| Effective Term: | 2024/05 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Data Science for All |
| Unit ID: | ITECH5007 |
| Credit Points: | 15.00 |
| Prerequisite(s): | Nil |
| Co-requisite(s): | Nil |
| Exclusion(s): | Nil |
| ASCED: | 020199 |
| Other Change: | |
| Brief description of the Unit |
DATA SCIENCE FOR ALL is an introductory unit to data science, a fast-growing and exciting field. This unit will provide an overview of a number of topics that play fundamental roles across various subjects in data science. The unit features an emphasis on foundations and practical knowledge of data science, as well as computational thinking and real-world relevance. Topics to be covered include data types, data representation, data preparation, data processing and mining, data management, data exploration and visualisation. Hands-on experience working with real-world data, techniques, and tools will prepare students for advanced units and enable students to start careers as data scientists. |
| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| No work experience |
| Placement Component: | |
| Supplementary Assessment:No |
| Supplementary assessment is not available to students who gain a fail in this Unit. |
| Course Level: |
| Level of Unit in Course | AQF Level(s) of Course | | 5 | 6 | 7 | 8 | 9 | 10 | | Introductory | | | |  | | | | Intermediate | | | | | | | | Advanced | | | | | | |
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| Learning Outcomes: |
| Knowledge: |
| K1. | Interpret the principles of modern data science as well as data science lifecycle. |
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| K2. | Differentiate between the most common forms of data types and representations. |
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| K3. | Critique and apply a core collection of elementary techniques for data preparation, processing, management, exploration, and visualisation. |
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| K4. | Examine a core collection of methods and algorithms for data analysis and mining. |
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| Skills: |
| S1. | Demonstrate competent skills in using data science technology for solving complex problems at an appropriate level of difficulty. |
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| S2. | Contrast and use data science software and tools. |
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| S3. | Implement any chosen data science solution and communicate the results effectively. |
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| Application of knowledge and skills: |
| A1. | Employ appropriate techniques and tools to process and analyse data. |
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| A2. | Integrate data science principles, methods, techniques and tools covered in this unit to plan and execute a data science project. |
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| Other outcomes: |
| Unit Content: |
Topics may include: introduction to data and data science data types and representation foundations of algorithms and programming data collection, pre-processing, and wrangling data visualisation data management data analytics fundamentals of data mining data science tools |
| Graduate Attributes: |
| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | Professional Standards |
| 1. |
S1-3, A1, A2 |
Students will apply data science principles, methods, techniques and tools to design, implement and document solutions to simple problems. |
Assignments and exercises |
40%-50% |
|
| 2. |
K1-4, S1-3, A1 |
Students will provide theoretical answers and provide practical solutions to a range of questions and problems drawn from theory and examples used during the unit. |
Test(s) |
50%-60% |
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