Course Title: Case Studies in Data Science

Part A: Course Overview

Course Title: Case Studies in Data Science

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2669

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021

COSC2669

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2022,
Sem 2 2023,
Sem 2 2024

Course Coordinator: Damiano Spina

Course Coordinator Phone: +61 3 9925 2739

Course Coordinator Email: damiano.spina@rmit.edu.au

Course Coordinator Location: 014.09.016

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-requisite Courses
Successful completion of the course/s

Furthermore, you may not enrol in this course unless it is explicitly listed in your enrolment program structure.

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course. For your information go to RMIT Course Requisites webpage.

Recommended Concurrent Study 
It is recommended to undertake the following course/s at the same time as this course as it contains areas of knowledge and skills which are implemented together in practice. 

Alternatively, if you have the equivalent skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.  Please contact your course coordinator for further details.     


Course Description

Case Studies in Data Science teaches you the end-to-end process of approaching a Data Science problem, including how to pose a Data Science question for a given domain and problem; the application of Design Thinking to structure an approach; how to implement a Data Science task using teamwork and lean methodology; how to evaluate results and present them to stakeholders. All techniques will be presented in the context of ethical practice, data security, privacy and governance, and legal and regulatory constraints.

Various case study options will be offered involving different business domains (e.g., finance, health, transport, etc) and/or different types of data (e.g., structured, text, large-scale).

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.


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.


Upon completion of this course, you should be able to:

  1. Obtain practical experience through applying data science concepts and techniques learnt in courses such as Data Science Professional by performing a data science project.
  2. Develop a data science project to analyse, theorise and make conclusions about new situations in data science professional practice valuable to business and industry.
  3. Contrast social impact and professional issues in the realm of different data science domains.
  4. Analyse and evaluate professional practice case studies in teams, and critically assess the work of peers.
  5. Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations.


Overview of Learning Activities

Key concepts will be explained in pre-recorded material, classes or online, where course material will be presented and the subject matter will be illustrated with demonstrations and examples. Lectorials will be driven by data science experts from industry.

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.

 


Overview of Learning Resources

You will make extensive use of computer laboratories and relevant software provided by the University. 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, peer reviews, and a reflective portfolio.

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) presented or 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 tasks

Assessment 1: Individual Assessments
There will be two written individual assessment tasks during the semester.
Weighting 40%
This assessment supports CLOs 1, 2, 3, 5

Assessment 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, oral presentations, and peer review.
Weighting 50%
This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment 3: Reflective Portfolio
The portfolio will consist of fortnightly reflections on the learning outcomes of Assessments 1 and 2.
Weighting 10%
This assessment supports CLOs 1, 2, 3, 4, 5