Big Data and Analytics (Masters)

Unit Outline (Higher Education)

Level of Unit in Course

AQF Level(s) of Course

Level of Unit in Course

Introductory

Intermediate

Effective Term:

2024/05

Institute / School :

Institute of Innovation, Science & Sustainability

Unit Title:

Big Data and Analytics (Masters)

Unit ID:

ITECH5103

Credit Points:

15.00

Prerequisite(s):

Nil

Co-requisite(s):

Nil

Exclusion(s):

(GPSIT1103 and ITECH1103)

ASCED:

020303

Other Change:

Brief description of the Unit

This unit provides fundamental concepts related to big data and analytics. This unit 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 unit will explain the challenges organisations are facing with managing big data.

Grade Scheme:

Graded (HD, D, C, P, MF, F, XF)

Work Experience Indicator:

Placement Component: No

Supplementary Assessment:

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:

Learning Outcomes:

Knowledge:

K1.	Describe the different types of data (e.g. structured, semi-structured, unstructured) and their sources (e.g. sensors, medical, business, social data)

K2.	Discuss the stages of the big data analytics lifecycle.

K3.	Outline the main tools and techniques in this area.

K4.	Explain the importance of big data governance.

Skills:

S1.	Create and deliver reports using an analytical tool(s) on a real-world or simulated dataset.

S2.	Research, explore and explain the current range of big data and analytics solutions and emerging trends and future issues.

S3.	Explain contemporary IT industry practices/presentations relevant to Big Data and Analytics, and relate them to professional standards and your own career aspirations

Application of knowledge and skills:

A1.	Communicate the stages, outcomes, and implications of the data analytics process, with reference to established academic literature.

A2.	Apply big data analytics technology to a real-world or simulated dataset.

Unit Content:

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.

Graduate Attributes:

Federation University recognises that students require key transferable employability skills to prepare them for their future workplace and society. FEDTASKS (Transferable Attributes Skills and Knowledge) provide a targeted focus on five key transferable Attributes, Skills, and Knowledge that are be embedded within curriculum, developed gradually towards successful measures and interlinked with cross-discipline and Co-operative Learning opportunities. One or more FEDTASK, transferable Attributes, Skills or Knowledge must be evident in the specified learning outcomes and assessment for each FedUni Unit, and all must be directly assessed in each Course.

FED TASK and descriptor		Development and acquisition of FEDTASKS in the Unit
FED TASK and descriptor		Learning outcomes (KSA)	Assessment task (AT#)
FEDTASK 1 Interpersonal	Students at this level will demonstrate an advanced ability in a range of contexts to effectively communicate, interact and work with others both individually and in groups. Students will be required to display high level skills in-person and/or online in: • Using and demonstrating a high level of verbal and non-verbal communication • Demonstrating a mastery of listening for meaning and influencing via active listening • Demonstrating and showing empathy for others • High order skills in negotiating and conflict resolution skills\\ • Demonstrating mastery of working respectfully in cross-cultural and diverse teams.	Not applicable	Not applicable
FEDTASK 2 Leadership	Students at this level will demonstrate a mastery in professional skills and behaviours in leading others. • Creating and sustaining a collegial environment • Demonstrating a high level of self -awareness and the ability to self-reflect and justify decisions • Inspiring and initiating opportunities to lead others • Making informed professional decisions • Demonstrating initiative in new professional situations.	Not applicable	Not applicable
FEDTASK 3 Critical Thinking and Creativity	Students at this level will demonstrate high level skills in working in complexity and ambiguity using the imagination to create new ideas. Students will be required to display skills in: • Reflecting critically to generate and consider complex ideas and concepts at an abstract level • Analysing complex and abstract ideas, concepts and information • Communicate alternative perspectives to justify complex ideas • Demonstrate a mastery of challenging conventional thinking to clarify complex concepts • Forming creative solutions in problem solving to new situations for further learning.	Not applicable	Not applicable
FEDTASK 4 Digital Literacy	Students at this level will demonstrate the ability to work competently across a wide range of tools, platforms and applications to achieve a range of tasks. Students will be required to display skills in: • Mastering, exploring, evaluating, managing, curating, organising and sharing digital information professionally • Collating, managing complex data, accessing and using digital data securely • Receiving and responding professionally to messages in a range of professional digital media • Contributing competently and professionally to digital teams and working groups • Participating at a high level in digital learning opportunities.	K1,K2,S1,S2,A1,A2	AT1
FEDTASK 5 sustainable and Ethical Mindset	Students at this level will demonstrate a mastery of considering and assessing the consequences and impact of ideas and actions in enacting professional ethical and sustainable decisions. Students will be required to display skills in: • Demonstrate informed judgment making that considers the impact of devising complex solutions in ambiguous global economic environmental and societal contexts • Professionally committing to the promulgation of social responsibility • Demonstrate the ability to evaluate ethical, socially responsible and/or sustainable challenges and generating and articulating responses • Communicating lifelong, life-wide and life-deep learning to be open to the diverse professional others • Generating, leading and implementing required actions to foster sustainability in their professional and personal life	Not applicable	Not applicable

	Learning Outcomes Assessed	Assessment Tasks	Assessment Type	Weighting
1.	S1, A2	Students will create data analytics models such as ERD's and apply various data preparation and preprocessing methods to data, implemented as an SQL database.	Lab work and/or Assignment(s)	10%
2.	K1, S1, A1, A2	Students will choose and/or implement an appropriate data analytic solution for a chosen specific problem and describe the components of the data analytics process. The assessment also includes theoretical questions drawing from the academic literature to provide context and opportunities for reflection on the analytical tasks undertaken, including consideration of big data, the diﬀerent types of data and their sources.	Lab work and/or Assignment(s)	30%-50%
3.	K2, K3, K4, S1, S2, S3, A1, A2	Students will create data analytics models, apply various data preparation, preprocessing, analytic solutions for a specific problem. This will be written up as a report, which also includes theoretical questions drawing from the academic literature to provide context and opportunities for reflection on the analytical tasks undertaken, including consideration of data governance.	Lab work and/or Assignment(s)	30%-50%
4.	K1-K4, S2, A1	Tests and/or examinations covering a range of taught big data and analytics topics	Oral / Written Test(s)	0% - 30%

Adopted Reference Style:

APA

Professional Standards / Competencies:

Standard / Competency

Australian Computer Society - Core Body of Knowledge: 2023 accreditation

Attribute			Assessed	Level
Core ICT Knowledge
	ICT Fundamentals
		Computational thinking: situation analysis and modelling using a range of methods and patterns to frame it so a computer system could operate effectively within it	Yes	Intermediate
		Design thinking: methods and tools that are used for handling abstraction could vary a great deal with the branch of ICT, from circuit diagrams to data modelling tools to business process modelling	Yes	Intermediate
		Information processing in humans and machines, artificial intelligence	Yes	Intermediate
	Information & Data Science and Engineering
		Nature of data, information and knowledge, meta-data, abstraction and representational quality	Yes	Introductory
		Data modelling and semantics, relational data engineering processes	Yes	Introductory
		Database Management Systems and SQL, non-relational systems (blockchain, NoSQL, files)	Yes	Introductory
		Data Science and Engineering, data analytics, mining and visualisation, big data	Yes	Introductory
	Computational Science and Engineering
		Process and algorithm modelling: methods of algorithm design, software quality	Yes	Introductory
	Application Systems
		Application context where specifically linked to ICT: Domain attributes (e-health, e-business, transport and logistics, agriculture, e-government, etc), language and cultural factors, users work practices and organisational contexts	Yes	Introductory
	ICT Projects
		Project management: team management, estimation techniques, project scheduling, quality assurance, configuration management, project management tools, progress analysis, reporting and presentation techniques	Yes	Introductory
Professionalism as it applied in ICT
	Professional ICT Ethics
		Fundamental ethics notions (stakeholders, responsibility, harm, benefit, rights, virtues, duty, respect and consequences) and ethics theories	Yes	Intermediate
		Methods of ethical reasoning, analysis and reflection, ethics canvas	Yes	Intermediate
		ICT specific ethics issues: adverse stakeholder impacts of ICT, surveillance and privacy, data matching, autonomous computing, digital divide, etc.	Yes	Introductory
	Impacts of ICT
		Impacts of ICT on organisations, workplaces, jobs and skills	Yes	Introductory
	Working Individually and in ICT development teams
		Team organisation, development and management, especially of multi-disciplinary, diverse ICT teams; collaboration, group dynamics, leadership styles, conflict resolution, groupware and virtual teams	Yes	Introductory
	Professional Communication
		Communication with different audiences (technical, managerial, users and non-digitally orientated audiences) in different forums (meetings, presentations, networking)	Yes	Introductory
	The Professional ICT Practitioner
		Continuing professional development, career upskilling, networking	Yes	Introductory

Skills Framework for the Information Age (SFIA): Version 8

Attribute			Assessed	Level
Development and implementation
	Data and analytics
		DTAN Data modelling and design (Levels 2 - 5) Developing models and diagrams to represent and communicate data requirements and data assets.	Yes	2
		DBDS Database design (Levels 3 - 5) Specifying, designing and maintaining mechanisms for storing and accessing data.	Yes	1
		DATS Data science (Levels 2 - 7) Applying mathematics, statistics, data mining and predictive modelling techniques to gain insights, predict behaviours and generate value from data.	Yes	2