Business analytics involves understanding the data-driven activities of a business to draw inferences to make calculated decisions with higher certainty. Business analytics encompasses a gamut of analysis around business data to draw information that could be used by the managers at various levels in an organisation. It enables fact-based decision making while extending accountability in decision making. For the purposes of this module, business analytics is defined as the process of extracting, transforming, and loading data in order to summarise it to extract information. This module introduces the learner to tools and methods for conducting business analytics.

1. Manage, explore and cleanse business data for analytics
2. Execute model-driven and data-driven analytics
3. Generate reports and visualisations
4. Apply data mining methods
5. Develop analytics solutions for finance and marketing functional problems

This module is designed as an introduction to Database Design and Development techniques.

1. Design a relational database schema for a software application
2. Devise a set of relational tables and develop a relational database.
3. Query a relational database using SQL
4. Design an Entity Relationship Diagram (ERD) and map itto a relational schema.

5. Evaluate the use of non-relational data storage technologies

This module will provide an introduction to solving problems using programming languages and automating computer tasks using scripting languages with a focus on the business context.

1. Execute computer tasks using a scripting language.

2. Set-up and configure a software development environment and toolchain.

3. Develop an algorithm to solve a businessproblem.

4. Write a computer program in a high-level programming language.
5. Construct a complex computer program from a series of simpler computer programs.

This module is intended to provide the student with an introduction to statistical and analytical techniques which underpin business analytics.

1. Demonstrate an understanding of the foundations for statistical techniques

2. Select, evaluate and apply appropriate descriptive statistical techniques

3. Demonstrate an understanding of the probability foundations of statistics and analytics

4. Select and apply appropriate techniques and procedures in inferential statistics

5. Design and evaluate quantitative forecasts and predictions for business problems

6. Evaluate and integrate statistical approaches for solving problems in business analytics

Systems Analysis is a structured process that allows for the development of business information systems. This involves a number of activities that include requirements gathering, data modelling, process description and design; prototyping and systems design. Techniques include data flow diagrams, entity relationship modelling, normalization and decision modelling.

1. Conduct analysis using systems analysis methodologies.

2. Complete Requirements Analysis (Requirements Catalogue / Specification).

3. Create Data Flow (Data Flow Diagrams).

4. Complete Entity Analysis and construct Entity Relationship Diagrams.

5. Prepare and executeObject-Oriented Analysis (Unified Modelling Language).

This module provides for the study of Databases that are appropriate for the modelling, design and operation of a Big Data environment. NoSQL databases are examined in detail; including concepts,architectures, patterns and availability.

1. Discuss how NoSQL data models can be employed for in conjunction BIG Data.

2. Construct and demonstrate competence with Key Value, Graph, Column Family and Document stores.

3. Compare and contrast transaction management using BASE and ACID approaches.

4. Apply replication and sharding approaches to appropriate data models.

5. Demonstrate how polyglot persistence can be achieved in a NoSQL environment.

The objective of this module is to examine how different decision theories, decision tools and data analytical and data visualisation approaches can improve the performance of employees & organisations, and to decide the types of business problems that these theories, tools and approaches can best address.

1. Critically evaluate the role of decision theory in enhancing employee and organisational performance

2. Evaluate different decision-making methods, tools, visualisations and interactive dashboards

3. Contrast the different data analytical, data visualisation tools and methods used by organisations

4. Critically evaluate different methods for managing risk and uncertaintyin decision making.

5. Appraise how digital transformation can impact decision making and analysis

This module will provide the students with the necessary skills to understand design and develop an Information System to specification. The applied techniques of the development lifecycle are demonstrated and practised on the sample project. The approaches and techniques of systems analysis life cycles are also completed, culminating in a developed Information System.

1. Explore and examine multi-method structured, object-oriented, and systems development methods.

2. Examine and understand Project Management tools and methodologies used in systems development

3. Plan and develop appropriate user interfaces.

4. Examine ethical, governance and privacy considerations in the design of systems,

5. Create an Information System as per a structured development methodology.

Students learn to apply analytics tools and business intelligence methodologies to build solutions to decision-making problems.

1. Demonstrate anunderstanding ofdata warehousing tools and technologies in a decision support context.

2. Apply Business Intelligence (BI) project management techniques

3. Analyse business problems and create solutions using Business Intelligence approaches and methodologies

4. Analyse and manipulate data using data mining techniques and tools

5. Evaluate visualisations, data transformations and predictive models in a business context

Upon completion of the module, the student will understand the transition from a traditional enterprise in-house environment to a Cloud based enterprise environment. This involves an examination of concepts such as virtualization at each layer – compute, storage, network, desktop, and application – along with business continuity in a Cloud environment. The student will understand Cloud computing fundamentals, infrastructure components, service management activities, security concerns, and considerations for Cloud adoption.

Current developments with respect to IS technologies and their impact on business models will also be examined; the student will have a knowledge of significant new technology approaches.

1. Understand and evaluate the tradtional Enterprise Infrastructure.
2. Identify and implement a Virtualized Storage solution.
3. Design and develop virtualization technology of compute, storage, network, desktop and application layers of IT infrastructure.
4. Describe and analyse business continuity solutions in a virtual data centre
5. Describe and analyse the key considerations for migration to the Cloud
6. Describe the emerging technology enviornment for Information Systems.

The professional practice project component is an integral part of the academic programme. This module enables the learner to develop functioning knowledge and experience by participating and reflecting on the process of defining, developing, implementing, reporting, and presenting a business/societal related analytical project. Learners currently in employment are encouraged to undertake a mutually beneficial project based on a work-based scenario/problem/industry/dataset. Learners not in a position to apply the project to a real-world scenario are encouraged to identify a societal-related analytical project based on publicly available datasets.

While the project is learner lead, each learner will be assigned a dedicated academic supervisor to facilitate and mentor for the implementation stage of the project. The project must be of sufficient technical challenge and depth, as agreed by an academic supervisor.

Appropriate ethical compliance safeguards will put in place in terms of data collection and data use. Data protection and GDPR guidelines will be strictly adhered to and communicated to learners who are using datasets obtained from their workplace. All considerations for the handling of data will be guided and governed by ATU's intellectual property policy and procedures.

A project portfolio will be submitted by each student at the end of the module, while students will also be required to present and discuss their project in the form of a presentation with particular emphasis placed on the communication of findings and results.

On completion of this module, the student should be able to take on (minimally) entry-level development and/or analyst roles relating to business analytics.

1. Analyse a business analytics issue and identify a solution.

2. Examine and critique topics relevant to the businessanalytics issue.

3. Apply business analytical skills in the development of a solution.

4. Demonstrateproject management techniques to the completion of the project.

5. Prepare a report and presentation on the project solution and/or prepare user documentation for the client.