Data Analytics Consulting Project

This subject is available as a postgraduate-level MBA subject offered by the International College of Management, Sydney (ICMS). Please click the button below to find a postgraduate course suitable for you.

Subject Code:

DAT805A

Subject Type:

Specialisation 

Subject Level:

800

Pre-requisite/s:

• DAT801A Big Data Architecture and Management 
• DAT802A Persuasive Data Visualisation and Communication 
• DAT803A Advanced Data Analytics 
• Course level of study pre-requisite:  A total of 32 credit points – 8 subjects (excl WIL) prior to enrolling into the subject  

Credit Points:

4 credit points

Subject Aim:

This subject integrates the learning towards the Master of Information Technology (Big Data and Analytics), whereby students will apply their knowledge and skills acquired throughout their studies in a range of data analytics discipline areas. In this subject, students will take the role of a data analytics consultant and deliver an innovative solution to a real-life problem in line with industry standards and practices by focusing on a specific business domain.

The emphasis is fundamentally on promoting learning in the technical, interdisciplinary, and project management elements in the data analytics field. Students will align their activities based on the practices of a contemporary project management methodology, carrying out its integral phases and processes, applying the tools and techniques, and producing project artefacts.

In this subject, students apply effective communication skills by synthesising their project work into a well-structured consultancy report and showcasing their solution to an audience interactively, exhibiting professionalism. They display integrity, ethical conduct, independent self-management, and professional accountability in all aspects of their project deliverables throughout the project lifecycle.

Learning Outcomes:

a) Synthesise domain-specific knowledge and skills to solve complex problems within a data analytics context using, the latest trends in the data analytics industry.

b) Critically analyse business requirements to plan, design, and systematically deliver an interdisciplinary data analytics solution incorporating industry relevant tools and practices.

c) Implement an agile framework in delivering an innovative data analytics-driven solution for a multiplex problem in coherence with an agile project lifecycle.

d) Effectively communicate in written, visual, and verbal forms, conveying complex information to others with clarity and impact.

e) Produce outcomes as an individual with autonomy and responsibility and in collaboration with others, exhibiting expert judgement, and ethical professionalism.

Assessment Information:

Project Overview 

The Data Analytics Consulting Project will be undertaken as a group of 4-5 students, supervised by lecturers who will role-play as the client throughout the subject schedule. All students will be assigned to a project team in Weeks 1- 2 of the subject, which they will remain until the end of completion unless they present an exceptional case to change to another group. 

Students will undertake a practical project to solve a complex industry-related problem in a data analytics context that will require applying various skills and concepts gained throughout the course and based on the data analytics lifecycle. The project will reflect the multidisciplinary nature of the data analytics field by considering the characteristics, structures, processes, and techniques of each phase of the data analytics lifecycle. The project aims to develop relevant organisational strategic and operational analytics capabilities. As part of the project, students will be provided with a business case that simulates a realistic complex business problem. In addition, students receive detailed information about the organisation’s internal data analytics governance structure as well as the underlying data analytics infrastructure.  

In addition, students will be provided with a Project Execution Framework that they will procedurally follow to govern their project lifecycle. In addition, groups will implement their projects employing an agile project management framework, Scrum, using the industry-standard platform, Atlassian JIRA, to exercise real-world tools, methods, and practices in an agile project environment. 

Every fortnight (Weeks 5, 7, 9, and 11), groups will meet the client, who will be role-played by their lecturer, to hold their sprint planning ceremonies, gather/validate requirements, and collaboratively agree on sprint scope and deliverables. These fortnightly sessions will also allow students to receive timely feedback on their progress and seek advice against their challenges where required. Students are required to review the scholarly and industry literature to understand the domain-specific experiences, which will enable them to build an evidence base for their solutions to their chosen real-world challenge. This will also allow them to explore what best practice may look like in a professional setting, which they will incorporate into their solution in alignment with the dynamics of the project business domain.

Learning outcomes for this subject are assessed using a range of assessment tasks as described in the table below.

Supplementary assessment is not available in this subject.

Broad topics to be covered: 

Week 1: Introduction and Data Analytics Professional Knowledge

Topics Covered in Class:

• Professionalism, ethics, responsibility, and accountability as a Data Analytics Consultant
• ACS values, ACS Code of Ethics, and ACS Code of Professional Conduct
• Societal and legal aspects of data analytics projects
• Developing a data analytics lifecycle strategy
• Evaluation of IT, data, and analytics maturity
• Data analytics resource planning

(In Class: Group Project Work Initiation)

• Project groupings
• Problem identification
• Overview of the Data Analytics Consulting Project template

Week 2 – 3: Communication and Proposal & Pitch Preparation

Topics Covered in Class:

• Developing the data analytics use case (business problem, data, and model understanding)
• Scoping and writing the data analytics project proposal
• Persuasive communication, negotiation, and conflict resolution
• Stakeholder management
• Pitching as a Data Analytics Consultant

(At Home – Group Project Work)

• Writing the proposal
• Readying the proposal pitch

Week 4: Project Governance and Proposal & Pitch Finalisation

Topics Covered in Class:

• Project governance and execution framework
• Scrum events and artifacts
• Risk management in Scrum

(In Class - Group Project Work)

• Proposal milestone:
  • Proposal submissions
  • Proposal pitches
  • Proposal approvals

Week 5: Teamwork Concepts and Issues & Scrum Ceremony 1

Topics Covered in Class:

• Working as a Scrum Team:
• interpersonal skills in an agile project environment
• teamwork concepts, effective collaboration, and group dynamics
• Cultural and social awareness
• Emotional and social intelligence
• Professional conduct in a team setting
• Leading, coaching, and managing a Scrum team

(In Class – Client Engagement) Sprint 1 Planning with the Client:

• Developing a data sourcing, quality, and management strategy
• Scoping, preliminary planning, and the initial product roadmap
• requirements, epics, user stories, and acceptance criteria
• release planning for Go-Live
• sprint planning and estimation

(At Home – Group Project Work) Sprint 1 Execution:

• Implementing the data sourcing, quality, and management strategy
• manage, monitor, and control sprint backlog as per the agreed Sprint 1 scope

Week 6: Abstract Modelling & Scrum in Action (Sprint 1)

Topics Covered in Class:

• Developing the data analysis strategy
• Qualitative data analysis methods
• Quantitative data analysis methods
• Analytics software solutions
• User journey mapping

(At Home- Group Project Work) Sprint 1 Execution continues

Week 7: Quality Management & Scrum Ceremony 2

Topics Covered in Class:

• Quality assurance and management in a Scrum environment

(In-Class- Client Engagement) Sprint 2 Planning with the Client:

• Sprint 1 review and retrospective
• product backlog refinement
• requirements, epics, user stories, and acceptance criteria
• Sprint 2 planning and estimation

(At Home- Group Project Work) Sprint 2 Execution:

• Data analysis execution
• maintain project artefacts
• manage, monitor, and control sprint backlog as per the agreed Sprint 2 scope

Week 8: Checkpoint & Scrum in Action (Sprint 2)

• Q&A and pulse-check with groups
• (Group Project Work) Sprint 2 Execution continues

Week 9: Project Report & Scrum Ceremony 3

Topics covered in class:

• Data analytics consulting report style and format
• Structuring and writing a data analytics consulting report
• Polishing and finalising a data analytics consulting report

(In Class- Client Engagement) Sprint 3 Planning with the Client:

• Sprint 2 review and retrospective
• solution backlog refinement
• requirements, epics, user stories, and acceptance criteria
• Sprint 3 planning and estimation

(At Home: Group Project Work) Sprint 3 Execution:

• Model evaluation and interpretation
• maintain project artefacts
• manage, monitor, and control sprint backlog as per the agreed Sprint 3 scope

Week 10: Change Management & Scrum in Action (Sprint 3)

Topics covered in class:

• Change management for data analytics consulting projects
• Business readiness
• Transition to business-as-usual and operational handover
• Q&A and pulse-check with groups

(At Home – Group Project Work)

• Sprint 3 Execution continues
• initiate project closure activities and artifacts

Week 11: Presentation Rehearsals & Scrum Ceremony 4

Topics covered in class:

• Persuasive data presentation tools and techniques
• Showcasing with impact

(In Class- Client Engagement)

• Sprint 3 review and retrospective
• finalising product backlog
• release closure for Go-Live
• validating solution

(At Home- Group Project Work)

• wrap up project closure activities and artifacts
• prepare for showcasing

Week 12: Closure

• Final project reporting
• Showcasing

Please note that these topics are often refined and subject to change so for up to date weekly topics and suggested reading resources, please refer to the Moodle subject page.