Course Title: Case Studies in Data Science
Part A: Course Overview
Course Title: Case Studies in Data Science
Credit Points: 12.00
171H School of Science
|Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020
Course Coordinator: Associate Professor Lawrence Cavedon
Course Coordinator Phone: +61 3 9925 2325
Course Coordinator Email: firstname.lastname@example.org
Course Coordinator Location: 14.8.9
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
Enforced Pre-requisite: Successful completion of the course: COSC2670 Practical Data Science.
Furthermore, you may not enrol in this course unless it is explicitly listed in your enrolment program structure.
In this course you will discuss issues of privacy, surveillance, security, classification, discrimination and decisional autonomy from a legal, ethical, and policy perspective (whether business or public policy). Areas of relevance include health, marketing, employment, law enforcement, and education are explored in detail.
This course will prepare you for undertaking the Data Science Postgraduate Project (COSC2667) and so includes a work integrated learning experience in which your knowledge and skills will be applied and assessed in a simulated workplace context where feedback from data scientists working in industry is integral to your experience.
This course will be taught by a sessional staff member with industry experience and will also include a session from library liaison staff.
Objectives/Learning Outcomes/Capability Development
Program Learning Outcomes
This course contributes to the following Program Learning Outcomes for MC267 Master of Data Science:
Communication: You will learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to:
- Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.
Team Work: You will learn to work as an effective and productive team member in a range of professional and social situations, in particular to:
- Work effectively in different roles, to form, manage, and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances, and differing levels of technical expertise.
Responsibility: You will be required to accept responsibility for your own learning and make informed decisions about judging and adopting appropriate behaviour in professional and social situations. This includes accepting the responsibility for independent life-long learning and a high level of accountability. Specifically, you will learn to:
- Effectively apply relevant standards, ethical considerations, and an understanding of legal and privacy issues to designing software applications and IT systems;
- Contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions;
- Reflect on experience and improve your own future practice;
- Locate and use data and information and evaluate its quality with respect to its authority and relevance.
Research and Scholarship: You will have technical and communication skills to design, evaluate, implement, analyse and theorise about developments that contribute to professional practice or scholarship, specifically you will have cognitive skills to:
- Demonstrate mastery of theoretical knowledge and to reflect critically on theory and professional practice or scholarship;
- Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship.
Course Learning Outcomes
On completion of this course you should be able to:
- Apply relevant standards, ethical and social considerations, and thus demonstrate an understanding of legal issues to the professional practice of data science
- Analyse and discuss social impact and professional issues in the realm of data science, in particular evaluate solutions to privacy threats within the context of data science practice in industry
- Analyse and evaluate professional practice case studies in teams, and critically assess the work of peers
- Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations
- Use research principles and choose appropriate methods to analyse, theorise and justify conclusions about new situations in data science professional practice and/or research
Overview of Learning Activities
Key concepts will be explained in lectures, classes or online, where course material will be presented and the subject matter will be illustrated with demonstrations and examples.
Tutorials, workshops and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide you practice in the application of theory and procedures. You will explore the concepts with teaching staff and other students, and receive feedback on your progress from data scientists working in industry. You will develop an integrated understanding of the subject matter through private study by working through the course as presented in classes. Comprehensive learning materials will aid you in gaining practice at solving conceptual and technical problems.
Teacher-guided activities (24 hours): this course includes 1 hour per week of lectures and 1 hour per week of tutorial classes.
Student-directed activities (96 hours): you are expected to spend up to an additional 8 hours per week on self-directed independent learning (reading, online activities and assignments).
Overview of Learning Resources
You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through myRMIT and may be provided with copies of additional materials in class or via email.
Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.
Overview of Assessment
This course has no hurdle requirements.
The assessment for this course comprises individual and group assignments, presentations and peer reviews, tests and a formal examination.
Each of the assessment items requires you to demonstrate your knowledge of theoretical concepts, including application to new industry relevant situations. Additionally, the assignments require you to research a given scenario/problem, analyse it based on a professional framework, evaluate solutions, apply relevant standards, and create a professional report. Peer review will involve written feedback on the report(s) written by other teams. The weekly assessments may involve analysis of case studies, group discussion with a moderator who gives feedback on communication dynamics, debates, role-play scenarios and short tests.
Assessment 1: Fortnightly assessments: There will be five small written individual assessment tasks during the semester.
This assessment task supports CLOs 1,2,4
Assessment Task 2: Work integrated learning project: This project will be undertaken in teams and involve applying relevant standards, ethical and social considerations to the analysis of a scenario of data science practice in industry, and communicating effectively through written technical reports and oral presentations.
This assessment task supports CLOs 1,2,3,4,5
Assessment 3: Exam
This assessment supports CLOs 1,2,5