Course Title: Case Studies in Data Science 1

Part A: Course Overview

Course Title: Case Studies in Data Science 1

Credit Points: 12.00


Course Coordinator: Minyi Li

Course Coordinator Phone: +61 3 9925 8991

Course Coordinator Email: minyi.li@rmit.edu.au

Course Coordinator Location: 014.08.008-2

Course Coordinator Availability: by appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

COSC2738 - Practical Data Science

COSC2815 - Advanced Programming in Python

MATH2201 - Basic Statistical Methodologies


Course Description

Case Studies in Data Science 1 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 agile 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 is a designated WIL course. Any or all of these aspects of a WIL experience may be in a simulated workplace environment.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

 

This course contributes to the following Program Learning Outcomes for BP340 Bachelor of Data Science:

 

Communication (PLO4)

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 technical and non-technical personnel via technical reports of professional standard and technical presentations.

 

Team Work (PLO5)

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 and educational backgrounds and life circumstances, and differing levels of technical expertise.

 

Responsibility (PLO6)

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 managing and processing data;
  • 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.


Course Learning Outcomes (CLOs):

 

On completion of this course you should be able to achieve the following:

 

  1. Apply relevant standards, ethical and social considerations, and thus demonstrate an understanding of legal issues to the professional practice of data science
  2. 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
  3. Analyse and evaluate professional practice case studies in teams, and critically assess the work of peers
  4. Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations
  5. 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.

 

WILReady Cred for students: https://www.rmit.edu.au/students/student-essentials/work-integrated-learning/preparing-for-wil

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 and/or available for download onto private laptops/machines. You will be able to access course information and learning materials via MyRMIT/Canvas and may be provided with copies of additional materials in the library or via freely accessible internet sites.

 

Use the RMIT Bookshop’s textbook list search page to find any recommended textbook(s).


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.

 

Assessment tasks:

 

Assessment 1: Fortnightly assessments x5

Weighting 25%

This assessment task supports CLOs 1,2,4

 

Assessment Task 2: Work integrated learning project presentations.

Weighting 50%

This assessment task supports CLOs 1,2,3,4,5

 

Assessment 3: Exam

Weighting 25%

This assessment supports CLOs 1,2,5