to work with data in their daily roles.

• Source and migrate data from already identified different sources.
• Collect, format and save datasets.
• Summarise and explain gathered data.
• Blend data sets from multiple sources and present in format appropriate to the task.
• Manipulate and link different data sets as required.
• Use tools and techniques to identify trends and patterns in data.
• Apply basic statistical methods and algorithms to identify trends and patterns in data.
• Apply cross checking techniques for identifying faults and data results for data project requirements.
• Audit data results.
• Demonstrate the different ways of communicating meaning from data in line with audience requirements.
• Produce clear and consistent technical documentation using standard organisational templates.
• Store, manage and distribute in compliance with data security standards and legislation.
• Explain data and results to different audiences in a way that aids understanding.
• Review own development needs.
• Keep up to date with developments in technologies, trends and innovation using a range of sources.
• Clean data i.e. remove duplicates, typos, duplicate entries, out of date data, parse data (e.g. format telephone numbers according to a national standard) and test and assess confidence in the data and its integrity.

• Operate as part of a multi-functional team.
• Prioritise within the context of a project Manage own time to meet deadlines and manage stakeholder expectations.
• Work independently and take responsibility.
• Use own initiative.
• A thorough and organised approach.
• Work with a range of internal and external customers.
• Value difference and be sensitive to the needs of others.

The L4 Data Analyst has more of an emphasis on strategic analysis and business context, regulation and compliance than the L3 technician. It can be undertaken directly, or as a progression from a L3 Data Technician apprenticeship.

In today’s world, data analysis plays a crucial role in making decisions and increasing operational efficiency. If you want to develop the data analytics expertise in your business, this L4 Data Analyst Apprenticeship will empower employees with the skills your business needs. The Top 5 skills that we teach are:

1. Problem Solving – Identify business problems or challenges and formulate data-driven solutions. Collaborate with cross-functional teams to understand requirements and provide analytical support.

2. Technical Skills – being proficient in Python and R for data manipulation and analysis

3. Analytical Skills – Having the ability to clean and preprocess data to ensure its quality and accuracy

4. Communication Skills – Communicating insights to non-technical stakeholders through storytelling

5. Data Excellence – Ensuring data security, ethics and compliance are maintained, monitored and updated

Data Engineering is one of the most in-demand technical skillsets that a business requires. This programme is designed to equip learners with the skills and knowledge necessary to build, manage, and optimize data systems in any organization.

As a key role in a data, analytics, business intelligence or business improvement team, a qualified data engineer will build systems that collect, manage, and convert data into usable information for data scientists, data analysts, business leaders and business intelligence analysts to interpret. A data engineer’s main aim is to make data accessible and valid so that an organisation can use it to evaluate and optimise their performance or to analyse better the need of customers. The role of the data engineer is pivotal to any organisation; they ensure that data pipelines are established to support data scientists and other business stakeholders.

Learners gain the ability to support business functions by creating and maintaining data analytics pipelines. They develop the skills to access organizational data and gain insights into the data engineering lifecycle, data modelling and more, enabling organizations to fully leverage the value of their data.

• Build processes to monitor and optimise the performance of the availability, management and performance of data product.
• Use methodologies for moving data from one system to another for storage and further handling.
• Learn data normalisation principles and the advantages they achieve in databases for data protection, redundancy, and inconsistent dependency.
• Learn frameworks for data quality, covering dimensions such as accuracy, completeness, consistency, timeliness, and accessibility.
• Understand inherent risks of data such as incomplete data, ethical data sources and how to ensure data quality.
• Learn software development principles for data products, including debugging, version control, and testing.
• Recognise principles of sustainable data products and organisational responsibilities for environmental social governance.
• Learn deployment approaches for new data pipelines and automated processes.
• Understand how to build a data product that complies with regulatory requirements.
• Grasp concepts of data governance, including regulatory requirements, data privacy, security, and quality control. Legislation and its application to the safe use of data.
• Learn data and information security standards, ethical practices, policies and procedures relevant to data management activities such as data lineage and metadata management.
• Understand how to cost and build a system whilst ensuring that organisational strategies for sustainable, net zero technologies are considered.
• Determine the implications of financial, strategic and compliance regarding to security, scalability, compliance and cost of local, remote or distributed solutions.
• Grasp the uses of on-demand Cloud computing platform(s) in a public or private environment such as Amazon AWS, Google Cloud, Hadoop, IBM Cloud, Salesforce and Microsoft Azure.
• Learn data warehousing principles, including techniques such as star schemas, data lakes, and data marts.
• Study the principles of data, including open and public data, administrative data, and research data including the value of external data sources that can be used to enrich internal data. Examples of how business use direct data acquisition to support or augment business operations.
• Learn approaches to data integration and how combining disparate data sources delivers value to an organisation.
• Know how to use streaming, batching and on-demand services to move data from one location to another.
• Know differences between structured, semi-structured, and unstructured data.
• Learn multiple types and uses of data engineering tools and applications in own organisation.
• Know policies and strategies to ensure business continuity for operations, particularly in relation to data provision.
• Learn technology and service management best practice including configuration, change and incident management.
• Learn how to undertake analysis and root cause investigation.
• Determine processes for evaluating prototypes and taking them to implementation within a production environment.
• Understand the lifecycle of implementing data solutions in a business, from scoping, though prototyping, development, production, and continuous improvement.
• Develop data development frameworks and approved organisational architectures.
• Learn the principles of descriptive, predictive and prescriptive analytics.
• Learn continuous improvement methods including how to: capture good practice and lessons learned.
• Study strategies for keeping up to date with new ways of working and technological developments in data science, data engineering and AI.
• Master the methods and techniques used to communicate messages to meet the needs of the audience.

L 7 AI Data Specialist

This course is ideal for professionals looking to drive business impact by utilizing data science tools and developing machine learning models. It is designed to equip learners with the advanced skills and knowledge

The Level 7 AI and Data Science Apprenticeship equips employees with the expertise to execute transformative data science projects that will enhance competitive advantage.

The comprehensive curriculum includes essential tools and techniques such as deep learning, supervised and unsupervised learning, and AI product management

The broad purpose of the skillset is to discover and devise new data-driven AI solutions to automate and optimise business processes and to support, augment and enhance human decision-making. AI Data Specialists carry out applied research in order to create innovative data-driven artificial intelligence (AI) solutions to business problems within the constraints of a specific business context. They work with datasets that are too large, too complex, too varied or too fast, that render traditional approaches and techniques unsuitable or unfeasible. 

AI Data Specialists champion AI and its applications within their organisation and promote adoption of novel tools and technologies, informed by current data governance frameworks and ethical best practices. 

• Use applied research and data modelling to design and refine the database & storage architectures to deliver secure, stable and scalable data products to the business
• Independently analyse test data, interpret results and evaluate the suitability of proposed solutions, considering current and future business requirements
• Critically evaluate arguments, assumptions, abstract concepts and data (that may be incomplete), to make recommendations and to enable a business solution or range of solutions to be achievedCommunicate concepts and present in a manner appropriate to diverse audiences, adapting communication techniques accordingly
• Manage expectations and present user research insight, proposed solutions and/or test findings to clients and stakeholders.Provide direction and technical guidance for the business with regard to AI and data science opportunities
• Work autonomously and interact effectively within wide, multidisciplinary teams
• Coordinate, negotiate with and manage expectations of diverse stakeholders suppliers with conflicting priorities, interests and timescales
• Manipulate, analyse and visualise complex datasetsSelect datasets and methodologies most appropriate to the business problem
• Apply aspects of advanced maths and statistics relevant to AI and data science that deliver business outcomes
• Consider the associated regulatory, legal, ethical and governance issues when evaluating choices at each stage of the data process
• Identify appropriate resources and architectures for solving a computational problem within the workplace
• Work collaboratively with software engineers to ensure suitable testing and documentation processes are implemented.
• Develop, build and maintain the services and platforms that deliver AI and data science
• Define requirements for, and supervise implementation of, and use data management infrastructure, including enterprise, private and public cloud resources and services
• Consistently implement data curation and data quality controls
• Develop tools that visualise data systems and structures for monitoring and performance
• Use scalable infrastructures, high performance networks, infrastructure and services management and operation to generate effective business solutions.
• Design efficient algorithms for accessing and analysing large amounts of data, including Application Programming Interfaces (API) to different databases and data sets
• Identify and quantify different kinds of uncertainty in the outputs of data collection, experiments and analyses
• Apply scientific methods in a systematic process through experimental design, exploratory data analysis and hypothesis testing to facilitate business decision making
• Disseminate AI and data science practices across departments and in industry, promoting professional development and use of best practice
• Apply research methodology and project management techniques appropriate to the organisation and products
• Select and use programming languages and tools, and follow appropriate software development practices
• Select and apply the most effective/appropriate AI and data science techniques to solve complex business problems
• Analyse information, frame questions and conduct discussions with subject matter experts and assess existing data to scope new AI and data science requirements
• Undertakes independent, impartial decision-making respecting the opinions and views of others in complex, unpredictable and changing circumstances