Course Title: Practical Data Science with Python
Part A: Course Overview
Course Title: Practical Data Science with Python
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2670 |
City Campus |
Postgraduate |
171H School of Science |
Face-to-Face |
Sem 1 2017, Sem 1 2018, Sem 1 2019, Sem 1 2020, Sem 1 2021, Sem 2 2021 |
COSC2670 |
City Campus |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
Sem 1 2022, Sem 2 2022, Sem 1 2023, Sem 2 2023, Sem 1 2024 |
COSC2999 |
RMIT University Vietnam |
Postgraduate |
175H Computing Technologies |
Face-to-Face |
Viet3 2022, Viet3 2023 |
Flexible Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
COSC2791 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2019 (KP6) |
COSC2791 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2020 (KP4) |
COSC2791 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JanJun2021 (KP2) |
COSC2791 |
RMIT Online |
Postgraduate |
171H School of Science |
Internet |
JulDec2021 (KP6) |
COSC2791 |
RMIT Online |
Postgraduate |
175H Computing Technologies |
Internet |
JanJun2022 (KP1), JanJun2022 (KP3) |
COSC2791 |
RMIT Online |
Postgraduate |
175H Computing Technologies |
Internet |
JulDec2022 (KP5) |
COSC2791 |
RMIT Online |
Postgraduate |
175H Computing Technologies |
Internet |
JanJun2023 (KP1), JanJun2023 (KP3) |
COSC2791 |
RMIT Online |
Postgraduate |
175H Computing Technologies |
Internet |
JulDec2023 (KP5) |
COSC2791 |
RMIT Online |
Postgraduate |
175H Computing Technologies |
Internet |
JanJun2024 (KP1), JanJun2024 (KP3) |
Course Coordinator: A/Prof. Yongli Ren
Course Coordinator Phone: via email
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, modelling, 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 for MC267 Master of Data Science:
Enabling Knowledge: You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will:
- Demonstrate mastery of a body of knowledge that includes recent developments in computer science and information technology;
- Understand and use appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools;
- Recognise and use research principles and methods applicable to data science.
Critical Analysis: You will learn to accurately and objectively examine, and critically investigate computer science, information technology (IT) and statistical concepts, evidence, theories or situations, in particular to:
- Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems;
- Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements;
- Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of statistical problems.
Problem Solving: Your capability to analyse complex problems and synthesise suitable solutions will be extended as you learn to:
- Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification;
- Apply an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution
On completion of this course you should be able to:
- Use industry and evidence-based tools and approaches to transform raw data into a format suitable for a data science pipeline
- Identify scenarios where a machine learning approach may support effective data analysis
- Generate an interpretation and visualisation of data using exploratory data analysis in Python
- Construct and document an experimental methodology for analysis of data
- Select appropriate models, and apply simple machine learning tools and feature selection strategy for a defined data science problem
- 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 with the opportunity to practice 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.
RMIT Online Overview of Leaning Activities
This course uses highly structured learning activities to guide your learning and prepare you to complete the assessment tasks. These activities consist of a combination of individual, peer-supported and facilitator-guided activities, and where possible project-led, with opportunities for regular feedback.
Authentic and industry-relevant learning is critical to this course as you will be expected to critically evaluate current thinking and practice within data science applications. You will apply your thinking by producing relevant real-world assessment tasks and engage with scenarios and case studies.
You will be expected to participate in class and group activities, as well as provide and receive peer feedback on drafts of work as social learning is an important component of this course.
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.
Lists of relevant reference texts[1], resources in the library and freely accessible Internet sites will be provided within the course materials.
[1] For example: Luca Massaron, Alberto Boschetti, Python Data Science Essentials - Learn the fundamentals of Data Science with Python, Packt Publishing, 2015,ISBN: 978-1785280429.
RMIT Online Overview of Learning Resources
The learning and teaching approaches used in this program may include webinars, problem-based learning and case studies. RMIT will provide you with resources and tools for learning in this course through our online systems.
The activities and tasks are designed to facilitate the application of theory and encourage peer learning in a collaborative, open manner using online tools and interactive discussion forums. Assessment is integrated throughout the program to ensure that you graduate with a set of applicable skills and knowledge of blockchain fundamentals and applications.
There are services available to support your learning via the RMIT University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services.
RMIT Online provides support and equal opportunities for students with a disability, long-term illness and/or mental health condition and primary carers of individuals with a disability. If you need assistance, please speak to your Program Manager or contact the Equitable Learning Services (ELS).
At RMIT you can apply for credit so your previous learning or experience counts toward your RMIT Online program. For further information on how to apply for credit, please click here.
Please view the Assessment and Assessment Flexibility Policy for further information regarding applying for an extension, special consideration, equitable assessment arrangements and supplementary assessment.
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.
On campus
Assessment Task 1: Weekly Quizzes
Weighting 10%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6
Assessment Task 2: Practical Assignment (individual)
Weighting 25%
This assessment task supports CLOs 1, 3, 4, 6
Assessment Task 3: Practical and Written Assignment (individual)
Weighting 35%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6
Assessment Task 4: Practical and Written Assignment (individual)
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4, 5, 6
RMIT Online Offering
You will be assessed on how well you meet the course learning outcomes and on your development against the program learning outcomes.
Assessment Task 1: Dataset preparation report
Weighting 25%. This assessment task supports CLOs 1, 3, 4, 6
Assessment Task 2: Data modelling report
Weighting 45%. This assessment task supports CLOs 2, 3, 4, 5, 6
Assessment Task 3: Data modelling presentation
Weighting 30%. This assessment task supports CLOs 2, 3, 5
Feedback will be given on all assessment tasks.
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.
Your course assessment conforms to RMIT assessment principles, regulations, policies, procedures and instructions.