Course Title: Practical Data Science
Part A: Course Overview
Course Title: Practical Data Science
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2738 |
City Campus |
Undergraduate |
171H School of Science |
Face-to-Face |
Sem 1 2018, Sem 1 2019, Sem 1 2020, Sem 1 2021, Sem 2 2021 |
COSC2738 |
City Campus |
Undergraduate |
175H Computing Technologies |
Face-to-Face |
Sem 1 2022, Sem 2 2022, Sem 1 2023, Sem 2 2023, Sem 1 2024, Sem 2 2024 |
COSC2789 |
RMIT University Vietnam |
Undergraduate |
171H School of Science |
Face-to-Face |
Viet2 2019, Viet3 2020, Viet3 2021 |
COSC2789 |
RMIT University Vietnam |
Undergraduate |
175H Computing Technologies |
Face-to-Face |
Viet3 2022, Viet3 2023, Viet3 2024 |
Course Coordinator: Professor Yongli Ren
Course Coordinator Phone: -
Course Coordinator Email: yongli.ren@rmit.edu.au
Course Coordinator Availability: By appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
The course gives you a set of practical skills for handling data that comes in a variety of formats and sizes, such as texts, spatial and time series data. These skills cover the data analysis lifecycle from initial access and acquisition, modeling, transformation, integration, querying, application of statistical learning and data mining methods, and presentation of results. This includes data wrangling, the process of converting raw data into a more useful form that can be subsequently analysed. The course is hands-on, using python.
Objectives/Learning Outcomes/Capability Development
This course contributes to the following Program Learning Outcomes (PLOs):
PLO1: Apply a broad and coherent set of knowledge and skills for developing data driven solutions for contemporary societal challenges.
PLO2: Apply systematic problem solving and decision making methodologies to identify, design and implement data driven solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
On completion of this course, you will be able to:
- Use industry and evidence-based tools and approaches to transform raw data into a format suitable for a data science pipeline;
2. Identify scenarios where a machine learning approach may support effective data analysis;
3. Generate an interpretation and visualisation of data using exploratory data analysis in Python;
4. Construct and document an experimental methodology for analysis of data;
5. Select appropriate models, and apply simple machine learning tools and feature selection strategy for a defined data science problem;
6. Apply professional standards to allow reproducibility of analysis.
Overview of Learning Activities
You will learn about key concepts in Pre-recorded lecture videos, where you can engage with course material and the subject matter being illustrated through 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. 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.
This course includes 2 hours per week of lectures and 2 hours per week of tutorial/laboratory classes. To achieve high levels of academic results you are expected to spend on average an additional 6 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 RMIT University. You will be able to access course information and learning materials through myRMIT.
Overview of Assessment
The assessment for this course comprises practical, written, and presentation assignments, including data pre-processing, data analysis and data modelling. The assessment tasks involve the processing and analysis of various types of datasets, and the applications of various machine learning models. While this course will use machine learning tools, the focus of the assessment is on analysis, application and problem solving. Across all assessment tasks, students are required to demonstrate their knowledge of theoretical concepts and practical techniques, including identifying the appropriate techniques and applying them to new situations.
This course has no hurdle requirements.
Assessment Task 1: Weekly Quizzes -- 10%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6
Assessment Task 2: Practical Assignment (individual) -- 25%
This assessment task supports CLOs 1, 3, 4, 6
Assessment Task 3: Practical and Written Assignment (individual) -- 35%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6
Assessment Task 4: Practical and Written Assignment (individual) -- 30%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6