| Effective Term: | 2022/20 |
| Institute / School : | Institute of Innovation, Science & Sustainability |
| Unit Title: | Big Data Analytics |
| Unit ID: | GPSIT1103 |
| Credit Points: | 15.00 |
| Prerequisite(s): | Nil |
| Co-requisite(s): | Nil |
| Exclusion(s): | Nil |
| ASCED: | 020303 |
| Other Change: | |
| Brief description of the Unit |
This course provides fundamental concepts related to big data and analytics. This course will explore the theory and applications of big data and demonstrate the process from data to decisions. Students will learn big data in various formats, data processing platforms and data analytics tools to transform, visualise, model, and communicate the insights hidden in the data, providing end users with timely knowledge to support decision making. The course will explain the challenges organisations are facing with managing big data. This course will incorporate additional learning hours to support the development of students’ academic and study skills.
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| Grade Scheme: | Graded (HD, D, C, P, MF, F, XF) |
| Work Experience Indicator: |
| Placement Component: No |
| Supplementary Assessment:Yes |
| Where supplementary assessment is available a student must have failed overall in the Unit but gained a final mark of 45 per cent or above, has completed all major assessment tasks (including all sub-components where a task has multiple parts) as specified in the Unit Description and is not eligible for any other form of supplementary assessment |
| 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. | Understand the modelling, design and implementation stages of modern data solutions (e.g. relational and big data). |
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| K2. | Analyze data of various types (e.g. structured, semi-structured, unstructured). |
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| K3. | Examine different big data analytics techniques, tools, applications. |
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| K4. | Understand the diverse types of contemporary big data (e.g. IoT, social data). |
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| K5. | Understand the stages of the big data analytics lifecycle. |
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| Skills: |
| S1. | Demonstrate skills in designing and building a data centric application. |
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| S2. | Use analytical tools on a real-world dataset. |
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| S3. | Analyse the current range of big data and analytics solutions and emerging trends and future issues. |
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| S4. | Understand the importance of IT governance for big data. |
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| S5. | Develop the appropriate English language and academic skills to successfully study at an undergraduate level |
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| Application of knowledge and skills: |
| A1. | Communicate a coherent exposition of the outcomes of the data analytics process. |
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| A2. | Apply big data analytics technology to a real-world dataset. |
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| A3. | Appreciate your career possibilities and how they can be achieved.
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| Unit Content: |
This may include:
•Big data concepts, applications and tools;
•Structured data processing such as RDBMS, SQL
•Non-structured data processing
•Data analytics technologies
•Stream mining, real time analytics
•Predictive analytics
•Big data applications. |
| Values: |
| V1. | Value the need for and complexity of mining, visualizing and management of data from diverse sources in various structures;
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| V2. | Appreciate the importance and benefits of Big Data Analytics techniques in today`s business world. |
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| V3. | Recognise the importance of research to the development and progress of the IT industry. |
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| V4. | Value IT as an underlying transformative technology to all of society in the information and immersive ages
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| V5. | Appreciate your career possibilities and how they can be achieved.
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| V6. | Appreciate the range of problems faced by industry practitioners and how problem solving skills learnt may be applied in the industry context.
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| V7. | Appreciate how theory and practice learnt is applied in industry. |
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| Graduate Attributes: |
| The Federation University graduate attributes (GA) are entrenched in the Higher Education Graduate Attributes Policy (LT1228).Federation University Australia graduates develop these graduate attributes through their engagement in explicit learning and teaching and assessment tasks that are embedded in all Federation Courses. Graduate attribute attainment typically follows an incremental development process mapped through Course progression. One or more graduate attributes must be evident in the specified learning outcomes and assessment for each Federation University Australia Unit, and all attributes must be directly assessed in each Course. |
| Graduate attribute and descriptor | Development and acquisition of GAs in the Unit | Learning outcomes
(KSA) | Assessment task
(AT#) | GA 1
Thinkers | Our graduates are curious, reflective and critical. Able to analyse the world in a way that generates valued insights, they are change makers seeking and creating new solutions. | K1, K2, K3, K4, K5, S4 | 1,2 | GA 2
Innovators | Our graduates have ideas and are able to realise their dreams. They think and act creatively to achieve and inspire positive change. | S2, K1, K3, K5 | 1,2 | GA 3
Citizens | Our graduates engage in socially and culturally appropriate ways to advance individual, community and global well-being. They are socially and environmentally aware, acting ethically, equitably and compassionately. | S1 ,S3 | 1 | GA 4
Communicators | Our graduates create, exchange, impart and convey information, ideas, and concepts effectively. They are respectful, inclusive and empathetic towards their audience, and express thoughts, feelings and information in ways that help others to understand. | K3, K4, K5, S5 | 1,2 | GA 5
Leaders | Our graduates display and promote positive behaviours, and aspire to make a difference. They act with integrity, are receptive to alternatives and foster sustainable and resilient practices. | N/A | N/A |
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| | Learning Outcomes Assessed | Assessment Tasks | Assessment Type | Weighting | | 1. | K1-K5, S1, S2, S3, S5, A1, A2 | Illustrate skills in the analysis and practical application of Big Data Analytics technologies. | Tutorial task(s)/Assignment(s)/Presentation(s) | 60% - 70% | | 2. | K1-K5, S3, S4, S5, A2, A3 | Attend lectures, read course content, summarise theoretical aspects of the course. | Examination(s) and/or Test(s) | 30% - 40% |
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