This module will examine the issues involved in selecting data for analysis in the context of Business data and Big Data and will consider whether and how techniques associated with Data Mining can be used to enhance business functions.
Topics covered will include:

The definition of data harvesting and its relevance in a Big Data context

Data preparation in a Business Data context (gathering and validating data, and evaluating the quality of the data)

Identifying analysis requirements in a Business context

Concepts that underpin data mining

Tools and techniques for data mining

Programming for data analysis

Evaluation of tools and techniques and suitability for use in specified contexts

Introduction to Business Data Analytics

Data Analytics Lifecycle

Clustering

Association Rules

Regression

Classification

Time Series Analysis

Text Analysis

Data visualisation techniques



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



Data Analytics Specialist

Knowledge

DAK1 How key algorithms and models are applied in developing analytical solutions and how analytical solutions can deliver benefits to organisations;

DAK2 The information governance requirements that exist in the UK, and the relevant organisational and legislative data protection and data security standards that exist. The legal, social and ethical concerns involved in data management and analysis;

DAK3 The principles of data driven analysis and how to apply these. Including the approach, the selected data, the fitted models and evaluations used to solve data problems;

DAK4 The properties of different data storage solutions, and the transmission, processing and analytics of data from an enterprise system perspective. Including the platform choices available for designing and implementing solutions for data storage, processing and analytics in different data scenarios;

DAK5 How relevant data hierarchies or taxonomies are identified and properly documented;

DAK6 The concepts, tools and techniques for data visualisation, including how this provides a qualitative understanding of the information on which decisions can be based.

Skills

DAS1 Identify and select the business data that needs to be collected and transitioned from a range of data systems; acquire, manage and process complex data sets, including large-scale and real-time data;

DAS2 Undertake analytical investigations of data to understand the nature, utility and quality of data, and developing data quality rule sets and guidelines for database designers;

DAS3 Formulate analysis questions and hypotheses which are answerable given the data available and come to statistically sound conclusions;

DAS4 Conduct high-quality complex investigations, employing a range of analytical software, statistical modelling & machine learning techniques to make data driven decisions solve live commercial problems;

DAS5 Document and describe the data architecture and structures using appropriate data modelling tools, and select appropriate methods to present data and results that support human understanding of complex data sets;

DAS6 Scope and deliver data analysis projects, in response to business priorities, create compelling business opportunities reports on outcomes suitable for a variety of stakeholders including senior clients and management.

Written Report – A written report in the format of a white paper on approaches and issues involved in data harvesting and data modelling which makes recommendations on the use of these areas in the context of an organisation

Learning Outcomes 1, 3, 4



Practical Assessment - An application of data mining tools and techniques in a data mining environment

Learning Outcomes 1, 2


Assessing the following KSBs from the DTSS apprenticeship standard

Data Analyst

Knowledge

DAK1 How key algorithms and models are applied in developing analytical solutions and how analytical solutions can deliver benefits to organisations;

DAK2 The information governance requirements that exist in the UK, and the relevant organisational and legislative data protection and data security standards that exist. The legal, social and ethical concerns involved in data management and analysis;

DAK3 The principles of data driven analysis and how to apply these. Including the approach, the selected data, the fitted models and evaluations used to solve data problems;

DAK5 How relevant data hierarchies or taxonomies are identified and properly documented;

DAK6 The concepts, tools and techniques for data visualisation, including how this provides a qualitative understanding of the information on which decisions can be based.

Skills

DAS2 Undertake analytical investigations of data to understand the nature, utility and quality of data, and developing data quality rule sets and guidelines for database designers;

DAS3 Formulate analysis questions and hypotheses which are answerable given the data available and come to statistically sound conclusions;

DAS5 Document and describe the data architecture and structures using appropriate data modelling tools, and select appropriate methods to present data and results that support human understanding of complex data sets;

Understand, discuss and be able to critically evaluate the issues involved in sourcing, preparing and making available data for analysis (Data harvesting).

Systematically understand the concepts that underpin data mining and be able to apply relevant tools and techniques to solve complex problems.

Critically evaluate data mining approaches in the context of Business data and be able to select the most appropriate tools and techniques for analysis of complex issues.

Critically identify and discuss appropriate strategies for modelling and analysing business data.

All teaching sessions will blend theory and practical learning. You will be introduced to curriculum concepts and ideas and will then be able to apply theory to practical examples.

You will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative (mock or practice) exercises which will help build your knowledge and confidence in preparation for summative (formal) assessment.

The module will be delivered through a Blended learning Approach, with a Module Launch, Guided Learning, Independent Learning and Individual Reviews:

Module Launch – 9 hours

There will be a module launch with 9 hours face to face contact which will provide details of the modules purpose, content and approach.

Guided Learning – 16 hours

There are a number of approached that may be used:

Weekly delivery - whilst there will be materials online there will be a series of webinars which will be content driven, these will either be 1 hour weekly or 2 hours on alternate weeks

Block delivery – a series of extended face to face sessions eg 4 x 4 hours

Independent Learning – 173 hours

You will be required to complete activities in support of developing your learning and your assessment solutions, as an apprentice some of these hours are drawn from the experience and the development of knowledge and skills in the workplace.

Individual Reviews – 2 hours

You will have 2 hours of tutorials sessions with your module tutor during the course of the module. In the main these will be individual but may be small group sessions during which your module tutor will be able to answer any queries that you have regarding module work.

Open source data analytics tools

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.

Clarke, E. (2022), Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights (All Things Data), ¿Kenneth Michael Fornari

Aspen-Taylor, S. (2022), Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy, Kogan Page; 1st edition

Theobald, A. (2022), Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy: (Introduction to Data, Data Visualization, Business Intelligence & ... Science, Python & Statistics for Beginners), Independently published¿¿

Dean J., (2014), Big Data, Data Mining and Machine Learning, John Wiley & Sons
Ahlemeyer-Stubbe A. et al, (2014), A Practical Guide to Data Mining for Business and Industry, John Wiley & Sons

Krishnan, K. (2013), Data Warehousing in the Age of Big Data, Morgan Kaufmann

Russell, M. A., (2013), Mining the Social Web Reilly, O’Reilly

Dietrich, D. et Al. , (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons

Holmes, D. E. (2017), Big Data: A Very Short Introduction (Very Short Introductions), OUP Oxford

You will explore techniques and tolls as well as the issues involved in identifying business data for analysis and modelling and will look at data mining in the context of Big Data and Business Data and will give you hands on experience of working with data mining tools and techniques.