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["Quick reference guide to KSB mapping",null,"Table to assist with B11-13",null,null,"Ed2"]
[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"]
["University of Brighton",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"]
["Certificate of Higher Education",null,"ST0118","Data analyst (level 4) (ST0118)"]
["Programme title if applicable:",null,"ST0825","DevOps engineer (level 4) (ST0825)"]
["Data Analyst",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 ",null,null,null,null,null,null,null,null,null,null,"Yes"]
[]
["Note: If a module is not aligned/mapped to an occupational standard it is classed as additional content"]
[]
["Module Code","Module Name","Full-Time Year","Part-Time Year","Core/Option","Mapped to Standard(s)","Credits","Level","Delivery Method","Delivery comments"]
["EXAMPLE001","Foundations of Technical Concepts","Year 1","Year 1","Core ","ST0116, ST0117",30,4,"Mixed","40% classroom, 60% placement. "]
["CI412","Databases","Year 1",null,"Core ","ST0118",20,4,"Provider setting (e.g. classroom)"]
["MM405","Introduction to Operational Research","Year 1",null,"Core ","ST0118",20,4,"Provider setting (e.g. classroom)"]
["MM410","Introduction to Statistics","Year 1",null,"Core ","ST0118",20,4,"Provider setting (e.g. classroom)"]
["MM424","Introduction to Data Analysis with SAS","Year 1",null,"Core ","ST0118",20,4,"Provider setting (e.g. classroom)"]
["MM425","Fundamentals of Programming","Year 1",null,"Core ","ST0118",20,4,"Provider setting (e.g. classroom)"]
["OS101","Managing Systems and Operations","Year 1",null,"Core ","ST0118",20,4,"Provider setting (e.g. classroom)"]

{"sheet":"ST0118","index":2,"n_rows":53,"n_cols":17,"rows_kept":53,"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","AB Score","IM Score","Mitigations ","CoE Score","Requirement","IM Comment","G7 QA","IM G7 Response","Conclusion"]
[null,"Knowledge"]
["K1","K1: current relevant legislation and its application to the safe use of data","Programme LOs:                                         LO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security.                                                              CI412: Module Specification             OS101: Module Specification              MM424: Module SPecification                                      ",null,"FULLY MET","CI412: LO4- Show awareness of issues affecting collection and storage of different types of  data.                                \nIndicative Content:    Characteristics of a system, Relevance of information, Data integrity and security                                                                                                             OS101:   Introduction to the role of the UK Information Commissioner and the Computer Misuse Act. Introduction to GDPR.  Introduction to \"The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018\". \nIndicative Content:        Systems for small business models, Procurement of systems: Cloud / outsourcing, Coordinating complex organisations: ERP and using SAP, Future watching, Quality Management, Supply Chain Management.                                        \nMM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task.                                                                                                                                             Module aim: To provide students with valuable, hands-on experience with the SAS software and concepts that they will encounter in a data analyst job.                                  ","CI412: Creation of a relational database, including data analysis, design and the implementation of a relational database.   \nOS101: Open book online test                                         MM424: Report- Students will have the opportunity to apply their knowledge of the safe use of data by analysing real world data.","In MM424 this concept will be explored throughout the module rather than in any one specific session.",{"$f":"IF(E5:E19 = \"FULLY MET\",1,IF(E5:E19 = \"PARTIALLY MET\",0.5,IF(E5:E19 = \"NOT MET\",0,)))","v":1},1,null,null,null,"no comment","Agree"]
["K2","K2: organisational data and information security standards, policies and procedures relevant to data management activities","Programme LOs:                                      LO1     Students will be able to demonstrate knowledge and understanding of the fundamentals of data structures, database system design, implementation and maintenance.  \nLO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security                                                    CI412 Module Content:  Information systems: characteristics of a system, relevance of information, data integrity and security.                                         OS101 Module Content:  Process Design, Quality Management                                              MM424 Module Content:                                                   ",null,"FULLY MET","CI412: LO4- Show awareness of issues affecting collection and storage of different types of  data.                                \nIndicative Content: characteristics of a system, relevance of information, data integrity and security          \nOS101:   Introduction to the role of the UK Information Commissioner and the Computer Misuse Act. Introduction to GDPR.     \nIndicative Content:        Ubquitous technology - mobile first, Business intelligence, big data and measuring, Systems for small business models, Coordinating complex organisations (ERP and using SAP), Procurement of systems (Cloud / outsourcing), Process Design, Process and layout types, Quality Management, Supply Chain Management.                                  \nMM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. ","CI412:  Creation of a relational database, including data analysis, design and the implementation of a relational database.  \nOS101: Open book online test                                   MM424: Report- Students will have the opportunity to apply their knowledge of data management activities by analysing real world data.","As above, in MM424, these principles will be discussed throughout the module rather than in one specific session.",1]
["K3","K3: principles of the data life cycle and the steps involved in carrying out routine data analysis tasks.","Programme LOs:                                      LO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis.  \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. \nLO12    \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","CI412: LO1- Analyse and model the requirements of information systems for a stated case study. \nIndicative Content:   Characteristics of a system, Relevance of information, Data integrity and security     \nMM410: LO2 - explore and summarise real-world data sets;    LO4 - Demonstrate an understanding of the role of the sampling distribution, estimate model parameters and conduct hypothesis tests.                            \nIndicative Content:  Data sources and their exploration and visualisation, estimation of parameters, significance testing.                                  \nMM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task.                                                                                                                                          Indicative Content: Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; \nFamiliarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology.","CI412: Creation of a relational database, including data analysis, design and the implementation of a relational database.                                                    MM410: Coursework- Students will analyse an individual data set, carrying out exploratory data analysis and then choosing further analysis based on their intitial findings. Exam- Students will be required to select suitable methods to analyse given statistical problems.                                MM424: Report- The assessment will consist of analysing a multivariate dataset. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report                                          ",null,1]
["K4","K4: principles of data, including open and public data, administrative data, and research data","Programme LOs:                                       LO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security.",null,"FULLY MET","OS101: Introduction to GDPR and definition of personal data. \nIndicative Content:         Business intelligence, big data and measuring, Digital Marketing, Procurement of systems (Cloud / outsourcing), Role of IT in Operations Management, Process Design, Process and layout types, Operations improvement approaches and techniques, Quality Management.\nMM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. \n","OS101: Open book online test                                       MM424: Report- Students will demonstrate understanding of this through the analysis and interpretation of their data sets.","MM424- This concept will be discussed within the context of relevant lecture examples.",1]
["K5","K5: the differences between structured and unstructured data","Programme LOs:                                       LO1     Students will be able to demonstrate knowledge and understanding of the fundamentals of data structures, database system design, implementation and maintenance.",null,"FULLY MET","CI412: LO2- Design and implement a simple business database.                           Indicative Content:    Characteristics of a system, Relevance of information, Data integrity and security                          \nMM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. \n","CI412: Creation of a relational database, including data analysis, design and the implementation of a relational database.           \nMM424: Report","MM424- This concept will be discussed within the context of relevant lecture examples.",1,0,null,null,null,"Can't see any specific mention structured and unstructured data - peer review and route panel picked up on this for cycle 1 and said it was importatnt. Also cannot find module CI412 in the qualification docs… have taken CI40x module as this one","We said we wouldn't consider evidence from the assessment method column, however "]
["K6","K6: the fundamentals of data structures, database system design, implementation and maintenance","Programme LOs:                                        LO1     Students will be able to demonstrate knowledge and understanding of the fundamentals of data structures, database system design, implementation and maintenance.  \nLO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security. \nLO10      Students will have the ability to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","CI412: LO1- Analyse and model the requirements of information systems for a stated case study; LO2-Design and implement a simple business database;                                  \nLO3- Use SQL to query databases, select and sort rows, join tables  and aggregate data.                                                                                                                                           Indicated Content: Characteristics of a system, Relevance of information, Data integrity and security   ","CI412: Creation of a relational database, including data analysis, design and the implementation of a relational database,  and an Exam                        ",null,1]
["K7","K7: principles of user experience and domain context for data analytics","Programme LOs:                                     LO12    \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","OS101: Data and Business Analytics lectures include lecture on Human Perception and Rules for Data Visualisation.                        \nIndicative Content:     Ubiquitous technology - mobile first, Business intelligence, big data and measuirng, Systems for small business models, Coordinating complex organisations (ERP and using SAP), Role of IT in operations management, Transformation process, IOGEs and 4Vs, Process Design, Process and layout types, Operations improvement approaches and techniques, Quality management.","OS101: Open book online test ",null,1]
["K8","K8: quality risks inherent in data and how to mitigate/resolve these",null,null,"FULLY MET","CI412: LO4- Show awareness of issues affecting collection and storage of different types of  data.                                  \nIndicative Content:     Characteristics of a system, Relevance of information, Data integrity and security   ","CI412: Creation of a relational database, including data analysis, design and the implementation of a relational database.  ",null,1]
["K9","K9: principal approaches to defining customer requirements for data analysis","Programme LOs:                                     LO12\t     Develop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","MM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. \nIndicative Content: Analytics lifecycle describing the steps based on the CRISP-DM methodology.","MM424: Report-Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report.",null,1,1,null,null,null,"CRISP-DM is a data mining process - Business Understanding - \nData Understanding - \nData Preparation - \nModeling - \nEvaluation - \nDeployment.  ","Agree"]
["K10","K10: approaches to combining data from different sources","Programme LOs:                                       LO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information.",null,"FULLY MET","MM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task.\nLO2 - Develop and execute a program with SAS code for working with data in a project, understanding the basics of data manipulation and producing summary statistics; \nIndicative Content: Import data, combine and merge multiple SAS datasets to facilitate combined analysis, run SQL queries to manipulate data, compute new variables and format data.","MM424: Report & Coursework-The assessment will consist of writing a SAS program to import given data, combining multiple datasets, transforming the data, exploring and visualising the data, and producing summary statistics.",null,1]
["K11","K11: approaches to organisational tools and methods for data analysis","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.      \nLO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information.",null,"FULLY MET","MM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task.   \nIndicative Content: Introduction to SAS Studio, types of data, and data structures in SAS; Program structures in SAS: if-then/else statements, data steps and procedures; Import data, combine and merge multiple SAS datasets to facilitate combined analysis, run SQL queries to manipulate data, compute new variables and format data; Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; Familiarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology                         \nMM425: LO1-Demonstrate an effective use of Excel in the context of various aspects of data analysis. LO4- Demonstrate an effective use of the Python programming language in the context of manipulating and displaying data.      \nIndicative Content:   Spreadsheets- Using spreadsheets as a tool for organising and displaying data; Data Analysis and Manipulation with Python- Using Python to import, analyse and display data in a variety of ways.                       \nOS101: LO5 - Demonstrate confidence in handling and analysing numerical data, and to demonstrate the relevance and use of quantitative techniques in business.                                                                                                                                          Indicative Content: Ubiquitous technology - mobile first, Business intelligence, big data and measuring, Systems for small business models, Coordinating complex organisations (ERP and using SAP), Role of IT in operations Management, Transformation processm IOGEs and 4Vs, Process and layout types, Operations improvement and approaches and techniques.","OS101: Open book online test                                  MM424: Report- The assessment will consist of analysing a multivariate dataset. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report                                       MM425: Portfolio- Students will undertake a range of portfolio assessments designed to test their ability to choose suitable tools and methods for analysing data and solving problems.",null,1,1,"PARTIALLY MITIGATED ",null,null,"good evidence for 'partially met'","Thanks Dan, agree this goes beyond partial mitigation"]
["K12","K12: organisational data architecture","Partially covered by modules CI412 and OS101.      ",null,"PARTIALLY MET","Partially covered by modules CI412 and OS101.                              \nIndicative Content:  CI412 includes Characteristics of a system, Relevance of information, Data integrity and security. OS101 includes Systems for small business models, Coordinating complex organisations (ERP and using SAP), Process Design, Project Management, Operations improvement approaches & Techniques,","OS101: Open book online test","Aspects of this will be covered by CI412 and OS101, however we don't feel this concept will be fully explored by either module.",0.5,null,"PARTIALLY MITIGATED "]
["K13","K13: principles of statistics for analysing datasets","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.       \nLO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving. \nLO4     Students will be able to demonstrate knowledge and understanding of the importance of assumptions, awareness of their use and consequences of their violation. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis.  \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information. \nLO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM410: LO1 - Demonstrate an understanding of the need for probabilistic and statistical methods to analyse real-world problems and use appropriate software; LO2 - explore and summarise real-world data sets;  LO3 - apply some standard probability and expectation notions and models in a variety of contexts;   LO4 - Demonstrate an understanding of the role of the sampling distribution, estimate model parameters and conduct hypothesis tests.   \nIndicative Content:      Data sources and their exploration and visualisation,  estimation of parameters, significance testing.        \nOS101: LO5 - Demonstrate confidence in handling and analysing numerical data, and to demonstrate the relevance and use of quantitative techniques in business.                                                                                                                                     Indicative Content:      Big data and measuring, Role of IT in operations management.                                     \nMM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. \nIndicative Content:  Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; Familiarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology","MM410: Exam & A case study involving data visualisation / analysis                            OS101:   Open-Book online test                                      MM424: Report- The assessment will consist of analysing a multivariate dataset. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report.",null,1]
["K14","K14: the principles of descriptive, predictive and prescriptive analytics","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting.",null,"FULLY MET","MM410:  LO1 - Demonstrate an understanding of the need for probabilistic and statistical methods to analyse real-world problems and use appropriate software; explore and summarise real-world data sets; LO2 - explore and summarise real-world data sets;  LO3 - apply some standard probability and expectation notions and models in a variety of contexts;  LO4 - Demonstrate an understanding of the role of the sampling distribution, estimate model parameters and conduct hypothesis tests. \nIndicative Content:   Data sources and their exploration and visualisation; estimation of parameters; significance testing.                                       \nMM424:  LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. \nIndicative Content: Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; Familiarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology","MM410: Coursework & Exam- Students will carry out exploratory data analysis, reporting on the descriptive statistics obtained as well as utilising regression analysis to make predictions about given data.                      \nMM424: Report- the task will enable students to explore and apply prescriptive analytic techniques.",null,1]
["K15","K15: the ethical aspects associated with the use of and collation of data","Programme LOs:                                        LO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security",null,"FULLY MET","OS101: Session on Data Ethics. Introduction to Legal issues related to data, data piracy, data protection.                            \nIndicative Content:     Role of IT in operations Management, Coordinating complex organisations, Process and layout types, Process design, Quality Management, Operations improvement approaches and techniques","OS101: Open book online test",null,1,1,null,null,null,"no comment","Agree"]
[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","Programme LOs:                                        LO1     Students will be able to demonstrate knowledge and understanding of the fundamentals of data structures, database system design, implementation and maintenance.   \nLO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security",null,"FULLY MET","OS101:   Introduction to the role of the UK Information Commissioner and the Computer Misuse Act. Introduction to GDPR.  Introduction to \"The Public Sector Bodies (Websites and Mobile Applications) Accessibility Regulations 2018\".   Indicative Content: Ubiquitous technology - mobile first, Procurement of systems: cloud / outsourcing, Future watching, Process and layout types, Process design, Operations improvement approaches & Techniques, Quality Management, Supply chain management.","OS101: Open book online test",null,{"$f":"IF(E21:E35 = \"FULLY MET\",1,IF(E21:E35 = \"PARTIALLY MET\",0.5,IF(E21:E35 = \"NOT MET\",0,)))","v":1},null,"PARTIALLY MITIGATED "]
["S2","S2 implement the stages of the data analysis lifecycle","Programme LOs:                                        LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO10      Students will have the ability to Research and learn independently and apply this knowledge in a data analysis setting.\nLO12    \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","MM424: LO1 - Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task. LO2: Develop and execute a program with SAS code for working with data in a project, understanding the basics of data manipulation and producing summary statistics. \nIndicative Content: Analytics lifecycle describing the steps based on the CRISP-DM methodology.","MM424: Report & Coursework.                           The report will consist of writing a SAS program to import given data, combining multiple datasets, transforming the data, exploring and visualising the data, and producing summary statistics.                                 The Coursework will consist of analysing a multivariate dataset based on a case study.",null,1,1,null,null,null,"CRISP - DM model as in K9 - process and stages for data mining / analysis","Agree"]
["S3","S3 apply principles of data classification within data analysis activity","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. LO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO12      Develop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","MM424:  LO2: Develop and execute a program with SAS code for working with data in a project, understanding the basics of data manipulation and producing summary statistics. \nIndicative Content: Import data, combine and merge multiple SAS datasets to facilitate combined analysis, run SQL queries to manipulate data, compute new variables and format data; Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; \nFamiliarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology.","MM424: Coursework",null,1]
["S4","S4 analyse data sets taking account of different data structures and database designs","Programme LOs:                                       LO1     Students will be able to demonstrate knowledge and understanding of the fundamentals of data structures, database system design, implementation and maintenance.    \n LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.      \nLO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. LO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions.",null,"FULLY MET","MM424:  LO2: Develop and execute a program with SAS code for accessing, understand the basics of data manipulation, producing summary statistics.  LO3: Visualize and explore the data using charts, plots, diagrams and tables. \nIndicative Content: Import data, combine and merge multiple SAS datasets to facilitate combined analysis, run SQL queries to manipulate data, compute new variables and format data.\nUse SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables. \n","MM424: Coursework, consisting of the analysing a multivariate dataset based on a case study.","Throughout this module, students will be presented with data in different formats and will need to learn to manipulate and/or import this data so that it can be analysed.",1,1,null,null,null,"good evidence but just focused on SAS software - peer review question?? Is focusing on SAS too narrow","Agree - Would be good to check with peer reviewer"]
["S5","S5 assess the impact on user experience and domain context on the data analysis activity","Programme LOs:                                     LO12     \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","OS101: Lectures on Business Analytics with real-world examples of the impact of data analytics.                                          \nIndicative Content: Process and layout types, process design, role of IT in operations management, Business intelligence, big data and measuring, operations improvement approaches & techniques.","OS101: Open book online test",null,1]
["S6","S6 identify and escalate quality risks in data analysis with suggested mitigation/resolutions as appropriate.",null,null,"PARTIALLY MET","Possibly covered by MM410 Coursework"," (Potentially) MM410: Coursework- A case study involving data visualisation/analysis.","This topic is discussed during the formative coursework assessment where students collect their own data sets for analysis. They are strongly  encouraged to think about/reflect on this in their MM410 coursework but as this is independent coursework, it cannot be guaranteed that all students will engage with it at the assessment level.",0.5]
["S7","S7 undertake customer requirements analysis and implement findings in data analytics planning and outputs","Programme LOs:                                        LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis.  LO8     Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information. \nLO11      Students will have the ability to effectively communicate the results and conclusions of statistical analysis appropriate for the intended audience.\nLO12     \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.",null,"FULLY MET","OS101: Small Business Systems and Information Systems Priority planning.                            Indicative Content:           Systems for small business models, project management, role of IT in operations management, Transformation processes, Operations improvement approaches & Techniques.\nMM424:  LO1: Analyse an application problem and select appropriate data, program structures and statistical analysis methods to perform a data analysis task. \nIndicative Content: Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; \nFamiliarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology.","OS101: Open book online test                               MM424: Report: The assessment will consist of analysing a multivariate dataset at the request of a hypothetical business \"client\" who wishes to understand more about customer behaviour in order to improve business/sales. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results and any recommendations in a well-structured data analysis report","Though  a hypothetical customer/situation will be used for this coursework, the students will be asked to treat this case study as they would in the role of a data analyst in the workplace and make appropriate recommendations.",1]
["S8","S8 identify data sources and the risks, challenges to combination within data analysis activity","We believe that we may satisfy this criterion with the MM424 report but are unsure whether it exactly matches any of our Learning Outcomes.",null,"PARTIALLY MET","Potentially covered by MM424 report.","MM424: Report- The assessment will consist of analysing a multivariate dataset. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report",null,0.5]
["S9","S9 apply organizational architecture requirements to data analysis activities",null,null,"NOT MET ",null,null,"Whilst this criterion is not directly satisfied/assessed by any of the modules, students will learn the information required to do this in OS101 and therefore it is a skill they should theoretically be able to satisfy.",0,null,"PARTIALLY MITIGATED "]
["S10","S10 apply statistical methodologies to data analysis tasks","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.     \nLO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving. \nLO4     Students will be able to demonstrate knowledge and understanding of the importance of assumptions, awareness of their use and consequences of their violation.  \nLO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis.\nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis.  \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information. \nLO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM410:  LO1 - Demonstrate an understanding of the need for probabilistic and statistical methods to analyse real-world problems and use appropriate software; LO2 - explore and summarise real-world data sets;  LO4 - Demonstrate an understanding of the role of the sampling distribution, estimate model parameters and conduct hypothesis tests.\nIndicative Content:      Data sources and their exploration and visualisation; sampling distribution;  estimation of parameters; significance testing.        \nMM424: LO1: Analyse an application problem and select appropriate data, program structures and statistical analysis methods to perfom a data analysis task;LO2: Develop and execute a program with SAS code for working with data in a project, understanding the basics of data manipulation and producing summary statistics. LO3: Visualize and explore the data using charts, plots, diagrams and tables.  \nIndicative Content:  Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; Familiarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology.                                         \nMM425: LO1-Demonstrate an effective use of Excel in the context of various aspects of data analysis.  \nIndicative Content: Data Analysis and Manipulation with Python- Using Python to import, analyse and display data in a variety of ways.","MM410: Exam & Coursework- In the exam, students will be presented with questions on various data sets and will be tasked with applying appropriate analysis in order to answer the given questions. In the courseowrk, student will each analyse their own personal data set and will be asked to conduct specific analysis as well as being given the opportunity to carry out analyses of their own choosing.                      MM424: Coursework-The assessment will consist of writing a SAS program to import given data, combining multiple datasets, transforming the data, exploring and visualising the data, and producing summary statistics; Report-  The assessment will consist of analysing a multivariate dataset. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report.                            MM425: Portfolio- students will select appropriate statistical methods to analyse the given data.","Students will have a vast number of opportunities to apply statistical methodologies to data analysis tasks both in the context of formal assessments and also in weekly tutorial exercises which they complete both in class and as part of their independent learning. ",1]
["S11","S11 apply predictive analytics in the collation and use of data","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications. \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions.",null,"FULLY MET","MM424: LO1: Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task; LO3: Visualize and explore the data using charts, plots, diagrams and tables. \nIndicative Content: Import data, combine and merge multiple SAS datasets to facilitate combined analysis, run SQL queries to manipulate data, compute new variables and format data; Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables; Familiarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology.","MM424: Report & Coursework- the assessments will give students the opportunity to analyse business data and make recommendations based on predictive analysis techniques.",null,1,1,null,null,null,"No mention of predictive analytics in content, but assessment method does. Forecasting is mentioned in the content. The M424 module is focused on SAS software","Agree"]
["S12","S12 collaborate and communicate with a range of internal and external stakeholders using appropriate styles and behaviours to suit the audience","Programme LOs:                                     LO12     \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.\nLO13     \tStudents will demonstrate a professional approach to time management and working effectively within a team\nLO14    Develop the ability to communicate effectively with awareness and understanding of the intended audience.",null,"PARTIALLY MET","MM405: Indicative Content: Introduction to management style reporting. ","MM405: Management style report and oral presentation.","Though not working directly with external stakeholders, this assessment challenges students to approach the report and presentation as though directly communicating to those external stakeholders to whom the (hypothetical) task relates. They are expected to demonstrate a professional approach and to treat their assessment as a formal business presentation.\n\nIn addition, students are asked to write reports directed at both technical and non-technical audiences throughout the course, thus giving them experience of aiming their explanations and communication at a variety of audiences.\n\n",0.5,1,null,null,null,"The supporting comments give confidence that students will develop this skill as far as able to within a CoE","Agree"]
["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","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications        \nLO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving. LO4     Students will be able to demonstrate knowledge and understanding of the importance of assumptions, awareness of their use and consequences of their violation.\n LO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. LO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM424: LO1: Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task; LO3: Visualize and explore the data using charts, plots, diagrams and tables. \nIndicative Content: Familiarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS.\nAnalytics lifecycle describing the steps based on the CRISP-DM methodology.","MM424: Report & Coursework- Students are encouraged to apply a range of analytical techniques taught across their modules in order to approach these assessment tasks. ",null,1]
["S14","S14 collate and interpret qualitative and quantitative data and convert into infographics, reports, tables, dashboards and graphs","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.      \nLO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information. \nLO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting.\nLO11      Students will have the ability to effectively communicate the results and conclusions of statistical analysis appropriate for the intended audience.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM410: LO2 - explore and summarise real-world data sets;  LO3 - apply some standard probability and expectation notions and models in a variety of contexts. \nIndicative Content:   Data sources and their exploration and visualisation.\nMM424:  LO3: Visualize and explore the data using charts, plots, diagrams and tables.    \nIndicative Content:  Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables.                    \nMM425:  LO1- Demonstrate an effective use of Excel in the context of various aspects of data analysis; LO4- Demonstrate an effective use of the Python programming language in the context of manipulating and displaying data. \nIndicative Content:   Spreadsheets- Using spreadsheets as a tool for organising and displaying data; Data Analysis and Manipulation with Python- Using Python to import, analyse and display data in a variety of ways.","MM410: Exam & Coursework- The exam tests the students' ability to choose,  generate and interpret appropriate statistical output to model given problems whilst the coursework gives students the opportunity to work with a uniue data set and extract as much information as possible using both graphical, tabular, descriptive and statistical techniques.                     MM424: Coursework- Carry out analysis of a multivariate dataset. Students will present the objectives of the case study and analyse the data using appropriate statistical methods, interpretating and presenting the results in a well-structured data analysis report                                    MM425: Portfolio- Students will select appropriate software to display and analyse data, providing a detailed interpretation of their findings.",null,1]
["S15","S15 select and apply the most appropriate data tools to achieve the best outcome","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.       \nLO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving. \nLO4     Students will be able to demonstrate knowledge and understanding of the importance of assumptions, awareness of their use and consequences of their violation. \nLO5     Students will be able to demonstrate knowledge and understanding of the use and application of software within data analysis. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis.  \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO9      Students will have the ability to choose and use appropriate software to aid data analysis processes and acquire and interpret further information. \nLO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM410: LO2 - explore and summarise real-world data sets. \nIndicative Content:   Data sources and their exploration and visualisation,  significance testing.      \nMM424: LO1: Analyse an application problem and select appropriate data and program structures; and statistical analysis methods to perform a data analysis task; LO3: Visualize and explore the data using charts, plots, diagrams and tables.         \nIndicative Content:      Use SAS procedures and tasks to explore and visualize the data using descriptive statistics, summary reports, charts, plots and tables. \nFamiliarise students with basic statistical modelling with examples to illustrate a data analysis case study, which will consider exploring relationship between variables, correlation, simple linear regression and basic forecasting methods implemented in SAS; Analytics lifecycle describing the steps based on the CRISP-DM methodology.                       \nMM425: Students will need to demonstrate this skill for the portfolio exercise tasks.                                         ","MM410: Coursework- students are given the opportunity to choose which software to utilise to complete their coursework as well as being able to choose the direction of their analyses in order to generate a  meaningful set of results.                     MM424: Report & Coursework- Students will develop their own SAS programmes to analyse the data sets provided for these assessments.                  MM425: Portfolio- Students will need to demonstrate this skill for the portfolio exercise tasks.","Students will develop a variety of software skills across this course (including the use of SAS, SQL, SPSS, Excel VBA and Python). They will therefore have a plethora of tools at their disposal when it comes to choosing how to analyse and present data.",1]
[null,"Behaviours"]
["B1","B1: maintain productive, professional and secure working environment","Programme LOs:                                       LO6     Students will be able to demonstrate knowledge and understanding of the importance of data management, data ethics and cyber security.",null,"FULLY MET","All modules require adherence to university regulations that are meant to promote  a professional and secure working environment, including on data protection: https://www.brighton.ac.uk/brighton-students/your-learning/student-policies-and-regulations/index.aspx","Demonstrated across all modules.",null,{"$f":"IF(E37:E43 = \"FULLY MET\",1,IF(E37:E43 = \"PARTIALLY MET\",0.5,IF(E37:E43 = \"NOT MET\",0,)))","v":1}]
["B2","B2: shows initiative, being resourceful when faced with a problem and taking responsibility for solving problems within their own remit","Programme LOs:                                       LO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving.\nLO10      Students will have the ability to Research and learn independently and apply this knowledge in a data analysis setting.   \nLO13\tStudents will demonstrate a professional approach to time management and working effectively within a team.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM405: Business production planning investigation in small team.        \nMM424: Individual report - case study analysis, select appropriate statistical methods, interpretation of the results and presentation to produce a well-structured data analysis report. \nMM425: Portfolio investigative tasks. ","MM405: Management style report and oral presentation.                                MM424: Report & Coursework . The report will consist of writing a SAS program to import given data, combining multiple datasets, transforming the data, exploring and visualising the data, and producing summary statistics.                                 The Coursework will consist of analysing a multivariate dataset based on a case study.                MM425: Portfolio","Students are encouraged to conduct independent research in order to solve assessment problems.",1]
["B3","B3: works independently and collaboratively","Programme LOs:                                     LO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting.\nLO12     \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.\nLO13     \tStudents will demonstrate a professional approach to time management and working effectively within a team\nLO14    Develop the ability to communicate effectively with awareness and understanding of the intended audience.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM405: Group of 3-4 students work on a production planning investigation.               \nMM410: Students collaborate in groups of 4-5 on a formative data analysis project before undertaking an individual data analysis coursework. \nAll other assessments are individual assessments requiring the students to work independently.","MM405: Management style group report and oral presentation.","The course provides both a formative and summative group work assessment opportunity. Whilst all other summative assessments are individual, students are encouraged to collaborate to solve problems during lectures and tutorials.",1]
["B4","B4: logical and analytical","Programme LOs:                                       LO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis.",null,"FULLY MET","MM405: Introduces students to analytical/mathematical topics in Operations research, such as network analysis, linear programming, stock control and simulation techniques. \nMM424: Individual report - case study analysis, select appropriate statistical methods, interpretation of the results and presentation to produce a well-structured data analysis report. ","MM405: Management style report and oral presentation (coursework) & Examination.                                          MM424: Report & Coursework","In particular, these assessments (two of which are aimed at writing formal reports for external \"customers\") require students to develop a clear strategy and systematically work through their analysis with careful consideration of how to proceed at each step.",1]
["B5","B5: 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.","Programme LOs:                                       LO2     Students will be able to demonstrate knowledge and understanding of a variety of statistical modelling techniques and their applications.      \nLO3     Students will be able to demonstrate knowledge and understanding of structured, analytical approaches to problem solving. \nLO4     Students will be able to demonstrate knowledge and understanding of the importance of assumptions, awareness of their use and consequences of their violation. \nLO7      Students will have the ability to plan and undertake a wide variety of statistical analysis. \nLO8      Students will have the ability to engage in the processes of statistical modelling, identifying assumptions and drawing appropriate conclusions. \nLO10      Students will have the ability to research and learn independently and apply this knowledge in a data analysis setting. \nLO12     \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.\nLO13\tStudents will demonstrate a professional approach to time management and working effectively within a team\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","MM405: Groups of 3-4 students work independently on a production planning investigation.                             \nIndicative Content: The module introduces students to Operational research problem-solving techniques, such as network analysis, transportation problems, linear programming, stock control and simulation techniques.                                      \nMM410:  LO1 - Demonstrate an understanding of the need for probabilistic and statistical methods to analyse real-world problems and use appropriate software; LO2 - explore and summarise real-world data sets; LO3 - apply some standard probability and expectation notions and models in a variety of contexts; LO4 - Demonstrate an understanding of the role of the sampling distribution, estimate model parameters and conduct hypothesis tests.            \nIndicative Content:  Data sources and their exploration and visualisation;  significance testing.","MM405: Coursework & Exam.      \nMM410: Exam & Coursework","In particular, the MM405 coursework provides students with a challenging problem where there are many possible approaches the students could take to solve this. It requires research and investigation as well as persistence in order to come up with a suitable solution.",1]
["B6","B6: demonstrates resilience by viewing obstacles as challenges and learning from failure.","Programme LOs:                                     LO12     \tDevelop adaptability so as to be able to transfer expertise, knowledge and understanding from one context to another.                                                   LO13      \tStudents will demonstrate a professional approach to time management and working effectively within a team\nLO15      Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","The course includes multiple assessment points which demand effort, resilience and careful time management. Formative assessments provide opportunities to learn from mistakes.","All Modules","Students receive detailed feedback and feedforward on all submitted assessment tasks and are encouraged to engage with this to learn from their mistakes/misunderstandings and improve their future work. ",1]
["B7","B7: demonstrates an ability to adapt to changing contexts within the scope of a project, direction of the organisation or Data Analyst role.","Programme LOs:                                       LO10      Students will have the ability to Research and learn independently and apply this knowledge in a data analysis setting.\nLO13      \tStudents will demonstrate a professional approach to time management and working effectively within a team.\nLO14    Develop the ability to communicate effectively with awareness and understanding of the intended audience.\nLO15    Learn independently in order to enhance understanding of concepts and practical skills.",null,"FULLY MET","The course gives the student a good grounding in the principles and real-world applications of data analysis and the phases of a project life cycle.  ","CI412: Database design coursework.\nMM405: Coursework\nMM410: Coursework\nMM424: Report & Coursework. The report will consist of writing a SAS program to import given data, combining multiple datasets, transforming the data, exploring and visualising the data, and producing summary statistics.                                 The Coursework will consist of analysing a multivariate dataset based on a case study.\nMM425: Portfolio","The assessments listed all give students the opportunity to explore open problems/datasets and to adapt their approaches throughout the tasks based on initial and subsequent investigations/results.",1,0.5,"PARTIALLY MITIGATED ",null,null,"This is at least partially met and posssibly fully met","I agree partially met"]
[]
[]
[null,null,null,null,null,null,null,null,"Gross score",{"$f":"SUM(J5:J43)","v":8.5},"Net Score",{"$f":"SUM(L5:L43)","v":0}]
[null,null,null,null,null,null,null,null,"total KSB coverage",{"$f":"J46/L50","v":0.22972972972972974},"total CoE KSB coverage",{"$f":"L46/L53","v":0}]
[]
[]
[null,null,null,null,null,null,null,null,null,null,"total KSBs",37]
[null,null,null,null,null,null,null,null,null,null,"fully mitigated",0]
[null,null,null,null,null,null,null,null,null,null,"partialy mitigated",5]
[null,null,null,null,null,null,null,null,null,null,"net KSB",{"$f":"L50-L51-(L52*0.5)","v":34.5}]

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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)"]