Computational and Data Tools for Scientists introduces practical computational, statistical, and machine-learning techniques for data-intensive research. The course is designed for students across different science and engineering disciplines. It offers an applied approach to developing the skills required to work with observed data and physical systems. Python will be the computational language used throughout the course.
The course follows the process of turning a scientific question into a well-defined problem and designing an appropriate computational workflow. Topics include command-line computing, remote access to high-performance computing systems, version control, scientific programming practices, data structures, statistical inference, uncertainty estimation, Monte Carlo methods, dimensionality reduction, signal processing, regression, machine learning, and Bayesian inference.
Students will gain hands-on experience in practical problem solving using real-world scientific datasets from different domains. They will develop skills in managing, analysing, modelling, and interpreting large and complex datasets, and in assessing the strengths and limitations of different computational approaches. Emphasis is placed on critical thinking and the responsible use of statistical and machine-learning tools.
The course combines lectures, practical exercises, and regular assignments that provide hands-on experience with contemporary computational methods used across a wide range of scientific disciplines.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Translate scientific questions into well-defined computational problems and design appropriate, reproducible workflows.
- Use computational tools and scientific programming practices to manage, analyse, and model complex scientific datasets.
- Select and apply appropriate statistical and computational methods to quantify uncertainty, evaluate hypotheses, and interpret scientific results.
- Critically assess the assumptions, strengths, and limitations of computational, statistical, and machine-learning approaches.
- Communicate computational methods and scientific conclusions clearly, with appropriate consideration of transparency, uncertainty, and limitations.
- Work effectively with others to solve data-intensive scientific problems.
Other Information
This course has a limit on enrolments, which is on a first-come-first-served basis.
Indicative Assessment
- Six practical assignments (40) [LO 1,2,3,4,5]
- Group project report (20) [LO 1,2,3,4,5,6]
- Group oral presentation (10) [LO 4,5,6]
- Final written examination (30) [LO 1,3,4,5]
The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.
Workload
The expected workload will consist of approximately 130 hours throughout the semester including:
- Face-to face component which may consist of 1 hour of lectures and 3 hours of tutorial per week
- Approximately 82 hours of self-directed study which will include preparation for lectures, presentations and other assessment tasks (average 3 hours per week of problem sets).
Students are expected to actively participate and contribute towards discussions and activities.
Inherent Requirements
No specific inherent requirements have been identified for this course.
Requisite and Incompatibility
Prescribed Texts
Free online textbook: The elements of statistical learning: https://hastie.su.domains/ElemStatLearn/
Preliminary Reading
Indicative readings will be provided in the Learning Management System.
Assumed Knowledge
Students are expected to be comfortable with algebra, analytic functions, graphs, and introductory statistical concepts. Students should understand scientific problem solving and quantitative reasoning. Some prior experience with programming is highly beneficial but not required. Students with experience in programming (i.e. Julia, Python, MATLAB, Mathematica) may find the transition easier, although computational skills will be developed in an applied way during the course. The course is suitable for students from science, engineering, mathematics, and related disciplines who are interested in developing computational and data-analysis skills for scientific research. Recommended prior courses include quantitatively and problem solving oriented courses in mathematics, statistics, computer science, physics, earth sciences, computational biology, or engineering.
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2027 | $5040 |
- International fee paying students
| Year | Fee |
|---|---|
| 2027 | $7200 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 10528 | 26 Jul 2027 | 02 Aug 2027 | 31 Aug 2027 | 29 Oct 2027 | In Person | N/A |