This is an industry based research case study unit suitable for the Master of Data Science (MDS) that will give the students an end to end experience of completing an advanced data analytics project through a case study experience. For introduction in Semester 1, 2018.

09/02/2018 - Admin:

• updating prerequisites on behalf of the CE as discussed with the Course Director and ADLT, for students commencing in 2018.
• added Chief Examiner and removed co-requisites on behalf of the CE as discussed with the Course Director and ADLT, for students commencing in 2018.
• adding Reasons for Change - CE updated the unit synopsis to clarify what is covered in this unit.

13/02/2018 - Mark Carman:

• updated Learning Outcomes to not refer to "research" but "data analytics".

19/11/2020: Admin - this unit was disestablished at FEC 5/20 on 12/11/2020 as it is no longer required.

Upon successful completion of this unit students should be able to:

1. critically analyse a business problem and translate it into a data science problem via interrogation of business stakeholders, with the purpose of ascertaining the underlying business realities and validity of the translation;
2. determine, in conjunction with business stakeholders, agreed methods of evaluation of solutions to-be-derived for the business problem, and critically assess them for their quality, practicality and difficulty of implementation;
3. critically evaluate available data for its sufficiency for the investigation of a given data science problem, and supplement it with external data where appropriate;
4. apply, in an industry standard setting, studied skills of data wrangling, data integration, imputation, model building, validation and optimisation, selecting the most appropriate tool for every situation, in order to derive a satisfactory solution to the business problem;
5. demonstrate discernment and judgement in effective two-way communication to all stakeholders/audiences, including for communicating the final problem solution, recommendations for its implementation, its projected impact on the business, and any insights derived as part of the project;
6. operate effectively as a member of a data analytics team, such as by effective intra-team communication, task assignment, progress communication and overall goal alignment.

029999

This unit gives students the opportunity to work in teams in order to acquire hands on experience in advanced data analytics, develop new skills, and apply the knowledge and skills they have already gained in a practical setting.

Teams will be given real or simulated data sets pertaining to real-world problems, and will apply advanced data analytics techniques in order to develop models over the data and to derive from these meaningful insights. Teams will be self-managed and will perform work by use of standard collaboration tools.

Throughout this process students will need to communicate findings, knowledge and ideas effectively and professionally to a range of stakeholders. The stakeholders will include academics, peers, project-based stakeholders and industry experts. The students will use these communicated findings in order to direct and further their research throughout the process, as well as to divide work in ways that will allow each student to carry out research individually.

Students will be ranked both on the performance of their teams (the quality of the insights and the models, and the ability to visualise and communicate findings to stakeholders) and on their individual performance (professionalism, commitment and collegiality of joint work, quality of research undertaken).

On-campus

Industry Mentors will visit campus to give industry relevant feedback to students working on the case study data.

Each lab will have a lead mentor and an advanced data analytics tutor who can support student learning and problem solving.

In-semester assessment: 100%

This unit provides students with an end to end experience of an Advanced Data Analytics Case Study Project and so is best evaluated via in-semester assessment with the production of Milestone artefacts (progress reports and presentations) as well as a final curated portfolio of the artefacts developed within the unit that demonstrate the achievement of the unit learning outcomes against expected performance criteria.

Minimum total expected workload equals 12 hours per week comprising:

• One weekly 1-hour meeting with academic and/or industry project stakeholders.
• One 3-hour workshop/meeting every week, including 3 meetings with relevant stakeholders for communication of (interim) findings and review.
• a minimum of 8 hours independent research per week, for completion of case study analysis work individually and together with other team members.

FIT

FIT5147, FIT5149 and FIT5201

Students must be in their final semester of study (have less than or equal to 24 points of study to complete)

Handbook - Students who commenced prior to 2018 need to see the Course Director.