Course Title: The Data Science Professional
Part A: Course Overview
Course Title: The Data Science Professional
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2818 |
City Campus |
Undergraduate |
175H Computing Technologies |
Face-to-Face |
Sem 1 2023, Sem 1 2024, Sem 1 2025 |
Course Coordinator: Professor Feng Xia
Course Coordinator Phone: -
Course Coordinator Email: feng.xia@rmit.edu.au
Course Coordinator Location: -
Course Coordinator Availability: by appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
The role of a Data Scientist is an increasingly ubiquitous job role in many organisations of varying size. As well as a variety of technical skills, the practicing Data Scientist also requires knowledge and awareness of key ethical, privacy and governance considerations for managing data, and data security and related techniques for implementing such. Moreover, a Data Scientist in an organisation must be an effective communicator both with other business units and with management, displaying an ability to both understand business needs and to communicate analyses effectively to inform decision making. This course will: introduce concepts related to legal, ethical, privacy, governance issues for data collections; describe techniques for effectively managing such issues; provide you with techniques for effective communication and other considerations required of the Data Scientist in a professional organisation.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes for BP340 Bachelor of Data Science:
PLO1: Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.
PLO2: Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
PLO4: Communication - Communicate effectively with diverse audiences, employing a range of communication methods in interactions to both computing and non-computing personnel.
PLO6: Responsibility and Accountability - Demonstrate integrity, ethical conduct, sustainable and culturally inclusive professional standards, including First Nations knowledges and input in designing and implementing computing solutions.
On completion of this course you should be able to:
- Key concepts will be explained in lectures, classes or online where syllabus material will also be presented and the subject matter will be illustrated with demonstrations, examples and case studies;
- Tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide practice in the application of theory and procedures, allow exploration of concepts with teaching staff and peers, and give feedback on your progress and understanding;
- Assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and
- Private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.
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.
Total study hours A total of 120 hours of study is expected during this course, comprising:
Teacher-directed hours (48 hours): lectures, tutorials and laboratory sessions. Each week there will be 2 hours of lectures and 2 hours of tutorial work. You are encouraged to participate during lectures through asking questions, commenting on the lecture material based on your own experiences and through presenting solutions to written exercises. The sessions will introduce you to the tools necessary to undertake the assignment work.
Student-directed hours (72 hours): You are expected to be self-directed, studying independently outside class.
Overview of Learning Resources
The course is supported by the Canvas learning management system which provides specific learning resources. See also the RMIT Library Guide at http://rmit.libguides.com/compsci
You will make 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. Use the RMIT Bookshop’s textbook list search page to find any recommended textbook(s).
Overview of Assessment
Assessment Task 1: Data Science case study (group)
Weighting: 20%
This assessment task supports CLOs 1,2,3,4,5
Assessment Task 2: Data Science for social good project (group)
Weighting: 45%
This assessment task supports CLOs 1, 2, 3, 4, 5
Assessment Task 3: Interviews and reflections (individual)
Weighting: 30%
This assessment task supports CLOs 1, 2, 3, 4, 5
Assessment Task 4: Micro-Credentials
Weighting: 5%
This assessment task supports CLOs 2, 4