The module will cover topics such as:

Introduction to business data analytics

The significance of data analytics to business

Types of data analytics tools, techniques, and technologies

Data-driven Decision-Making for businesses

Legal and ethical issues surrounding data collection, utilisation, and management.





This module will support the development and assessment of the following Data Analyst Professional Skill from the DTSP Apprenticeship Standard:

Knowledge

DAK1 The barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these.

DAK4 Sources of data such as files, operational systems, databases, web services, open data, government data, news and social media.

DAK5 Approaches to data processing and storage, database systems, data warehousing and online analytical processing, data-driven decision making and the use of evidence and analytics in making choices and decisions.

DAK6 How Data Analytics operates within the context of data governance, data security, and communications.

DAK7 How Data Analytics can be applied to improve an organisation’s processes, operations and outputs.

DAK8 How data and analysis may exhibit biases and prejudice. How ethics and compliance affect Data Analytics work, and the impact of international regulations. For example, General Data Protection Regulation, Data Protection Act 2018.



Skills

DAS4 Identify barriers to effective analysis encountered both by analysts and their stakeholders within data analysis projects.

DAS6 Apply exploratory or confirmatory approaches to analysing data. Validate and test stability of the results.

DAS7 Extract data from a range of sources. For example, databases, web services, open data.

An individual report that illustrates the analysis, research, ethics consideration, business requirements, and design of basic concepts in Data Analytics - Learning Outcomes 1, 2 and 3.



Assessing the following Data Analyst Professional Knowledge and Skills:

Knowledge

DAK1 The barriers that exist to effective data analysis between analysts and their stakeholders and how to avoid or resolve these.

DAK4 Sources of data such as files, operational systems, databases, web services, open data, government data, news and social media (

DAK5 Approaches to data processing and storage, database systems, data warehousing and online analytical processing, data-driven decision making and the use of evidence and analytics in making choices and decisions.

DAK6 How Data Analytics operates within the context of data governance, data security, and communications.

DAK7 How Data Analytics can be applied to improve an organisation’s processes, operations and outputs.

DAK8 How data and analysis may exhibit biases and prejudice. How ethics and compliance affect Data Analytics work, and the impact of international regulations. For example, General Data Protection Regulation, Data Protection Act 2018.

Skills

DAS4 Identify barriers to effective analysis encountered both by analysts and their stakeholders within data analysis projects (LO 1).



A portfolio demonstrating the application of basic data analytics tools and techniques - Learning Outcomes 1, 2 and 3.



Assessing the following Data Analyst Professional Knowledge and Skills:

Knowledge

DAK4 Sources of data such as files, operational systems, databases, web services, open data, government data, news and social media

DAK5 Approaches to data processing and storage, database systems, data warehousing and online analytical processing, data-driven decision making and the use of evidence and analytics in making choices and decisions.

DAK8 How data and analysis may exhibit biases and prejudice. How ethics and compliance affect Data Analytics work, and the impact of international regulations. For example, General Data Protection Regulation, Data Protection Act 2018.

Skills

DAS6 Apply exploratory or confirmatory approaches to analysing data. Validate and test stability of the results.

DAS7 Extract data from a range of sources. For example, databases, web services, open data.

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

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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.¿

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The delivery will be delivered as follows:¿

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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.¿

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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.¿

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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 solutions, review working drafts etc.¿

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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 your workplace experience to data and in others less so depending on the nature of your workplace duties, this will have a direct impact on to the number of Independent Learning required.¿

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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. Demonstrate a clear knowledge of the significance and role of data analytics for businesses and reflect on impact of data analytics methods and techniques for businesses

2. Identify and document applications of Data Analytics methods and techniques within business contexts and be able to identify solutions that address common associated concerns and problems that emerge in related domain.

3. Demonstrate the ability to identify the legal and ethical issues and approaches taken to accommodate them in solutions and demonstrate the ability to reflect on ethical issues arising from the emergence of Data Analytic outputs

These are indicative only. Texts are updated on an annual basis and when you start to study this module, you will be referred to an online reading list, currently provided through Keylinks. You are advised not to buy any textbooks for this module without checking the online reading list.



Moses, Gavish, Vorwerck (2022) Data Quality Fundamentals: O'Reilly Media, Inc. ISBN: 9781098112042



Paul, D. (2020), Business analysis, 3rd edn, BCS, Swindon. ISBN: 1780175108; 978-1780175102.



Cadle, J., Paul, D., and Turner, P. (2014) Business Analysis Techniques: 99 Essential Tools for Success. 2nd Edition. BCS, Swindon. ISBN 1906124236



Krishnan, K. (2013) Data Warehousing in the Age of Big Data. San Francisco: Elsevier Science & Technology. ISBN: 0124058914

Blackburn, S. (2003) Ethics, a Very Short Introduction, Oxford Press, ASIN: B00DEKQQQK



Suikkanen, j. (2015) This is Ethics: An Introduction, Wiley-Blackwell, ISBN-10: 1118479858



Gordon, K. Principles of Data Management (2nd edition) (2013) ISBN: 9781780171845

Blackboard (VLE)

University library

LinkedIn Learning

Web Browser

This module introduces you to the main concepts and ideas in the domain of Data Analytics within a context of its use in the business world. Teaching on the module will be explorative using case studies and emerging research to teach and inform on the key concepts.

You will be exposed to core data analytics concepts and models, the current technology landscape, and topical application scenarios that are using a variety of environments and datasets. Within this module we shall also discuss and consider the morals and ethics we need to use in creating a new society in the modern technological age.