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

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.

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


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 is an option course so it is not required to contribute to the development of program learning outcomes (PLOs) though it may assist your achievement of several PLOs.

For more information on the program learning outcomes for your program, please see the program guide.


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

Note: This course has no hurdle requirements.

Assessment tasks

Assessment 1: Individual Assessments
Weighting 40%
This assessment supports CLOs 1, 2, 3, 5

Assessment 2: Work Integrated Learning Project
Weighting 50%
This assessment task supports CLOs 1, 2, 3, 4, 5

Assessment 3: Reflective Portfolio
Weighting 10%
This assessment supports CLOs 1, 2, 3, 4, 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.