SIT112 - Data Science Concepts
Unit details
| Year: | 2023 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Davoud Mougouei |
| Prerequisite: | Nil |
| Corequisite: | Nil |
| Incompatible with: | SIT114 |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. |
| Scheduled learning activities - campus: | 1 x 2 hour online class per week, 1 x 2 hour workshop per week, weekly drop-in sessions. |
| Scheduled learning activities - online: | Online independent and collaborative learning including 1 x 2 hour online class per week (recordings provided), 1 x 2 hour online workshop per week, weekly drop-in sessions. |
Content
Data science is an emerging field and data scientists must be able to know how to make sense of data. In SIT112, students will develop knowledge of fundamentals in data science, in particular data manipulation and algorithms for analytics. The unit will also cover the practice of data science including ethical and responsible behaviour when crawling, cleaning, analysing, representing and repurposing the data. Students will be able to obtain data, recognise data formats, summarise and visualise relationships in the data, perform exploratory data analysis tasks and build predictive models.
| ULO | These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: | Deakin Graduate Learning Outcomes |
|---|---|---|
| ULO1 | Demonstrate data acquisition, data representation and data pre-processing skills to describe, analyse and repurpose data from a variety of sources. | GLO1: Discipline-specific knowledge and capabilities GLO2: Communication GLO3: Digital literacy GLO4: Critical thinking GLO5: Problem solving GLO7: Teamwork |
| ULO2 | Apply critical thinking and statistical techniques to understand and visualize relationships in data. | GLO2: Communication GLO4: Critical thinking GLO5: Problem solving GLO7: Teamwork |
| ULO3 | Apply machine-learning techniques in exploratory data analysis for problems related to commerce, industry and research. | GLO1: Discipline-specific knowledge and capabilities GLO3: Digital literacy GLO4: Critical thinking GLO5: Problem solving GLO7: Teamwork |
| ULO4 | Design and compute a statistical relationships in data including correlation and linear regression. | GLO1: Discipline-specific knowledge and capabilities GLO4: Critical thinking GLO5: Problem solving GLO7: Teamwork |
| ULO5 | Design and develop data-driven algorithms for outcome prediction. | GLO1: Discipline-specific knowledge and capabilities GLO5: Problem solving GLO7: Teamwork |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Learning Portfolio | Portfolio consisting of various programming and software design modelling tasks | 80% | Weekly task submissions with final learning portfolio finalised by the end of week 12 |
| End-of-Unit Assessment | 2-hour online quiz | 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.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.
Learning Resource
The texts and reading list for the unit can be found on the University Library via the link below: SIT112 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
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