Level 3 Data Technician
Course Outline
This Data Technician apprenticeships is designed for individuals for roles such as Data Support Analyst or Junior Data Analyst across diverse sectors like finance, retail, and healthcare. Learners develop skills in securely sourcing and formatting data, analysing structured and unstructured data, and blending information from various sources. They also gain proficiency in ethical data manipulation, effective communication of results, secure data management, and collaboration with stakeholders. The course emphasises continuous self-learning to stay abreast of technological advancements and encourages behaviours such as time management, initiative, and valuing diversity.
Duration: 12 months + EPA
Cost : £12,000 | 5% Contribution: £600
Entry Requirements:
- 4 / C or above GCSE Maths & English
Course Content
Data Science Process & Challenges
Understand the end-to-end data science process, from data gathering and preparation to modelling, optimisation, and presentation. Explore key issues, potential challenges, and strategies for assessing model accuracy and issue mitigation.
Data Lifecycle & Processing
Explore the diverse types of existing data, common data sources, and the importance of data formats for analysis. Understand data architecture, both on-premises and in the cloud, and learn how to access and extract data from various sources.
Data Sources & Architecture
Gain insights into collating and formatting data in line with industry standards. Learn to migrate data from different sources, clean datasets, and appreciate the complexities of ensuring data integrity and confidence.
Data Formatting & Management Tools
Explore data formats, management tools, and communication techniques for effective collaboration. Understand the roles within an organisation and appreciate the time it takes to present data meaningfully to end users.
Algorithms & Data Filtering
Explore algorithms, their step-by-step application, and the potential for automation. Learn how to filter details relevant to a data project, apply basic statistical methods, and understand the iterative nature of the data modeling process.
Data Value & Blending
Understand the value of data to the business and learn how to blend data from multiple sources. Manipulate and link datasets, use tools to identify trends, and appreciate the challenges of linking and blending data from diverse sources.
Data Quality & Validation
Address common data quality issues and learn methods of validating data. Apply cross-checking techniques and quality assurance processes to ensure accurate data results for project requirements.
Communicating Data Results
Develop skills in communicating data results through basic narrative and tailored approaches for different audiences. Work with internal and external customers, prioritising effective communication in line with project needs.
Legal & Ethical Dimensions of Data
Explore legal and regulatory requirements in data science, including data protection, security, and intellectual property rights. Understand the ethical use of data and ensure compliance with standards and legislation.
Customer-Centric Data Considerations
Recognise the significance of customer issues, business value, and cultural diversity in a data science context. Learn to explain data and results to different audiences, operate within multi-functional teams, and prioritise project considerations.
Professional Development in Data Science
Explore different learning techniques and knowledge sources in data science. Review personal development needs, manage time effectively, work independently, and engage in ongoing self-improvement at the end of each project.