What courses can I do after this?
What jobs can I get?
Data Analyst, Data Technician, Information Analyst
To do this course you should have...
Candidates will likely require five GCSEs at grades 9-4, (especially English, Mathematics and a Science or Technology subject), a relevant Level 3 Apprenticeship or other relevant qualifications and experience.
What about work experience?
|Course Code||Start Date||Costs 16-18||Costs 19-23 (with concessions)||Costs 19+ (no concessions)|
* Please note fees are subject to change.
K1: Range of different types of existing data. Common sources of data - internal, external, open data sets, public and private. Data formats and their importance for analysis. Data architecture - the framework against which data is stored and structured including on premises and cloud.
K2: How to access and extract data from a range of already identified sources
K3: How to collate and format data in line with industry standards
K4: Data formats and their importance for analysis Management and presentation tools to visualise and review the characteristics of data Communication tools and technologies for collaborative working
K5: Communication methods, formats and techniques, including: written, verbal, non-verbal, presentation, email, conversation, audience and active listening Range of roles within an organisation, including: customer, manager, client, peer, technical and non-technical
K6: The value of data to the business How to undertake blending of data from multiple sources
K7: Algorithms, and how they work using a step-by-step solution to a problem, or rules to follow to solve the problem and the potential to use automation
K8: How to filter details, focusing on information relevant to the data project
K9: Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data
K10: The range of common data quality issues that can arise e.g. misclassification, duplicate entries, spelling errors, obsolete data, compliance issues and interpretation/ translation of meaning
K11: Different methods of validating data and the importance of taking corrective action
K12: Communicating the results through basic narrative
K13: Legal and regulatory requirements e.g. Data Protection, Data Security, Intellectual Property Rights (IPR), Data sharing, marketing consent, personal data definition. The ethical use of data
K14: The significance of customer issues, problems, business value, brand awareness, cultural awareness/ diversity, accessibility, internal/ external audience, level of technical knowledge and profile in a business context
K15: The role of data in the context of of the digital world including the use of eternal trusted open data sets, how data underpins every digital interaction and connectedness across the digital landscape including applications, devises, IoT, customer centricity
K16: Different learning techniques, learning techniques and the breadth and sources of knowledge
S1: Source and migrate data from already identified different sources
S2: Collect, format and save datasets
S3: Summarise and explain gathered data
S4: Blend data sets from multiple sources and present in format appropriate to the task
S5: Manipulate and link different data sets as required
S6: Use tools and techniques to identify trends and patterns in data
S7: Apply basic statistical methods and algorithms to identify trends and patterns in data
S8: Apply cross checking techniques for identifying faults and data results for data project requirements
S9: Audit data results
S10: Demonstrate the different ways of communicating meaning from data in line with audience requirements
S11: Produce clear and consistent technical documentation using standard organisational templates
S12: Store, manage and distribute in compliance with data security standards and legislation
S13: Explain data and results to different audiences in a way that aids understanding.
S14: Review own development needs
S15: Keep up to date with developments in technologies, trends and innovation using a range of sources
S16: 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.
S17: Operate as part of a multi-functional team
S18: Prioritise within the context of a project
More about the course
Typical job duties include:
Duty 1 Source data from a collection of already identified trusted sources in a secure manner
Duty 2 Collate and format data to facilitate processing and presentation for review and further advanced analysis by others
Duty 3 Present data for review and analysis by others, using required medium for example tables, charts and graphs
Duty 4 Blend data by combining data from various sources and formats to explore its relevance for the business needs
Duty 5 Analyse simple and complex structured and unstructured data to support business outcomes using basic statistical methods to analyse the data.
Duty 6 Validate results of analysis using various techniques, e.g cross checking, to identify faults in data results and to ensure data quality
Duty 7 Communicate results verbally, through reports and technical documentation and tailoring the message for the audience
Duty 8 Store, manage and share data securely in a compliant manner
Duty 9 Collaborate with people both internally and externally at all levels with a view to creating value from data
Duty 10 Practise continuous self learning to keep up to date with technological developments to enhance relevant skills and take responsibility for own professional development