Program Overview

Overview

The OLET1618: Data Science in Astronomy: Algorithms unit is an intensive program that explores the challenges of working with large datasets in astronomy. It focuses on practical skills, with all activities conducted in Python 3, a modern programming language widely used in astronomy. Students should have strong programming skills in Python 3, with a good understanding of loops, decisions, and user-defined functions.

Unit Details and Rules

• Academic unit: Physics Academic Operations
• Credit points: 2
• Prerequisites: None
• Corequisites: None
• Prohibitions: None
• Assumed knowledge: Strong programming skills in Python 3, with a good understanding of loops, decisions, and user-defined functions.
• Available to study abroad and exchange students: Yes

Teaching Staff

• Coordinator: Nicholas Scott

Assessment

The assessment for this unit includes:

• Assignment: Quiz 1, an online quiz in Canvas, worth 5% of the total mark, due in Week 03.
• Assignment: Topic 1 tutorials, complete online coding questions, worth 7.5% of the total mark, due in Week 03.
• Assignment: Topic 2 tutorials, complete online coding questions, worth 7.5% of the total mark, due in Week 04.
• Assignment: Quiz 2, an online quiz in Canvas, worth 5% of the total mark, due in Week 05.
• Assignment: Topic 3 tutorials, complete online coding questions, worth 7.5% of the total mark, due in Week 05.
• Assignment: Topic 4 tutorials, complete online coding questions, worth 7.5% of the total mark, due in Week 06.
• Online task: In-class test, completed online, worth 60% of the total mark, due in Week 07.

Assessment Summary

• Quizzes: Designed to solidify understanding of the current module, with a requirement to score at least 3/5 to pass.
• Tutorial assignments: Test knowledge learned from the current module, requiring the writing of working Python scripts.
• In-class test: Tests the ability to write Python scripts, theory knowledge, and problem-solving skills.

Learning Support

• Simple extensions: Available for up to five calendar days for submitting work, with the application process varying depending on the assessment type.
• Special consideration: For exceptional circumstances that prevent the completion of an assessment, or for essential commitments impacting performance.

Weekly Schedule

The unit is structured over several weeks, with each week focusing on different topics and learning activities:

• Week 01: Thinking about data (1), with online classes and tutorials.
• Week 02: Thinking about data (2), with online classes and tutorials.
• Week 03: Big data makes things slow (1), with online classes and tutorials.
• Week 04: Big data makes things slow (2), with online classes and tutorials.

Learning Outcomes

Upon completion of this unit, students should be able to:

• LO1: Demonstrate an understanding of some astronomical phenomena.
• LO2: Understand the type of problems that may arise when dealing with big data.
• LO3: Discuss ways to improve computational solutions to data analysis problems.
• LO4: Write short programs (in Python) to analyze datasets.

Graduate Qualities

The unit contributes to the development of several graduate qualities, including:

• GQ1: Depth of disciplinary expertise.
• GQ2: Critical thinking and problem-solving.
• GQ3: Oral and written communication.
• GQ4: Information and digital literacy.
• GQ5: Inventiveness.
• GQ6: Cultural competence.
• GQ7: Interdisciplinary effectiveness.
• GQ8: Integrated professional, ethical, and personal identity.
• GQ9: Influence.

Additional Information

• Equity, Access, and Diversity statement: The School of Physics is committed to creating a supportive and inclusive environment, recognizing the impact of biases, bullying, and discrimination.
• Work, Health, and Safety: The University is governed by the Work Health and Safety Act 2011 and Regulation 2011, with responsibilities and expectations outlined for workers and others.
• Disclaimer: The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrollment numbers.