24754 Studio 2: Specialisation

Java Python SUBJECT OUTLINE

24754 Studio 2: Specialisation

Spring 2024

Subject description

The Specialisation Studio will allow students to apply their analytics skills, to an area of analytics, and take students through all the steps required in a business analytics project. In this studio, student teams will be provided with some real-world tasks and problems, from a range of industries (e.g. Finance, Marketing and Health). Students will choose an area that interests them and will develop a solution that effectively communicates the results.

Subject learning objectives (SLOs)

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

1. apply multiple data analytic techniques to data in order to convey recommendations that are actionable in a local

and international setting

2. analyse assumptions of data analytics techniques that can affect the representation of cultural groups

3. evaluate data analysis techniques for decision making

4. assess the data analysis process to ensure that it is performed professionally and ethically and takes into account indigenous values

5. apply advanced data analytic techniques to business problems to extract meaningful insights from complex contexts

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the following program learning objectives:

Research, identify and evaluate the assumptions implicit in data and apply analytical techniques to facilitate business decision making (1.1)

Critically evaluate and apply professional ethical standards, the principles of sustainability, social responsibility, and Indigenous values as business analysts (3.1)

Integrate advanced data analysis techniques with business practices to generate actionable knowledge to inform. and facilitate effective decision-making in local and international contexts (4.1)

Contribution to the development of graduate attributes

This studio is designed to help students integrate and apply knowledge from their previous subject’s data analytics and present results. The students will write a report based on the findings that convey key recommendations and extract insights from data. Students will be expected to discern and evaluate what are the appropriate analytical techniques to be used with different types of data.

Students’ understandings of issues regarding representation of cultural groups in data analytics will be developed, and ethical issues in data analytics will be discussed.

This subject is aligned with the following graduate attributes:

Intellectual rigour and innovative problem solving

Social responsibility and cultural awareness

Professional and technical competence

Teaching and learning strategies

This subject synthesises knowledge acquired in different subjects from the Business Analytics course.

Students will also use appropriate statistical software such as R and SAS, to complete assigned tasks. The project will be an authentic assessment using problems from industry partners.

The subject is taught by academic staff as well as industry professionals in an interactive setting. Both academic and industry staff will help guide and facilitate discussions in class as well as using webinar technologies.

Students are required to watch assigned videos and read specified articles and case study material in preparation for each class. These learning materials will be available on the subject webpages on the learning management system.

Students will be involved in active learning experiences during class. These experiences will be completed in collaboration with other students and will provide students with the opportunity to obtain in-class peer feedback on their learning as well as direct feedback from their lecturer. Additionally, feedback will be given to students online via video conferencing (e.g. Skype, Zoom) during times specified by the lecturer.

An aim of this subject is to help you develop academic and professional language and communication skills to succeed at university and in the workplace. During the course of this subject, you will complete a milestone assessment task that will, in addition to assessing your subject-specific learning objectives, assess your English language proficiency.

Content (topics)

Using software to analyse data

Effective report writing

Problem Solving

Critical Thinking

Determining Key Insights

Teamwork Skills

Assessment

Assessment task 1: Project Report and Analysis

Objective(s): This addresses subject learning objective(s): 1, 3 and 5

This addresses program learning objectives(s): 4.1

Groupwork: Group, individually assessed Weight: 65%

< 24754 Studio 2: Specialisation p > Task: The project report has an individual (35%) and a group component (30%). The group portion of the

report will demonstrate that a student can work together with others in compiling and analysing data.  The individual portion of the report will demonstrate that a student can individually complete the tasks necessary in an analytics report that would be viable in an industry setting.

The further details and information will be available on Canvas.

Assessment task 2: Reflective Essay

Objective(s): This addresses subject learning objective(s): 2, 3 and 4

This addresses program learning objectives(s):

1.1 and 3.1 Groupwork: Individual Weight: 35%

Task: This essay will demonstrate that the students understand how their project will benefit an

organization. The essay must show that the students have understood the ethical implications of their project and discuss the potential positive and negative impacts on some groups.

This task includes a milestone assessment component that evaluates English language proficiency. You may be guided to further language support after the completion of this subject if your results in  this milestone task indicate you need more help with your language skills.

The further details and information will be available on Canvas.

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

References

Camm, Jeffrey D., Cochran, James J., Fry, Micahel J., and Ohlmann, Jeffrey W., Business Analytics - 5th Edition, Cengage.

Assessment: faculty procedures and advice

Extensions and late assessment protocol

Any assessment task (excluding take-home final exams, frequent assessment tasks that must be submitted on a

regular basis prior to or during a synchronous class such as weekly homework preparation, pre-class or in-class

quizzes or single assessments that are performed and assessed during class time such as presentations) submitted after the due date and time, will be either:

. penalised by way of loss of marks where 10 per cent (10%) of the marks for the assessment task will be deducted per day for assessment tasks submitted after the due date. A day is defined as a 24-hour period or part thereof

following the published due date and time of the assignment, and

. will be rejected and not marked if the assessment is submitted more than five (5) calendar days for subjects offered in the Main Calendar (Autumn, Spring and Summer) and seven (7) calendar days for subjects offered in the UTS Online (Sessions 1 to 6), after the stated submission date, unless a formal short-term extension has been granted  by the Subject Coordinator, or

. rejected without marking (where the subject outline states that this will be the consequence of an assessment task being submitted after the due time on the due date without an approved extension)

The maximum penalty that can be applied is 50% of the assessment marks. Students cannot receive a negative score for the assessment task after a penalty is levied.

A penalty for late work will not apply in cases of approved extensions by the Subject Coordinator. Approved

extensions cannot be made without a request for extension sent directly to the coordinator (or nominated approving

tutor as designated by the subject coordinator) for short-term extensions (within the timeframe set out above) or via an application for special consideration (within the UTS time frames for submission). Any direct requests must be

received by the Subject Coordinator at least 24 hours prior to the due date and time.

Students enrolled in UTS Online courses must complete the Request for Short Extension without Academic Penalty form. to apply for a short-term extension.

A penalty may not apply after due consideration of any submission (request for extension or application for special  consideration) by the Academic Liaison Officer (ALO), on behalf of students registered with Accessibility Services