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["Quick reference guide to KSB mapping",null,"Table to assist with B11-13"]
[null,null,"Reference","Name of occupational standard"]
["1. START HERE",null,"ST0117","Business analyst (level 4) (ST0117)"]
["Awarding body: ",null,"ST1021 - CSE","Cyber security technologist (level 4) (ST1021) - Cyber security engineer"]
["NCFE  ",null,"ST1021 - CRA","Cyber security technologist (level 4) (ST1021) - Cyber risk analyst"]
["Qualification name: ",null,"ST1021 - D&R","Cyber security technologist (level 4) (ST1021) - Cyber defend & respond"]
["NCFE Level 4 Diploma: Data Analyst (Higher Technical Qualification) ",null,"ST0118","Data analyst (level 4) (ST0118)"]
["Programme title if applicable:",null,"ST0825","DevOps engineer (level 4) (ST0825)"]
["Please type the programme title here",null,"ST0863","Digital accessibility specialist (level 4) (ST0863)"]
["Please select the occupational standard(s) you are mapping in this spreadsheet","Dropdown Options","ST0345","Digital community manager (level 4) (ST0345)"]
["Standard 1 - select from the dropdown list to the right","ST0118","ST0127","Network engineer (level 4) (ST0127)"]
["Standard 2 - select from the dropdown list to the right",null,"ST0116","Software developer (level 4) (ST0116)"]
["Standard 3 - select from the dropdown list to the right",null,"ST0129","Software tester (level 4) (ST0129)"]
["If your qualification maps to more than three occupational standards, please contact us at HTQ.approvals@education.gov.uk"]
[]
["2. GUIDE TO KSB MAPPING  "]
["Awarding bodies must demonstrate that a qualification will enable a person to demonstrate that they have attained as many of the knowledge, skills and behaviours set out in the standard as may be reasonably expected by undertaking a course of education. "]
[]
["Before you begin, please list your qualification modules in the 'Structure' tab."]
["1. You can map your qualification to one or more occupational standards. "]
["2. Select the occupational standard(s) you wish to map to your qualification by clicking the blue STxxxx link in the reference column, to the right."]
["3. When the occupational standard opens, double check the name of the standard is correct. "]
["4. Please respond to each KSB statement."]
["5. Populate the Detailed page/paragraph reference, KSB coverage, Evidence, Assessment method and Supporting comments/rationale columns, as shown in the example below. "]
["6. Please be as specific as possible about which content in the qualification aligns to each KSB statement. "]
["7. You may delete sheets/tabs that are not relevant to  your qualification. "]
["8. Submit the completed spreadsheet along with any documents you have cited in the 'EVIDENCE UPLOAD' part of the application form. Where we have capacity we are happy to feedback on early drafts of your KSB mapping."]
["9. Please contact HTQ.approvals@education.gov.uk if you need further help or guidance on mapping."]
[]
["3. EXAMPLE OF KSB MAPPING"]
["Please see the KSB mapping examples to the right. ","KSB Number","This is the list of KSB statements","Where in the qualification is the content? \nIndicate the reference document(s)/module(s)/unit(s) and page/paragraph number(s) you are citing as evidence of coverage","Select the coverage from the drop down list","Paste or type clear evidence supporting your KSB coverage. For example, learning outcomes or indicative module content","Provide information on how this KSB is assessed, i.e. the method of assessment used.  ","Provide comments and/or a rationale to support the evidence you have cited. If the KSB mentions key terms not explicitly referenced in your evidence, please explain how they are covered. If you have partially met or not met a KSB, provide a rationale to support your judgement"]
[null,"KSB","KSB Statement ","Detailed page/paragraph reference","KSB coverage","Evidence","Assessment method","Supporting comments/rationale"]
[null,"K1","Understand the background legislation and regulatory frameworks (such as IBL 188) relating to the occupation. ","Module 1.1 Legal and Ethical Considerations; p44-45, para 4-7.\nModule 4.2 Professional Issues and CPD; p9, para 3-4. \nQualification specification p4.","FULLY MET","MO1.1 LO3 Understand and critically reflect on legislation relating to and affecting the occupation.\nIndicative content\nLegal background; legislation; role of industry bodies; national/international considerations. \n\nMO4.2 LO2 Understand the importance of staying up to date with professional developments in the field, including legal frameworks, industry best practice, guidelines and relevant standards. ","Students are required to produce a written report using a brief provided. Within this report students are expected to cover relevant laws, legislation and industry body standards relevant to the brief. ","The detailed coverage of relevant law and legislation is taught in Year 1. It is covered again in Year 2, where learners reflect on it from a  professional perspective. The national regulatory framework and industry body standards (e.g. IBL 188) are covered in detail. "]
[null,"K2","The principles, features and differences of waterfall and agile methodologies for project delivery and software development.","Module 3. Software development; p.6, para 4-7.","FULLY MET","MO3 LO 1 Critically analyse and evaluate approaches to software development methodologies. Theory, method and application.\n\nIndicative content \nUse of different software methodologies; matching methodology to function. \nA range of software methodologies are covered and applied to software problems.","K2 is assessed via a project. Within this project students will be expected to demonstrate practical application and produce written project report(s) which evidence methodologies and methods. ","Waterfall and agile are not explicitly referred to in the LO or indicative content, although they are both fully covered in this module. We cover four approaches to software development, providing background, theory and practical applications. Learners will get 'hands on' experience of evaluating waterfall and agile, including their use in real-world problem solving and project delivery."]

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["Return to Guide"]
["Please list all the modules in the qualification",null,null,null,null,null,null,null,null,null,null,"Response"]
["Does every possible combination of modules/units/pathways ensure that a learner achieves competence in the occupation(s) which the TQ is in respect of and for which the Institute has published an occupational standard "]
[]
["Note: If a module is not aligned/mapped to an occupational standard it is classed as additional content"]
[]
["Assessment Component","Themes Covered","Full-Time Year","Part-Time Year","Core/Option","Mapped to Standard(s)","Credits","Level","Delivery Method","Delivery comments",null,null,"AM Analysis","AM Comment","G7 Comment"]
["1: ORSPA: Data cleansing and security","1, 2, 3, 4","Year 1","Year 2","Core ","ST0118","n/a",4,"Mixed, Provider setting (e.g. classroom)","100% classroom"]
["2: ORSPA: Database design and joining","2, 3, 4","Year 1","Year 2","Core ","ST0118","n/a",4,"Mixed, Provider setting (e.g. classroom)","100% classroom",null,null,"Substantive Element"]
["3: ORSPA: Insight showcasing and presentation","2, 3, 4, 5","Year 1","Year 2","Core ","ST0118","n/a",4,"Mixed, Provider setting (e.g. classroom)","100% classroom"]
["4: ORSPA: Predictive modelling and statistical learning","2, 3, 4","Year 1","Year 2","Core ","ST0118","n/a",4,"Mixed, Provider setting (e.g. classroom)","100% classroom"]

{"sheet":"ST0118","index":2,"n_rows":43,"n_cols":8,"rows_kept":43,"cut":false,"hidden":false,"merged":[]}
[null,"Data analyst (level 4) (ST0118)"]
[null,"Return to Guide"]
["KSB","KSB Statement ","Detailed page/paragraph reference","Documents Present","KSB coverage","Evidence","Assessment method","Supporting comments/rationale"]
[null,"Knowledge"]
["K1","K1: current relevant legislation and its application to the safe use of data","Theme 1 - K1.1 - page 16",null,"FULLY MET","K1.1 - The learner must understand legislative requirements of data analysis processes\n\nIndicative content: General Data Protection Regulation(GDPR)/Data Protection Act (DPA) 2018; Copyright, Designs and Patents Act 1988; Digital Economy Act 2017","K1.1, Assessment 1, Task 2","In understanding the legislation listed and exemplified as part of K1.1, learners will demonstrate knowledge of current relevant legislation and its application to the safe use of data. The listed and exemplified legislation reflects legislation relevant to the data analyst role."]
["K2","K2: organisational data and information security standards, policies and procedures relevant to data management activities","Theme 1 - K1.2 - page 16",null,"FULLY MET","K1.2 - The learner must understand the purpose of security standards within data analytics\n\nIndicative content: International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC) 27001 requrements; ISO/IEC 9001; ISO/IEC 20252; ISO/IEC 27701","K1.2, Assessment 1, Task 2","In understanding the ISO/IEC standards listed and exemplified as part of K1.2, learners will demonstrate knowledge of current security standards and their influence on policies and procedures relevant to the data analyst role."]
["K3","K3: principles of the data life cycle and the steps involved in carrying out routine data analysis tasks.","Theme 2 - K2.3 - page 19",null,"FULLY MET","K2.3 - The learner must understand the data lifecycle and the steps involved when carrying out routine data analysis tasks\n\nIndicative content: compliance with data governance policies and regulatory requirements throughout; creation; storage; usage; archival; destruction","K2.3, Assessment 2, Task 2","The  content for K2.3 exemplifies the  the data lifecycle, which reflect the steps involved in carrying out routine data analysis tasks. In understanding each stage of the lifecycle and what the impact each stage has for a data analyst, learners will demonstrate knowledge of K3."]
["K4","K4: principles of data, including open and public data, administrative data, and research data","Theme 2 - K2.1 and K2.3 - pages 18-19",null,"FULLY MET","K2.1 - The learner must understand the principles of data management\n\nIndicative content: storage; usage; archival; destruction\n\n\nK2.3 - The learner must understand the data lifecycle and the steps involved when carrying out routine data analysis tasks\n\nIndicative content: creation - types of data (open, closed, public, administrative, research)","K2.1, Assessment 2, Task 2; K2.3, Assessment 2, Task 2","K4 is covered by two stems: K2.1 and K2.3.\n\nK2.1 gives learners the opportunity to understand the principles of data, exemplified as the storage, usage, archiving and destruction of data. \n\nIn understanding the types of data listed as part of K2.3, learners will demonstrate knowledge of open and public data, administrative data and research data.\n\nK2.1 and K2.3 give learners the opportunity to exemplify the knowledge of K4."]
["K5","K5: the differences between structured and unstructured data","Theme 3 - K3.1 - page 22",null,"FULLY MET","K3.1 - The learner must understand the characteristics of structured and unstructured data\n\nIndicative content: structured; semi-structured; unstructured","K3.1, Assessment 2, Task 2","In understanding K3.1, learners will develop a foundational knowledge of the characteristics of structured, semi-structured and unstructured data. Based on stakeholder guidance, semi-structured data was included to support learners' understanding of structured and unstructured data. Their knowledge  of the characteristics of structured, semi-structured and unstructured supports learners in understanding the differences between structured, semi-structured and unstructured data."]
["K6","K6: the fundamentals of data structures, database system design, implementation and maintenance","Theme 3 - K3.2 and K3.3 - pages 22-23",null,"FULLY MET","K3.2 - The learner must understand data structures within data analytics\n\nIndicative content: data structures - array; stack; queue; linked list; tree; binary heap; list; tables\n\n\nK3.3 - The learner must understand the types of database and the factors of database system design, implementation, and maintenance\n\nIndicative content: types of database; database system design; database system implementation; database system maintenance","K3.2, Assessment 2, Task 2, Assessment 4, Task 2; K3.3, Assessment 2, Task 2","K6 is covered by two stems: K3.2 and K3.3.\n\nThe exemplification of different types of data structures in K3.2 reflects the data structures relevant to the data analyst role, giving learners an understanding of the fundamentals of relevant data structures. \n\nThe listed indicative content for K3.3 gives learners an understanding of data structures, database system design, implementation and maintenance.\n\nK3.2 and K3.3 give learners the opportunity to exemplify the knowledge of K6."]
["K7","K7: principles of user experience and domain context for data analytics","Theme 5 - K5.1 - page 30",null,"FULLY MET","K5.1 - The learner must understand factors impacting user experience in data analytics\n\nIndicative content: accessibility requirements; communication; collaboration","K5.1, Assessment 3, Task 2 and Task 3","In taking into account accessibility requirements, communication and collaboration as detailed in K5.1, learners can understand the relevant principles of user experience in the context of data analytics."]
["K8","K8: quality risks inherent in data and how to mitigate/resolve these","Theme 3 - K3.5 and K3.6 - pages 23-24",null,"FULLY MET","K3.5 - The learner must understand quality risks in data and risks of the data combination process\n\nIndicative content: types of quality risks; risks of the data combination process\n\n\nK3.6 - The learner must understand forms of risk mitigation and resolution\n\nIndicative content: validation of original data source; cleansing; model enhancement; risk communication","K3.5, Assessment 1, Task 2; K3.6, Assessment 1, Task 2","K8 is covered by two stems: K3.5 and K3.6\n\nK3.5 details types of quality risks and  risks of the data combination process, giving learners an understanding of quality risks inherent in data. \n\nK3.6 focuses on risk mitigation and resolution, exemplifying the latter part of K8 ('how to mitigate/resolve these'). Forms of mitigation and resolution have been detailed in the content as validation of original data source; cleansing; model enhancement; and risk communication. \n\nTogether, the content of K3.5 and K3.6 fully exemplify the knowledge of K8."]
["K9","K9: principal approaches to defining customer requirements for data analysis","Theme 5 - K5.2 - pages 30-31",null,"FULLY MET","K5.2 - The learner must understand the approach to defining stakeholder requirements for data analytics planning\n\nIndicative content: analysis of stakeholder requirements; data analytics planning","K5.2, Assessment 3, Task 2 and Task 3","K5.2 gives learners the understanding of analysis of stakeholder requirements, which then feeds into data analytis planning. Together, these aspects ensure learners have a foundation of knowledge of the principal approaches to defining customer requirements specifically for data analysis."]
["K10","K10: approaches to combining data from different sources","Theme 3 - K3.4 - page 23",null,"FULLY MET","K3.4 - The learner must understand the techniques of combination\n\nIndicative content: approaches to combination - fuzzy matching; record linkage; statistical matching; conditional matching; special case","K3.4, Assessment 2, Task 2","The listed indicative content for K3.4 gives learners a foundation of knowledge of relevant approaches to combination of data from a range of sources. The exemplification of these approaches supports learners in meeting the requirements of K10. "]
["K11","K11: approaches to organisational tools and methods for data analysis","Theme 4 - K4.1 - page 26",null,"FULLY MET","K4.1 - The learner must understand methods of data analysis and tools used within data analytics\n\nIndicative content: considerations for the selection of appropriate methods and tools; methods of data analysis; tools for methods of data analysis","K4.1, Assessment 2, Task 2","K4.1 gives learners an understanding of the process of selecting data analysis methods and tools as well as detailing methods and tools of data analysis. This supports learners in not only understanding tools and methods for data analysis, but how they would approach and select these tools and methods."]
["K12","K12: organisational data architecture","Theme 4 - K4.3 - page 27",null,"FULLY MET","K4.3 - The learner must understand the responsibilities of a data analyst to meet requirements of organisational data architecture\n\nIndicative content: access and permissions; data security; data ownership; data integrity; data visualisation; legislative compliance in data processing","K4.3, Assessment 2, Task 2","The indicative content listed for K4.3 outlines the responsibilities of a data analyst role that are relevant to the requirements of organisational data architecture. Note that K12 has been interpreted by subject specialist stakeholders as referring to the role a data analyst plays in organisational data architecture. "]
["K13","K13: principles of statistics for analysing datasets","Theme 4 - K4.2 - pages 26-27",null,"FULLY MET","K4.2 - The learner must understand the application of statistical types and methods\n\nIndicative content: statistical types; statistical methods","K4.2, Assessment 4, Task 2","The exemplification of statistical types and methods in K4.2 together support learners in understanding the principles of statistics for analysing datasets."]
["K14","K14: the principles of descriptive, predictive and prescriptive analytics","Theme 4 - K4.4 - pages 27-28",null,"FULLY MET","K4.4 - The learner must understand the principles of descriptive, predictive and prescriptive analytics and analytical approaches with models\n\nIndicative content: descriptive; predictive; prescriptive; analytical approaches within models","K4.4, Assessment 4, Task 2","K4.4 gives learners the opportunity to understand descriptive, predictive and prescriptive analytics. In order to exemplify the principles of descriptive, predictive and prescriptive analytics (K14), K4.4 also details analytical approaches within models in order to give learners a full understanding in this area and to ensure K14 is fully exemplified."]
["K15","K15: the ethical aspects associated with the use of and collation of data","Theme 2 - K2.2 - pages 18-19",null,"FULLY MET","K2.2 - The learner must understand ethical responsibilities within the data analyst role\n\nIndicative content: transparency; accountability; fairness","K2.2, Assessment 1, Task 2","K2.2 exemplifies key aspects of the ethical responsibilities within the data analyst role, applicable to the use and collation of data. Transparency, accountability and fairness were determined as crucial and relevant ethical responsiblities of a data analyst."]
[null,"Skills"]
["S1","S1 use data systems securely to meet requirements and in line with organisational procedures and legislation, including principles of Privacy by Design","Theme 1 - S1.1 - page 17",null,"FULLY MET","S1.1 - The learner must be able to operate data systems in compliance with legislation and organisational policies\n\nIndicative content: review operational requirements against relevant legislation and policies; operate data systems in compliance with relevant legislation and policies","S1.1, Assessment 1, Task 1","In demonstrating skills against S1.1, learners will be able to use data systems securely and in line with procedures and legislation as detailed in S1. S1.1 also supports learners in understanding how to use and incorporate the principles of Privacy by Design."]
["S2","S2 implement the stages of the data analysis lifecycle","Theme 2 - S2.1 - pages 20-21",null,"FULLY MET","S2.1 - The learner must be able to implement the stages of the data lifecycle when carrying out routine data analysis tasks\n\nIndicative content: identify organisational requirements; ensure compliance with data governance policies and regulatory requirements; store data in line with organisational policy; collate and interpret appropriate data; prepare data to meet requirements; analyse and model data; visualise data in appropriate format; archive data in compliance with organisational policy; destroy data in compliance with data governance policies and regulatory requirements","S2.1, Assessment 1, Task 1 and Task 2, Assessment 2, Task 1, Assessment 3, Task 1, Assessment 4, Task 1","S2.1 details how a learner would implement the stages of the data lifecyle, building their skills in this area when carrying out routine data analysis tasks. The indicative content for S2.1 details how a learner would demonstrate each stage of the data lifecycle. "]
["S3","S3 apply principles of data classification within data analysis activity","Theme 4 - S4.4 - page 29",null,"FULLY MET","S4.4 - The learner must be able to apply data classification within data analysis\n\nIndicative content: review requirements of classification; select analytical techniques; apply appropriate analytical techniques; analyse and interpret outcomes; visualise data to support further analysis; determine next steps","S4.4, Assessment 4, Task 1 and Task 2","The content for S4.4 outlines the application of data classification within data analysis. The listed indicative content gives learners the opportunity to demonstrate skill in applying this process of data classification within data analysis."]
["S4","S4 analyse data sets taking account of different data structures and database designs","Theme 3 - S3.1 - page 25",null,"FULLY MET","S3.1 - The learner must be able to analyse datasets within the context of data structure and database design\n\nIndicative content: assess data structure; identify optimal data storage; assess database design; determine database type taking into account system design considerations; review outcomes against analysis requirements","S3.1, Assessment 2, Task 1 and Task 2","S3.1 outlines the process for analysing datasets, contextualised within data structure and database design. Learners are able to demonstrate skill in this area as exemplified by the outline content listed."]
["S5","S5 assess the impact on user experience and domain context on the data analysis activity","Theme 5 - S5.1 - page 31",null,"FULLY MET","S5.1 - The learner must be able to work with a range of internal and external stakeholders\n\nIndicative content: identify the stakeholder type; identify the requirements of stakeholders; clarify the responsibilities of stakeholders; adhere to agreed accessibility requirements; apply agreed communication scope; apply agreed collaboration scope; communicate/collaborate withstakeholders at each stage of the process","S5.1, Assessment 3, Task 1, Task 2 and Task 3","S5.1 supports learners in demonstrating how to work with a range of internal and external stakeholders. In following the process outlined in the indicative content, learners will demonstrate skills in incorporating user experience and domain context in data analysis activity and the impact this can have."]
["S6","S6 identify and escalate quality risks in data analysis with suggested mitigation/resolutions as appropriate.","Theme 3 - S3.3 - page 25",null,"FULLY MET","S3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\n\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation; summarise resolution suggestions; escalate quality risk and resolution suggestions to appropriate contact","S3.3, Assessment 1, Task 1 and Task 2","In demonstrating skills in line with the indicative content for S3.3, learners will be able to identify and escalate quality risks, including identifying and suggesting appropriate mitigations and resolutions. "]
["S7","S7 undertake customer requirements analysis and implement findings in data analytics planning and outputs","Theme 5 - S5.2 - page 31",null,"FULLY MET","S5.2 - The learner must be able to determine and analyse stakeholder requirements to inform data analytics planning and data analysis activities\n\nIndicative content: determine stakeholder requirements; analyse stakeholder requirements to inform data analytics plan; create data analytics plan implementing stakeholder requirements; complete data analysis activity in line with data analysis plan; check completed task against stakeholder requirements and action any changes to original stakeholder requirements","S5.2, Assessment 3, Task 1 and Task 3","S5.2 supports learners in demonstrating skill in determining and analysing stakeholder requirements in order to inform data analytics planning and data analysis activities. The use of 'stakeholder' as opposed to 'customer' (as in S7) is to ensure learners consider requirements of a range of stakeholders, which includes customers. The ue of this terminology is also in line with that used throughout the qualification content. "]
["S8","S8 identify data sources and the risks, challenges to combination within data analysis activity","Theme 3 - S3.2 - page 25",null,"FULLY MET","S3.2 - The learner must be able to identify risks within datasets for combination\n\nIndicative content: identify data sources; validate data sources; assess compatibility of datasets for combination; assess datasets for risks","S3.2, Assessment 1, Task 1, Assessment 2, Task 1, Assesment 3, Task 1","S3.2 gives learners the opportunity to apply skills in risk identification when combining datasets. In following the process outlined in the indicative content, learners can demonstrate skills to exemplify S8."]
["S9","S9 apply organizational architecture requirements to data analysis activities","Theme 4 - S4.3 - page 29",null,"FULLY MET","S4.3 - The learner must be able to apply the requirements of organisational data architecture to data analysis\n\nIndicative content: identify and apply appropriate levels of access and permissions for stakeholders; identify and apply appropriate data security measures; determine data ownership; maintain data integrity; visualise data as appropriate; comply with legislative requirements of data processing","S4.3, Assessment 1, Task 1 and Task 2, Assessment 3, Task 1 and Task 3","The content for S4.3 outlines the process of application of the requirements of organisational data architecture to data analysis activities. Learners will be able to demonstrate the steps detailed in the indicative content, giving a full exemplification of the requirements of S8."]
["S10","S10 apply statistical methodologies to data analysis tasks","Theme 4 - S4.2 - pages 28-29",null,"FULLY MET","S4.2 - The learner must be able to apply statistical methods to meet data analysis requirements\n\nIndicative content: review requirements of task; assess data in preparation for task; apply statistical methods to meet task requirements; review outcomes against task requirements","S4.2, Assessment 4, Task 1 and Task 2","The indicative content listed for S4.2 supports learners in applying statistical methods in order to meet data analysis requirements, as relevant to the data analyst role. Their demonstration of the process detailed in S4.2 supports learners indemonstrating the skill of applying statistical methodologies to data analysis tasks (S10)."]
["S11","S11 apply predictive analytics in the collation and use of data","Theme 4 - S4.5 - page 29",null,"FULLY MET","S4.5 - The learner must be able to apply predictive analytics to data analysis tasks\n\nIndicative content: review requirements of predictive analysis; locate and collate datasets; apply predictive analytical techniques; analyse and interpret outcomes; visualise data to support further analysis; determine next steps","S4.5, Assessment 2, Task 1, Assessment 4, Task 1","S4.5 outlines the process of the application of predictive analytics in the context of data analysis tasks relevant to the data analyst role. The process detailed in the indicative content supports learners in applying predictive analytics in the collation and use of data."]
["S12","S12 collaborate and communicate with a range of internal and external stakeholders using appropriate styles and behaviours to suit the audience","Theme 5 - S5.1 - page 31",null,"FULLY MET","S5.1 - The learner must be able to work with a range of internal and external stakeholders\n\nIndicative content: identify the stakeholder type; identify the requirements of stakeholders; clarify the responsibilities of stakeholders; adhere to agreed accessibility requirements; apply agreed communication scope; apply agreed collaboration scope; communicate/collaborate withstakeholders at each stage of the process","S5.1, Assessment 3, Task 1, Task 2 and Task 3","The indicative content for S5.1 supports learners to demonstrate skill in the process of working with a range of internal and external stakeholders. It was determined that this skill could be demonstrated through simulation within a classroom environment. "]
["S13","S13 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","Theme 4 - S4.1 - page 28",null,"FULLY MET","S4.1 - The learner must be able to use descriptive and predictive models to perform data mining\n\nIndicative content: review task requirements; select and apply appropriate method of data analysis; model to meet requirements; select and apply appropriate data mining tool; identify and predict trends and patterns in data; share visualisations and summary of statistics with stakeholders; review additional stakeholder requirements/feedback","S4.1, Assessment 3, Task 1 and Task 3, Assessment 4, Task 1","The indicative content for S4.1 gives learners the opportunity to follow the detailed process for using descriptive and predictive models to perform data mining. Within the content for S4.1, data mining and time series forecasting are included, ensuring learners fully demonstrate the requirements of S13."]
["S14","S14 collate and interpret qualitative and quantitative data and convert into infographics, reports, tables, dashboards and graphs","Theme 2 - S2.1 - pages 20-21",null,"FULLY MET","S2.1 - The learner must be able to implement the stages of the data lifecycle when carrying out routine data analysis tasks\n\nIndicative content: identify organisational requirements (qualitative and quantitative data); visualise data in appropriate format (infographics; reports; tables; dashboards; graphs)","S2.1, Assessment 1, Task 1 and Task 2, Assessment 2, Task 1, Assessment 3, Task 1, Assessment 4, Task 1","In following the process outlined in the indicative content for S2.1, learners will be able to demonstrate appropriate visualisation of qualitiative and quantitative data following collation and interpretation. S2.1 also supports learners in understanding organisational requirements of visualisation, with them also demonstrating an ability to select the appropriate format."]
["S15","S15 select and apply the most appropriate data tools to achieve the best outcome","Theme 4 - S4.1 - page 28",null,"FULLY MET","S4.1 - The learner must be able to use descriptive and predictive models to perform data mining\n\nIndicative content: select and apply appropriate data mining tool","S4.1, Assessment 3, Task 1 and Task 3, Assessment 4, Task 1","In selecting and applying an appropriate data mining tool, learners are able to demonstrate skills against S15. "]
[null,"Behaviours"]
["B1","B1: maintain productive, professional and secure working environment","Theme 1: K1.1, K1.2, S1.1 - pages 17-18\n\nTheme 2: S2.1 - pages 20-21\n\nTheme 3: K3.5, K3.6, S3.1, S3.3 - pages 23-25\n\nTheme 4: K4.3, S4.1, S4.3 - pages 27-29\n\nTheme 5: K5.2, S5.1, S5.2 - pages 30-31",null,"FULLY MET","Theme 1:\nK1.1 - The learner must understand legislative requirements of data analysis processes\nIndicative content: GDPR/DPA 2018; Copyright, Designs and Patents Act 1988; Digital Economy Act 2017\n\nK1.2  - The learner must understand the purpose of security standards within data analytics\nIndicative content: ISO/IEC 27001 requirements; ISO/IEC 9001; ISO/IEC 20252; ISO/IEC 27701\n\nS1.1 - The learner must be able to operate data systems in compliance with legislation and organisational policies\nIndicative content: operate data systems in compliance with relevant legislation and policies\n\nTheme 2:\nS2.1 - The learner must be able to implement the stages of the data lifecycle when carrying out routine data analysis tasks\nIndicative content: ensure compliance with data governance policies and regulatory requirements; store data in line with organisational policy; archive data in compliance with organisational policy; destroy data in compliance with data governance policies and regulatory requirements\n\nTheme 3:\nK3.5 - The learner must understand quality risks in data and risks of the data combination process\nIndicative content: types of quality risks, risks of the data combination process\n\nK3.6 - The learner must understand forms of risk mitigation and resolution\nIndicative content: validation of original data source; cleansing; model enhancement; risk communication\n\nS3.1 - The learner must be able to analyse datasets within the context of data structure and database design\nIndicative content: assess data structure; identify optimal data storage; assess database design; determine database type taking into account system design considerations; review outcomes against analysis requirements\n\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation; summarise resolution suggestions; escalate quality risk and resolution suggestions to appropriate contact\n\nTheme 4:\nK4.3 - The learner must understand the responsibilities of a data analyst to meet requirements of organisational data architecture\nIndicative content: access and permissions; data security; data ownership; data integrity; data visualisation; legislative compliance in data processing\n\nS4.1 - The learner must be able to use descriptive and predictive models to perform data mining\nIndicative content: review task requirements; select and apply appropriate method of data analysis; model to meet requirements; select and apply appropriate data mining tool; identify and predict trends and patterns in data; share visualisations and summary of statistics with stakeholders; review additional stakeholder requirements/feedback\n\nS4.3 - The learner must be able to apply the requirements of organisational data architecture to data analysis\nIndicative content: identify and apply appropriate levels of access and permissions for stakeholders; identify and apply appropriate data security measures; determine data ownership, maintain data integrity, visualise data as appropriate; comply with legislative requirements of data processing\n\nTheme 5: \nK5.2 - The learner must understand the approach to defining stakeholder requirements for data analytics planning\nIndicative content: analysis of stakeholder requirements; data analytics planning\n\nS5.1 - The learner must be able to work with a range of internal and external stakeholders\nIndicative content: identify the stakeholder type; identify the requirements of stakeholders; clarify the responsibilities of stakeholders; adhere to agreed accessibility requirements; apply agreed communication scope; apply agreed collaboration scope; communicate/collaborate with stakeholders at each stage of the process\n\nS5.2 - The learner must be able to determine and analyse stakeholder requirements to inform data analytics planning and data analysis activities\nIndicative content: determine stakeholder requirements; analyse stakeholder requirements to inform data analytics plan; create data analytics plan implementing stakeholder requirements; complete data analysis activity in line with data analytics plan; check completed task against stakeholder requirements and action any changes to original stakeholder requirements","K1.1, Assessment 1, Task 2\n\nK1.2, Assessment 1, Task 2\n\nS1.1, Assessment 1, Task 1\n\nS2.1, Assessment 1, Task 1 and Task 2, Assessment 2, Task 1, Assessment 3, Task 1, Assessment 4, Task 1\n\nK3.5, Assessment 1, Task 2\n","Theme 1: \nThe indicative content for K1.1 gives learners the opportunity to demonstrate maintenance of a professional and secure working environment by understanding legislation relevant to the data analyst role.\n\nThe security standards listed as part of K1.2 give learners the opportunity to demonstrate maintenance of a professional and secure working environment through understanding the security standards and their purpose.\n\nIn demonstrating skills against the indicative content listed for S1.1, learners will be able to exhibit maintenance of a productive, professional and secure working environment. \n\nTheme 2:\nThe indicative content for S2.1 gives learners the opportunity to maintain productive, professional and secure working environment through the implementation of the data lifecycle. Learners can demonstrate productivity in impmenting each stage of the lifecycle when carrying out routine data analysis tasks; in storing, archiving and destroying data and complying with the relevant data governance policies and regulatory requirements they are able to demonstrate their maintenance of a secure and professional working environment.\n\nTheme 3:\nIn demonstrating knowledge of the indicative content listed for K3.5, learners will have the opportunity to show their understanding of maintenance of a secure working environment.\n\nThe indicative content for K3.6 gives the learners the opportunity to show understanding of risk mitigation and resolution, thus supporting their ability to demonstrate skills in maintaining a secure working environment.\n\nIn demonstrating the skills for S3.1, learners will have the opportunity to exhibit maintenance of a productive and secure working environment. \n\nThe indicative content for S3.3 gives learners the opportunity to demonstrate maintenance of a productive, professional and secure working environment through the escalation of quality risks, including identifying and suggesting appropriate mitigations and resolutions.\n\nTheme 4:\nK4.3's indicative content gives learners the opportunity to demonstrate knowledge of maintaining a professional and secure working environment in the context of the role of a data analyst.\n\nIn demonstrating the skills outlined in the indicative content for S4.1, learners have the opportunity to exemplify the behaviour of maintaining a productive, professional and secure working environment. In sharing their work with stakeholders, learners have the opportunity to demonstrate professionalism; in using models to perform data mining, learners are able to demonstrate a productive working environment. Throughout the indicative content for S4.1, learners will be able to demonstrate maintenance of a secure working environment.\n\nS4.3 gives learners the opportunity to exhibit the behaviour of maintaining a productive, professional and secure working environment through their working with stakeholders and secure handling of data throughout the process listed in the indicative content.\n\nTheme 5:\nAn understanding of the indicative content for K5.2 gives learners the opportunity to demonstrate knowledge of maintenance of a productive, professional and secure working environment through understanding of the professional implementation of stakeholder requirements into data analytics plannning.\n\nIn demonstrating the skills outlined in the indicative content for S5.1, learners will have the opportunity to exhibit maintenance of a professional and productive working environment.\n\nThe indicative content for S5.2 gives learners the opportunity to demonstrate maintenance of a professional working environment when determining and implementing stakeholder requirements, and to demonstrate a productive and secure working environment throughout data analytics planning and analysis activities."]
["B2","B2: shows initiative, being resourceful when faced with a problem and taking responsibility for solving problems within their own remit","Theme 3: K3.5, S3.1, S3.2, S3.3 - pages 23-25\n\nTheme 4: S4.3, S4.4, S4.5 - page 29",null,"FULLY MET","S3.2 - The learner must be able to identify risks within datasets for combination\nIndicative content: assess datasets for risks\n\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation; summarise resolution suggestions; escalate quality risk and resolution suggestions to appropriate contact\n\nTheme 4:\nS4.3 - The learner must be able to apply the requirements of organisational data architecture to data analysis\nIndicative content: identify and apply appropriate levels of access and permissions for stakeholders; identify and apply appropriate data security measures; determine data ownership; maintain data integrity\n\nS4.4 - The learner must be able to apply data classification within data analysis\nIndicative content: select analytical techniques; apply appropriate analytical techniques; determine next steps\n\nS4.5 - The learner must be able to apply predictive analytics to data analysis tasks\nIndicative content: locate and collate datasets; apply predictive analytical techniques; determine next steps","S3.2, Assessment 1, Task 1, Assessment 2, Task 1, Assesment 3, Task 1\n\nS3.3, Assessment 1, Task 1 and Task 2\n\nS4.3, Assessment 1, Task 1 and Task 2, Assessment 3, Task 1 and Task 3\n\nS4.4, Assessment 4, Task 1 and Task 2\n\nS4.5, Assessment 2, Task 1, Assessment 4, Task 1\n","Theme 3:\nThe indicative content for S3.2 gives learners the opportunity to show initiative when assessing datasets for risks.\n\nIn exhibiting the skills outlined in the listed indicative content for S3.3, learners are able to show initiative and resourcefulness in indentification of quality risks and how to mitigate and resolve these these.\n\nTheme 4:\nThe indicative content for S4.3 gives learners the opportunity to demonstrate initiative and resourcefulness when identifying and applying appropriate security measures and levels of access and permissions for stakeholders. They will also be able to demonstrate B2 when determining data ownership and maintaining data integrity.\n\nThe indicative content for S4.4 gives learners the opportunity to demonstrate the behaviours of B2: in selecting and applying appropriate analytical techniques, learners are able to show initiative; further, in determining appropriate next steps when applying data classification within data analysis, learners have the opportunity to take responsibility for solving problems within their own remit as a data analyst.\n\nIn demonstrating the skills listed in the indicative content for S4.5, learners have the opportunity to show initiative, resourcefulness in the face of problems, and that they can take responsibility for solving problems that may arise during the application of predictive analytics to data analysis tasks.\n\n\n"]
["B3","B3: works independently and collaboratively","Theme 2: S2.1 - page 20\n\nTheme 3: K3.6, S3.1, S3.2, S3.3 - pages 24-25\n\nTheme 4: K4.3, S4.1, S4.2, S4.3, S4.4, S4.5 - pages 27, 28-29\n\nTheme 5: K5.2,  S5.1, S5.2 - pages 30-31",null,"FULLY MET","Theme 2:\nS2.1 - The learner must be able to implement the stages of the data lifecycle when carrying out routine data analysis tasks\nIndicative content: identify organisational requirements; ensure compliance with data governance and regulatory requirements; store data in line with organisational policy; collate and interpret appropriate data; prepare data to meet requirements; analyse and model data; visualise data in appropriate format; archive data in compliance with organisational policy; destroy data in compliance with data governance policies and regulatory requirements\n\nTheme 3:\nK3.6 - The learner must understand forms of risk mitigation and resolution\nIndicative content: validation of original data source; cleansing; model enhancement; risk communication\n\nS3.1 - The learner must be able to analyse datasets within the context of data structure and database design\nIndicative content: structured; semi-structured; unstructured\n\nS3.2 - The learner must be able to identify risks within datasets for combination\nIndicative content: identify data sources; validate data sources; assess compatibility of datasets for combination; assess datasets for risks\n\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation; summarise resolution suggestions; escalate quality risk and resolution suggestions to appropriate contact\n\n\n\nTheme 4:\nK4.3 - The learner must understand the responsibilities of a data analyst to meet requirements of organisational data architecture\nIndicative content: access and permissions; data security; data ownership; data integrity; data visualisation; legislative compliance in data processing\n\nS4.1 - The learner must be able to use descriptive and predictive models to perform data mining\nIndicative content: review task requirements; select and apply appropriate method of data analysis; model to meet requirements; select and apply appropriate data mining tool; identify and predict trends and patterns in data; share visualisations and summary of statistics with stakeholders; review additional stakeholder requirements/feedback\n\nS4.2 - The learner must be able to apply statistical methods to meet data analysis requirements\nIndicative content: review requirements of task; assess data in preparation for task; apply statistical methods to meet task requirements; review outcomes against task requirements\n\nS4.3 - The learner must be able to apply the requirements of organisational data architecture to data analysis\nIndicative content: identify and apply appropriate levels of access and permissions for stakeholders; identify and apply appropriate data security measures; determine data ownership; maintain data integrity; visualise data as appropriate; comply with legislative requirements of data processing\n\nS4.4 - The learner must be able to apply data classification within data analysis\nreview requirements of classification; select analytical techniques; apply appropriate analytical techniques; analyse and interpret outcomes; compare analytical techniques; visualise data to support further analysis; determine next steps\n\nS4.5 - The learner must be able to apply predictive analytics to data analysis tasks\nIndicative content: review requirements of predictive analysis; locate and collate datasets; apply predictive analytical techniques; analyse and interpret outcomes; compare analytical techniques; visualise data to support further analysis; determine next steps\n\nTheme 5:\nK5.2 - The learner must understand the approach to defining stakeholder requirements for data analytics planning\nIndicative content: analysis of stakeholder requirements; data analytics planning\n\nS5.1 - The learner must be able to work with a range of internal and external stakeholders\nIndicative content: identify the stakeholder type; identify the requirements of stakeholders; clarify the responsibilities of stakeholders; adhere to agreed accessibility requirements; apply agreed communication scope; apply agreed collaboration scope; communicate/collaborate with stakeholders at each stage of the process\n\nS5.2 - The learner must be able to determine and analyse stakeholder requirements to inform data analytics planning and data analysis activities\nIndicative content: determine stakeholder requirements; analyse stakeholder requirements to inform data analytics plan; create data analytics plan implementing stakeholder requirements; complete data analysis activity in line with data analytics plan; check completed task against; stakeholder requirements and action any changes to original stakeholder requirements","S2.1, Assessment 1, Task 1 and Task 2, Assessment 2, Task 1, Assessment 3, Task 1, Assessment 4, Task 1\n\nK3.6, Assessment 1, Task 2\n\nS3.1, Assessment 2, Task 1 and Task 2\n\nS3.2, Assessment 1, Task 1, Assessment 2, Task 1, Assesment 3, Task 1\n\nS3.3, Assessment 1, Task 1 and Task 2\n","Theme 2:\nIn demonstrating the skills outlined in the indicative content for S2.1, learners will be able to exhibit working independently when implementing the stages of the data lifecycle. There may also be opportunity to demonstrate working collaboratively at points of the lifecycle.\n\nTheme 3:\nK3.6 gives learners the opportunity to demonstrate understanding of independent working when validating data sources, cleansing data and enhancing models. This content also gives learners the opportunity to demonstrate knowledge of collaborative working when communicating risk.\n\nIn S3.1, learners have the opportunity to exhibit independent working when analysing datasets (structured, semi-structured and unstructured data).\n\nS3.2 presents learners with the opportunity to work independently when identifying risks within datasets ahead of combination.\n\nIn S3.3, learners are able to exhibit independent and collaborative working: the former when identifying and assessing quality risks and their impact and mitigation, and the latter when summarising and escalating resolution suggestions to appropriate contacts.\n\nTheme 4:\nK4.3 presents learners with the opportunity to understand working independently to ensure they meet the requirements of organisational data architecture. K4.3 also gives learners the opportunity to demonstrate understanding of working across teams when ensuring compliance in data processing.\n\nIn S4.1, learners are able to work independently when performing data mining using descriptive and prescriptive models. Additionally, they will have the opportunity to demontrate collaborative working when sharing visualisations and statistical summaries with stakeholders and reviewing their requirements and feedback.\n\nIn S4.2, learners have the opportunity to work independently when applying statistical methods to meet data analysis requirements.\n\nS4.3 gives learners an opportunity to exhibit independent working when applying the requirements of organisational data architecture to data analysis. Within this, they are able to demonstrate collaborative working when working across teams to ensure compliance.\n\nIn demonstrating the skills outlined in the indicative content for S4.4, learners have the opportunity to work independently to apply data classification within data analysis.\n\nS4.5 gives learners the opportunity to work independently when applying predictive analytics to data analysis tasks.\n\nTheme 5:\nAn understanding of the indicative content for K5.2 gives learners the opportunity to demonstrate understanding of indepdent (data analytics planning) and collaborative (analysis of stakeholder requirements) working.\n\nIn demonstrating the skills outlined in the indicative content for S5.1, learners have the opportunity to exhibit collaborative working.\n\nS5.2 gives learners the opportunity to demonstrate independent and collaborative working. In determining stakeholder requirements and actioning any changes against these, learners can demonstrate collaborative working. Other content within S5.2 gives learners the opportunity to demonstrate independent working."]
["B4","B4: logical and analytical","Theme 1: S1.1 - page 17\n\nTheme 2: K2.1, K2.3, S2.1 - pages 18-21\n\nTheme 3: K3.1, K3.2, K3.3, K3.5, K3.6, S3.1, S3.2, S3.3 - pages 22-25\n\nTheme 4: K4.1, K4.2, K4.4, S4.1, S4.2, S4.3, S4.4, S4.5 - pages 26-29\n\nTheme 5: S5.1, S5.2 - page 31",null,"FULLY MET","Theme 1:\nS1.1 - The learner must be able to operate data systems in compliance with legislation and organisational policies\nIndicative content: review operational requirements against relevant legislation and policies; operate data systems in compliance with relevant legislation and policies\n\nTheme 2:\nK2.3 - The learner must understand the data lifecycle and the steps involved when carrying out routine data analysis tasks\nIndicative content: usage  (data preparation; data sharing; data processing; data analysis; data modelling)\n\nS2.1 - The learner must be able to implement the stages of the data lifecycle when carrying out routine data analysis tasks\nIndicative content: collate and interpret appropriate data; analyse and model data\n\nTheme 3:\nK3.3 - The learner must understand the types of database and the factors of database system design, implementation, and maintenance\nindicative content: database system design; database system implementation; database system maintenance\n\nK3.5 - The learner must understand quality risks in data and risks of the data combination process\nIndicative content: types of quality risks; risks of the data combination process\n\nK3.6 - The learner must understand forms of risk mitigation and resolution\nIndicative content: validation of original data source; cleansing; model enhancement\n\nS3.1 - The learner must be able to analyse datasets within the context of data structure and database design\nIndicative content: assess data structure; identify optimal data storage; assess database design; determine database type taking into account system design considerations; review outcomes against analysis requirements\n\nS3.2 - The learner must be able to identify risks within datasets for combination\nIndicative content: validate data sources; assess compatibility of datasets for combination; assess datasets for risks\n\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation\n\nTheme 4: \nK4.1 - The learner must understand methods of data analysis and tools used within data analytics\nIndicative content: methods of data analysis; tools for methods of data analysis\n\nK4.2 - The learner must understand the application of statistical types and methods\nIndicative content: statistical types; statistical methods\n\nK4.4 - The learner must understand the principles of descriptive, predictive and prescriptive analytics and analytical approaches with models \nIndicative content: analytical approaches with models \n\nS4.1 - The learner must be able to use descriptive and predictive models to perform data mining\nIndicative content: select and apply appropriate method of data analysis; select and apply appropriate data mining tool; identify and predict trends and patterns in data\n\nS4.2 - The learner must be able to apply statistical methods to meet data analysis requirements\nIndicative content: assess data in preparation for task; apply statistical methods to meet task requirements\n\nS4.4 - The learner must be able to apply data classification within data analysis\nIndicative content: select analytical techniques; apply appropriate analytical techniques; analyse and interpret outcomes\n\nS4.5 - The learner must be able to apply predictive analytics to data analysis tasks\nIndicative content: apply predictive analytical techniques; analyse and interpret outcomes\n\n\nTheme 5:\nS5.2 - The learner must be able to determine and analyse stakeholder requirements to inform data analytics planning and data analysis activities\nIndicative content: analysis of stakeholder requirements; data analytics planning","S1.1, Assessment 1, Task 1\n\nK2.3, Assessment 2, Task 2\n\nS2.1, Assessment 1, Task 1 and Task 2, Assessment 2, Task 1, Assessment 3, Task 1, Assessment 4, Task 1\n\nK3.3, Assessment 2, Task 2\n\nK3.5, Assessment 1, Task 2\n\nK3.6, Assessment 1, Task 2","Theme 1:\nS1.1 gives learners the opportunity to demonstrate logical and analytical skills when reviewing operational requirements against relevant legislation and policies. They will be able to logically and analytically operate data systems in compliance with relevant legislation and policies.\n\nTheme 2:\nIn demonstrating their understanding of the listed indicative content for K2.3, learners are able to exhibit logical and analytical behaviours when carrying out routine data analysis tasks\n\nLearners will have the opportunity to demonstrate both logical and analytical behaviour when collating and interpreting appropriate data whilst implementing the stages of the data life cycle as part of S2.1. They will also be able to exhibit logical and analytical behaviour at the analysis and modelling data stage of the life cycle.\n\nTheme 3:\nIn demonstrating their knowledge of the design, implementation and maintenance of a database system in K3.3, learners have the opportunity to exhibit logical and analytical behaviour\n\nLearners are able to demonstrate logical and analytical behaviour when understanding the types of quality risks in data as part of K3.5 and, additionally, when exhibiting knowledge of the risks of the data combination process\n\nIn demonstrating their understanding of data source validation, data cleansing and model enhancement as part of  K3.6, learners have the opportunity to exhibit logical and analytical behaviours\n\nWhen analysing datasets as part of S3.1, learners have the opportunity to demonstrate logical and analytical behaviour. \n\nIn their identification of risks within datasets for combination as part of S3.2, learners have the opportunity to demonstrate logical and analytical behaviours. \n\nAs part of the listed indicative content for S3.3 learners have the opportunity to demonstrate logical and analytical behaviours.\n\nTheme 4: \nDemonstration of the knowledge of methods of data analysis and the tools for methods of data analysis gives learners opportunity to exhibit logical and analytical behaviours\n\nIn K4.2, learners can exhibit logical and analytical behaviours when demonstrating knowledge of statistical types and methods\n\nListed indicative content for K4.4 gives learners the opportunity to demonstrate logical and analytical behaviours when developing their understanding of analytical approaches with models.\n\nIn their use of descriptive and predictive models to perform data mining as part of the indicative content for S4.1, learners have the opportunity to demonstrate logical and analytical behaviours.\n\nIn assessing data in preparation for data analysis task, and applying statistical methods in order to meet data analysis requirements as part of S4.2, learners have the opportunity to develop logical and analytical behaviours.\n\nLearners are able to demonstrate logical and analytical behaviours when selecting and applying appropriate analytical techniques, and analysing and interpreting outcomes, as part of S4.4.\n\nApplying predictive analytical techniques and analysing and interpreting outcomes gives learners the opportunity to demonstrate logical and analytical behaviours.\n\n\nTheme 5:\nWhen analysing stakeholder requirements in order to inform data analytics planning as part of S5.2, learners will be able to demonstrate logical and analytical behaviours."]
["B5","B5: identifies issues quickly, investigates and solves complex problems and applies appropriate solutions. Pushes to ensure the true root cause of any problem is found and a solution is identified which prevents recurrence.","Theme 3: K3.5, S3.1, S3.2, S3.3 - pages 23-25\n\nTheme 4: S4.2, S4.4, S4.5 - pages 28-29",null,"PARTIALLY MET","Theme 3:\nK3.5 - The learner must understand quality risks in data and risks of the data combination process\nIndicative content: types of quality risks; risks of the data combination process\n\nS3.1 - The learner must be able to analyse datasets within the context of data structure and database design\nIndicative content: assess data structure; assess database design; determine database type taking into account system design considerations; review outcomes against analysis requirements\n\nS3.2 - The learner must be able to identify risks within datasets for combination\nIndicative content: assess datasets for risks\n\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation\n\n\nTheme 4:\nS4.4 - The learner must be able to apply data classification within data analysis\nIndicative content: select analytical techniques; apply appropriate analytical techniques; analyse and interpret outcomes\n\nS4.5 - The learner must be able to apply predictive analytics to data analysis tasks\nIndicative content: locate and collate datasets; apply predictive analytical techniques; analyse and interpret outcomes; determine next steps","K3.5, Assessment 1, Task 2\n\nS3.1, Assessment 2, Task 1 and Task 2\n\nS3.2, Assessment 1, Task 1, Assessment 2, Task 1, Assesment 3, Task 1\n\nS3.3, Assessment 1, Task 1 and Task 2\n\nS4.4, Assessment 4, Task 1 and Task 2\n\nS4.5, Assessment 2, Task 1, Assessment 4, Task 1","PARTIALLY MET RATIONALE: Behaviour B5 has been changed from 'Identifies issues quickly, enjoys investigating and solving complex problems and applies appropriate solutions. Has a strong desire to push to ensure true root cause of any problem is found and a solution is identified which prevents recurrence' to 'Identifies issues quickly, investigates and solves complex problems and applies appropriate solutions. Pushes to ensure the true root cause of any problem is found and a solution is identified which prevents recurrence'. The reason for this change, and the rationale for partial coverage of the original B5 statement, is that it is not possible to quantify and objectively assess a learner's \"enjoyment\" of investigating and problem solving or their \"strong desire\" to push to find the true root cause of a problem. In order to ensure no learner is unfairly advantaged or disadvantaged by the potentially subjective assessment of behaviours, qualification content has been aligned to the reworded B5 statement above.\n\nTheme 3:\nIn demonstrating knowledge of the indicative content listed for K3.5, learners have the opportunity to exhibit risk/issue identification and, additionally, risk identification specific to the data combination process.\n\nS3.1 gives learners the opportunity to demonstrate problem solving when analysing datasets within the context of data structure and database design.\n\nIn S3.2, learners can exhibit risk identification when assessing datasets for risks.\n\nIn demonstrating the skills in the indicative content for S3.3, learners are able to investigate and solve complex problems and apply appropriate solutions.\n\nTheme 4:\nThe indicative content for S4.4 gives learners the opportunity to demonstrate problem investigation and solving. Further, they can apply appropriate solutions when selecting and applying appropriate analytical techniques. \n\nThe indicative content for S4.5 supports learners in demonstrating the identification of potential issues when applying predictive analytics to data analysis tasks. They also have the opportunity to demonstrate ensuring an appropriate resolution or outcome is identified, including next steps."]
["B6","B6: demonstrates resilience by viewing obstacles as challenges and learning from failure.","Theme 3: K3.5, S3.1, S3.2, S3.3 - pages 23-25\n\nTheme 4: S4.4, S4.5 - page 29",null,"FULLY MET","Theme 3:\nK3.5 - The learner must understand quality risks in data and risks of the data combination process\nIndicative content: types of quality risks; risks of the data combination process\n\nS3.2 - The learner must be able to identify risks within datasets for combination\nIndicative content: assess datasets for risks\n\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: identify possible quality risks within data activity; assess impact on data analysis; determine possible risk mitigation; summarise resolution suggestions; escalate quality risk and resolution suggestions to appropriate contact\n\nTheme 4:\nS4.4 - The learner must be able to apply data classification within data analysis\nIndicative content: apply appropriate analytical techniques; analyse and interpret outcomes; visualise data to support further analysis; determine next steps\n\nS4.5 - The learner must be able to apply predictive analytics to data analysis tasks\nIndicative content: apply predictive analytical techniques; analyse and interpret outcomes; visualise data to support further analysis; determine next steps","K3.5, Assessment 1, Task 2\n\nS3.2, Assessment 1, Task 1, Assessment 2, Task 1, Assesment 3, Task 1\n\nS3.3, Assessment 1, Task 1 and Task 2\n\nS4.4, Assessment 4, Task 1 and Task 2\n\nS4.5, Assessment 2, Task 1, Assessment 4, Task 1","Theme 3:\nThe listed indicative content for K3.5 gives learners the opportunity to demonstrate resilience when exhibiting their understanding of quality risks, the risk of the data combination process, with the opportunity for potential risks to be viewed as challenges.\n\nS3.2 gives learners the opportunity to demonstrate resilience when assessing datasets for risks, including learning from (potential) failure throughout the process.\n\nIn demonstrating skills against the listed indicative content for S3.3, learners are able to exhibit resilience when escalating quality risks and suggestiong resolutions.\n\nTheme 4:\nListed indicative content for S4.4 gives learners the opportunity to demonstrate resilience when applying analytical techniques, analysing and interpreting outcomes, and potentially repeating this process as part of determined next steps in order to apply data classification within data analysis. \n\nS4.5 gives learners the opportunity to demonstrate resilience when applying predictive analytical techniques, analysing and interpreting outcomes, and potentially repeating this process as part of determined next steps in order to apply predictive analytics to data analysis tasks."]
["B7","B7: demonstrates an ability to adapt to changing contexts within the scope of a project or Data Analyst role.","Theme 3: S3.3 - page 25\n\nTheme 5: K5.2, S5.1, S5.2 - pages 30-31",null,"PARTIALLY MET","Theme 3:\nS3.3 - The learner must be able to escalate quality risks with appropriate mitigations and resolutions\nIndicative content: determine possible risk mitigation; escalate quality risk and resolution suggestions to appropriate contact\n\nTheme 5:\nK5.2 - The learner must understand the approach to defining stakeholder requirements for data analytics planning\nIndicative content: analysis of stakeholder requirements – scope (problem statements); parameters (constraints; resource availability); data analytics planning – identification of (stakeholder requirements; variables)\n\nS5.1 - The learner must be able to work with a range of internal and external stakeholders\nIndicative content: identify the requirements of stakeholders; clarify the responsibilities of stakeholders; apply agreed communication scope; apple agreed collaboration scope; communicate/collaborate with stakeholders at each stage of the process\n\nS5.2 - The learner must be able to determine and analyse stakeholder requirements to inform data analytics planning and data analysis activities\nIndicative content: check completed task against stakeholder requirements and action any changes to original stakeholder requirements","S3.3, Assessment 1, Task 1 and Task 2\n\nK5.2, Assessment 3, Task 2 and Task 3\n\nS5.1, Assessment 3, Task 1, Task 2 and Task 3\n\nS5.2, Assessment 3, Task 1 and Task 3","PARTIALLY MET RATIONALE: Behaviour B7 has been changed from ‘Demonstrates an ability to adapt to changing contexts within the scope of a project, direction of the organisation or Data Analyst role’ to ‘Demonstrates an ability to adapt to changing contexts within the scope of a project or Data Analyst role’. The reason for this change, and the rationale for partial coverage of the original B7 statement, is that it is not possible to appropriately to expect a learner to adapt to changing context in line with the ‘direction of the organisation’ when undertaking this HTQ, which is an entirely classroom-based qualification. In order to ensure no learners are unfairly advantaged or disadvantaged by their centre’s access to/links with existing organisations (and thus, their ability to explore/simulate the direction of said organisation), this phrase has been removed from behaviour statement B7.\n\nTheme 3:\nIn demonstrating the skills outlined in the listed indicative content for S3.3, learners can exhibit an ability to adapt to changing contexts when faced with quality risks, through escalating risks and suggesting solutions as appropriate.\n\nTheme 5:\nThe knowledge outlined in the listed indicative content for K5.2 gives learners the opportunity to demonstrate an understanding of adapting to changing contexts within the scope of a project, including working with problem statements, within specific constraints and, additionally, through the ongoing identification of stakeholder requirements and variables.\n\nS5.1 gives learners the opportunity to exhibit an ability to adapt to changing contexts within the scope of a project when working with a range of internal and external stakeholders. \n\nIn demonstrating skills against the listed indicative content for S5.2, learners have the opportunity to exhibit their ability to adapt to changing contexts by checking their completed task(s) against stakeholder requirements, and adapting their work based on any changes to original stakeholder requirements.\n\n"]

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[]
[]
[]
["Additional content","YES","Standard 1","FULLY MET","N/A","PARTIAL"]
["ST0116","NO","Standard 2","PARTIALLY MET","MITIGATED","FULL"]
["ST0117",null,"Standard 3","NOT MET ","PARTIALLY MITIGATED "]
["ST0118",null,"Standard 4"]
["ST1021",null,"Standard 5"]
["ST0127",null,"Standard 6"]
["ST0129",null,"NO COVERAGE "]
["ST0345"]
["ST0825"]
["ST0863",null,"Provider setting (e.g. classroom)"]
[null,null,"Online"]
[null,null,"Distance"]
[null,null,"Placement"]
[null,null,"Mixed"]
[]
[]
[]
[]
[]
[]
[]
[]
["Year 1"]
["Year 2"]
["Year 3"]
["Year 4"]
["Across years"]
["Core "]
["Option"]
[]
["Yes"]
["No"]
[]
[4]
[5]
[]
[]
[null,null,null,null,null,null,null,null,"Business analyst (level 4) (ST0117)"]
[null,null,null,null,null,null,null,null,"Cyber-security technologist (level 4) (ST0124)"]
[null,null,null,null,null,null,null,null,"Data analyst (level 4) (ST0118)"]
[null,null,null,null,null,null,null,null,"DevOps engineer (level 4) (ST0825)"]
[null,null,null,null,null,null,null,null,"Digital accessibility specialist (level 4) (ST0863)"]
[null,null,null,null,null,null,null,null,"Digital community manager (level 4) (ST0345)"]
[null,null,null,null,null,null,null,null,"Network engineer (level 4) (ST0127)"]
[null,null,null,null,null,null,null,null,"Software developer (level 4) (ST0116)"]
[null,null,null,null,null,null,null,null,"Software tester (level 4) (ST0129)"]