SIT742 - Modern Data Science
Unit details
| Year | 2025 unit information |
|---|---|
| Enrolment modes: | Trimester 2: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 2: Gang Li |
| Prerequisite: | Prerequisite: SIT771 and SIT718 For students enrolled in S406, S408, S434, S479, S464: Must have completed 16 credit points including SIT232 and one of SIT112 or SIT202. For students enrolled in S470, S535, S536, S538, S576, S677, S735, S735A, S737, S739, S751, S770, S778, S779, S789: SIT718 |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week. Weekly meetings. |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour practical experience (workshop) per week, weekly meetings. |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
Content
In this unit, students will have the opportunity to learn fundamental aspects of data science, modern methods, techniques and applications of data science. Upon successful completion of study, students will be able to use distributed storage and computing platform to process and analyse big data, and use modern techniques in data analytics.
Learning activities in this unit are designed for students to develop knowledge and skills in reviewing tabular data such as relational database, distributed storage and computing platforms with materials on Apache Spark. In learning data analytics, students will use feature selection, data reduction and machine learning methods. Students will also have the opportunity to learn advanced concepts in prediction including linear regression, logistic regression and decision tree classifiers, and to learn frequent pattern discovery using association rule mining algorithms.
Learning Outcomes
| ULO | These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: | Alignment to Deakin Graduate Learning Outcomes (GLOs) |
|---|---|---|
| ULO1 | Develop knowledge of and discuss new and emerging fields in data science. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Describe advanced constituents and underlying theoretical foundation of data science. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Evaluate modern data analytics and its implication in real-world applications, considering associated privacy and security concerns. | GLO4: Critical thinking |
| ULO4 | Use appropriate platform to collect and process relatively large datasets. | GLO2: Communication |
| ULO5 | Collect, model and conduct inferential as well predictive tasks from data. | GLO4: Critical thinking |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Assessment 1 | Coding and written report | 30% | Week 5 |
| Assessment 2 | Coding and written report | 50% | Week 10 |
| End-of-Unit Assessment | Timed online test | 20% | End-of-Unit Assessment Period |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Learning resource
The texts and reading list for SIT742 can be found via the University Library.
Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Unit Fee Information
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.