Mathematics for Data Analysis and Computing

Basic arithmetic and mathematical concepts required for Computing including.

Propositional Logic

The basics and operators, truth tables, equivalence, valid arguments.

The concept of a function and inverse functions for simple polynomials.

Probability - Rules of probability, Probability trees, Conditional Probability.

Descriptive Statistics, spreadsheets such as Excel/Google sheets, tabulating and charting data, averages, measures of spread.

Use of graphs and charts for the presentation of statistics



Primary Research and Data

Primary research techniques and tools

Presentation skills and presentation tools such as MS PowerPoint

Data quality and value

Legislation around data collection and storage

Teamwork.



This module will support the development and assessment of the following Core Knowledge, Skills and Behaviours from the DTSP Apprenticeship Standard:

Knowledge

K8 How teams work effectively to produce digital and technology solutions.

K9 The concepts and principles of leadership.

K13 Principles of data analysis for digital and technology solutions.

K14 A range of quantitative and qualitative data gathering methods and how to appraise and select the appropriate method.

K17 Reporting techniques, including how to synthesise information and present concisely, as appropriate to the target audience.

K18 Techniques of robust research and evaluation for the justification of digital and technology solutions.

K19: Relevant legal, ethical, social and professional standards to a digital and technology solution. For example, Diversity, Accessibility, Intellectual Property, Data Protection Acts, Codes of Practice, Regulatory and Compliance frameworks.

Skills

S7 Work effectively within teams, leading on appropriate digital technology solution activities.

S11 Determine and use appropriate data analysis techniques. For example, Text, Statistical, Diagnostic or Predictive Analysis, to assess digital and technology solutions.

S13 Report effectively to colleagues and stakeholders using the appropriate language and style, to meet the needs of the audience concerned.

Behaviours

B2 Reliable, objective and capable of independent and team working.

B3 Acts with integrity with respect to ethical, legal and regulatory requirements ensuring the protection of personal data, safety and security.

B5 Interacts professionally with people from technical and non-technical backgrounds. Presents data and conclusions in an evidently truthful, concise and appropriate manner.

B8 Champions diversity and inclusion in their work ensuring that digital technology solutions are accessible.

This module will support the development and assessment of the following Specialist Route Knowledge, Skills and Behaviours from the DTSP Apprenticeship Standard:



Data Analyst

Skills

S48 Define Data Requirements and perform Data Collection, Data Processing and Data Cleansing.

S52 Apply a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics and modelling techniques to identify and predict trends and patterns in data.





WEB DESCRIPTOR

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Decision making needs to be informed by data and information, working in small teams of fellow apprentices you will undertake a collaborative research project to make and present conclusions from your work. Building on a grounding of appropriate maths and stats techniques and tools your research team will required to design a research tool to obtain primary data and present your findings making justifiable observations from your data to an audience as required. Project requires the collaboration of individuals, and you will consider your role in your team and reflect on the achievements of the project and how you can ensure further collaborative work is a success.









LEARNING STRATEGIES



The module will be delivered in a Blended Learning Mode consisting of face to face, online and guided learning sessions.



Teaching sessions will blend theory and practical learning and most importantly where possible contextualised in your workplace as part of your apprenticeship. Learners will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples. In addition, students will be provided with a range of resources for independent study such as case studies, academic papers and industry case studies. There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.



The delivery will be delivered as follows:



Module Launch week: 12 hours.

There will be a module launch session consisting of up to 12 hours face to face contact time devoted to developing your understanding of the core purpose and assessment of the module. Learners will be presented with details of how the learning will be structure and how to access to the learning materials for the remainder of the module.



Structured Learning Sessions: 15 hours

Following the module launch week you will have a further 15 hours of contact time as a class with the module team. This will typically be as 10 x 1.5-hour online classes which will be a combination of activities including lectures, demonstrations, discussions, tutorials and seminars. Some sessions are likely to be in flipped classroom style, where you will be expected to watch online recordings, read materials or respond to practical activities in preparation for active engagement with problem solving in the online session.



1:1 Progress Checks: 1 hour

As a Blended Learner understanding your progress can be a challenge so you are allocated an hour of 1:1 time with your tutor (typically 3 x 20 minute). Some of these may be in small groups if appropriate. These sessions may be used to discuss key topics, troubleshoot salutations, review working drafts etc.



Guided Independent Learning: 178 hours.

The module leader will provide resources through the virtual learning environment which will include videos and presentations as well as links to useful websites and other resources. Additional academic learning will be achieved through reading around the subject area, module tutors will suggest useful texts, though many others will be suitable and can be found in our e-library. You should also draw on the expertise in your workplace via your workplace mentor and other colleagues. If you require help understanding any of the concepts, you should contact your module tutor for assistance.

As an apprentice you are constantly developing your Digital Skills as part of your substantial role, and this applies to the development of the knowledge for your modules too. In some cases, there will be a significant cross over between the module content and in others less so, depending on the nature of your workplace duties, this will have direct impact on to the number of Independent Learning required.



Within the Independent learning time you will be expected to complete your assignments, as a guide a typical module assignment should take around 60 hours to complete.

1. Mathematics Class Test (45 minutes) weighted at 30% - Learning outcomes 4.



Assessing the following Core KSBs

Knowledge

K13 Principles of data analysis for digital and technology solutions.



2. A spreadsheet model with presentation of statistics based on a case study weighted at 20% - Learning outcomes 3 & 4.

Assessing the following Core KSBs

Skill

S11 Determine and use appropriate data analysis techniques. For example, Text, Statistical, Diagnostic or Predictive Analysis, to assess digital and technology solutions.

.



3. Collaborative Primary research Project and Presentation weighted at 35% - Learning outcomes 1,2 & 3
A group presentation, of up to 15 slides lasting 20 minutes of a Primary Research activity including a spreadsheet showing further data analysis, with a further 10 minutes for questions.

Assessing the following Core KSBs

Knowledge

K8 How teams work effectively to produce digital and technology solutions.

K13 Principles of data analysis for digital and technology solutions.

K14 A range of quantitative and qualitative data gathering methods and how to appraise and select the appropriate method.

K17 Reporting techniques, including how to synthesise information and present concisely, as appropriate to the target audience.

K18 Techniques of robust research and evaluation for the justification of digital and technology solutions.

K19: Relevant legal, ethical, social and professional standards to a digital and technology solution. For example, Diversity, Accessibility, Intellectual Property, Data Protection Acts, Codes of Practice, Regulatory and Compliance frameworks.

Skill

S11 Determine and use appropriate data analysis techniques. For example, Text, Statistical, Diagnostic or Predictive Analysis, to assess digital and technology solutions.

S13 Report effectively to colleagues and stakeholders using the appropriate language and style, to meet the needs of the audience concerned.



4. An individual report including research into teamwork theories weighted at 15% - Learning outcomes 1 & 2.

An evaluation of the group work experience with reference to the theories and a plan of action for approaches to group work.

Assessing the following Core KSBs

Knowledge

K8 How teams work effectively to produce digital and technology solutions.

Skills

S7 Work effectively within teams, leading on appropriate digital technology solution activities.

S13 Report effectively to colleagues and stakeholders using the appropriate language and style, to meet the needs of the audience concerned.







INDICATIVE CONTENT



Mathematics for Data Analysis and Computing

Basic arithmetic and mathematical concepts required for Computing including.

Propositional Logic

The basics and operators, truth tables, equivalence, valid arguments.

The concept of a function and inverse functions for simple polynomials.

Probability - Rules of probability, Probability trees, Conditional Probability.

Descriptive Statistics, spreadsheets such as Excel/Google sheets, tabulating and charting data, averages, measures of spread.

Use of graphs and charts for the presentation of statistics



Primary Research and Data

Primary research techniques and tools

Presentation skills and presentation tools such as MS PowerPoint

Data quality and value

Legislation around data collection and storage

Teamwork.



This module will support the development and assessment of the following Core Knowledge, Skills and Behaviours from the DTSP Apprenticeship Standard:

Knowledge

K8 How teams work effectively to produce digital and technology solutions.

K9 The concepts and principles of leadership.

K13 Principles of data analysis for digital and technology solutions.

K14 A range of quantitative and qualitative data gathering methods and how to appraise and select the appropriate method.

K17 Reporting techniques, including how to synthesise information and present concisely, as appropriate to the target audience.

K18 Techniques of robust research and evaluation for the justification of digital and technology solutions.

K19: Relevant legal, ethical, social and professional standards to a digital and technology solution. For example, Diversity, Accessibility, Intellectual Property, Data Protection Acts, Codes of Practice, Regulatory and Compliance frameworks.

Skills

S7 Work effectively within teams, leading on appropriate digital technology solution activities.

S11 Determine and use appropriate data analysis techniques. For example, Text, Statistical, Diagnostic or Predictive Analysis, to assess digital and technology solutions.

S13 Report effectively to colleagues and stakeholders using the appropriate language and style, to meet the needs of the audience concerned.

Behaviours

B2 Reliable, objective and capable of independent and team working.

B3 Acts with integrity with respect to ethical, legal and regulatory requirements ensuring the protection of personal data, safety and security.

B5 Interacts professionally with people from technical and non-technical backgrounds. Presents data and conclusions in an evidently truthful, concise and appropriate manner.



This module will support the development and assessment of the following Specialist Route Knowledge, Skills and Behaviours from the DTSP Apprenticeship Standard:



Data Analyst

Skills

S48 Define Data Requirements and perform Data Collection, Data Processing and Data Cleansing.

S52 Apply a range of techniques for analysing quantitative data such as data mining, time series forecasting, algorithms, statistics and modelling techniques to identify and predict trends and patterns in data.

1. Reflect on the aspects of professional practice such as team working, self-assessment, peer assessment, leadership, negotiation, influence and motivation making clear, concise, engaging and well-structured verbal and written communication of arguments and explanations.

Reflection

Communication

2. Apply appropriate primary research techniques to source data understanding key concepts including legislation and ethics around data collection and storage (including confidentiality, integrity and availability). data quality, data security and the limitations of the data in terms of reliability and bias.

Knowledge & Understanding

Enquiry

3. Use tools to analyse both quantitative and qualitative data in a legitimate and effective fashion using graphs and charts where appropriate to present information in a suitable format for decision making.

Knowledge & Understanding

Application

Learning

Analysis

Communication

4. Understand and apply mathematical concepts and processes that are applicable to using and developing digital technology solutions

Knowledge & Understanding

The module will be delivered in a Blended Learning Mode consisting of face to face, online and guided learning sessions.



Teaching sessions will blend theory and practical learning and most importantly where possible contextualised in your workplace as part of your apprenticeship. Learners will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples. In addition, students will be provided with a range of resources for independent study such as case studies, academic papers and industry case studies. There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help students build knowledge and confidence in preparation for summative (formal) assessment.



The delivery will be delivered as follows:



Module Launch week: 12 hours.

There will be a module launch session consisting of up to 12 hours face to face contact time devoted to developing your understanding of the core purpose and assessment of the module. Learners will be presented with details of how the learning will be structure and how to access to the learning materials for the remainder of the module.



Structured Learning Sessions: 15 hours

Following the module launch week you will have a further 15 hours of contact time as a class with the module team. This will typically be as 10 x 1.5-hour online classes which will be a combination of activities including lectures, demonstrations, discussions, tutorials and seminars. Some sessions are likely to be in flipped classroom style, where you will be expected to watch online recordings, read materials or respond to practical activities in preparation for active engagement with problem solving in the online session.



1:1 Progress Checks: 1 hour

As a Blended Learner understanding your progress can be a challenge so you are allocated an hour of 1:1 time with your tutor (typically 3 x 20 minute). Some of these may be in small groups if appropriate. These sessions may be used to discuss key topics, troubleshoot salutations, review working drafts etc.



Guided Independent Learning: 178 hours.

The module leader will provide resources through the virtual learning environment which will include videos and presentations as well as links to useful websites and other resources. Additional academic learning will be achieved through reading around the subject area, module tutors will suggest useful texts, though many others will be suitable and can be found in our e-library. You should also draw on the expertise in your workplace via your workplace mentor and other colleagues. If you require help understanding any of the concepts, you should contact your module tutor for assistance.

As an apprentice you are constantly developing your Digital Skills as part of your substantial role, and this applies to the development of the knowledge for your modules too. In some cases, there will be a significant cross over between the module content and in others less so, depending on the nature of your workplace duties, this will have direct impact on to the number of Independent Learning required.



Within the Independent learning time you will be expected to complete your assignments, as a guide a typical module assignment should take around 60 hours to complete.

All texts and electronic resources will be updated and refreshed on an annual basis and available for students via the online Study Links resource platform. All reference materials will be collated and curated and aligned to Equality, Diversity & Inclusion indicators.

CROFT, A., DAVIDSON, R., (2020), Foundation Maths, Addison-Wesley (Edition: 7)

COTTRELL, S. (2019), The Study Skills Handbook, Bloomsbury Academic (Edition: 5)

QUINN, M. (2010) Ethics for the Information Age: International Edition, Pearson Education (Edition: 4)

Brown,¿Victoria,¿and¿Belbin,¿(2022) Team Roles at Work.,¿Taylor & Francis Group. (Edition: 3)

Decision making needs to be informed by data and information, working in small teams of fellow apprentices you will undertake a collaborative research project to make and present conclusions from your work. Building on a grounding of appropriate maths and stats techniques and tools your research team will required to design a research tool to obtain primary data and present your findings making justifiable observations from your data to an audience as required. Project requires the collaboration of individuals, and you will consider your role in your team and reflect on the achievements of the project and how you can ensure further collaborative work is a success.