Level 4 Data Analyst
Course Outline
This Data Analyst apprenticeship is designed for professionals in diverse sectors, teaching essential skills for effective data utilisation, problem-solving, and evidence-based decision-making. Covering areas from identifying data sources to drawing conclusions and ensuring ethical data handling, the curriculum provides practical insights through scenarios like analysing social media trends, sales figures, and staff retention rates. Participants gain proficiency in statistical analysis, data visualisation, and stakeholder communication, preparing them for the dynamic field of data analytics.
Duration: 15 months + EPA
Cost : £15,000 | 5% Contribution: £750
Entry Requirements:
- 4 / C or above GCSE Maths & English
Course Content
Fundamentals of Data Analysis
Learn about current legislation, organisational data security, the data life cycle, and ethical considerations, forming the foundation for secure and ethical data use.
Structured and Unstructured Data Fundamentals
Explore the differences between structured and unstructured data, grasp database system fundamentals, and understand user experience and customer requirement definitions.
Quality Risks and Organisational Approaches
Identify and mitigate quality risks in data, combine data effectively, understand organisational tools, methods, and data architecture for robust data analysis.
Principles of Statistics and Analytics
Understand the principles of statistics for dataset analysis and delve into descriptive, predictive, and prescriptive analytics techniques for comprehensive data insights.
Secure Data Systems and Lifecycle Implementation
Apply secure data system practices and implement the stages of the data analysis lifecycle, ensuring ethical and compliant data handling.
Customer Requirements and User Experience
Undertake customer requirements analysis and assess the impact of user experience on data analysis, enhancing the relevance and effectiveness of analytical outputs.
Data Classification, Set Analysis, and Quality Risks
Apply data classification principles, analyse datasets with varied structures, and manage quality risks, ensuring accurate and reliable data insights.
Data Source Identification and Organisational Architecture
Identify data sources, assess risks, and apply organisational architecture requirements, creating a foundation for effective and secure data analysis activities.
Statistical Methodologies and Predictive Analytics
Apply statistical methodologies to analyse datasets, delve into predictive analytics techniques, and use various analytical methods for trend identification and prediction.
Stakeholder Communication and Data Presentation
Collaborate with stakeholders, effectively communicate data insights, and master data presentation techniques, selecting appropriate tools for optimal