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ITEC203 Introduction to Data Science and Machine Learning


ITEC327 Essentials of Artificial Intelligence and Machine Learning

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

Unit rationale, description and aim

To make analysts’ work faster, more efficient, and more accurate and enable natural language interactions with analytics systems, organisations are turning to Artificial Intelligence (AI) analytics technologies and augmented analytics—including machine learning, natural language interactions, and complex algorithms. Augmented analytics is the use of enabling technologies such as Machine Learning and Artificial Intelligence (AI) to assist with data preparation, insight generation and insight explanation to augment how people explore and analyse data in analytics and Business Intelligence (BI) platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management, and deployment.

This unit provides students with the foundational knowledge of using AI and Analytics as computerised support for decision making. This unit covers Microsoft’s AI Fundamentals curriculum and provides a pathway to the Microsoft Azure AI Fundamentals certification. Also, students will learn to apply various advanced tools for developing AI-driven analytics solutions.

The aim of the unit is to prepare students for the era of cognitive analytics and artificial intelligence which is changing the way individuals interact with data and systems, and the way businesses are run. This unit enables students to build AI-driven solutions for supporting individuals, organisations and society. 

This unit if offered from 2024.

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

LO1 - Describe fundamental principles of Artificial Intelligence and machine learning for business and data analytics (GA5, GA8) 

LO2 - Describe features of Computer Vision, Natural Language Processing (NLP) and conversational Artificial Intelligence workloads on Microsoft Azure platform (GA5, GA10) 

LO3 - Use visual tools to create machine learning models, build a conversational bot and analyse text and images (GA5, GA10)

LO4 - Apply Artificial Intelligence and Machine Learning tools and techniques to solve real-world problems of businesses, environment and society (GA2, GA5) 

Graduate attributes

GA2 - recognise their responsibility to the common good, the environment and society 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 

GA10 - utilise information and communication and other relevant technologies effectively.


Topics will include:

  • Artificial intelligence, Concepts, Drivers, Major Technologies, and Business Applications
  • AI support for decision making
  • Machine-Learning Techniques for predictive analytics
  • Deep learning and Cognitive computing
  • Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants and Robo Advisors
  • Caveats of Analytics and AI: Ethics, Privacy, Organisational and Societal impacts

Learning and teaching strategy and rationale

Students should anticipate undertaking 150 hours of study for this unit, including class attendance, readings, online forum participation and assessment preparation. 

This unit is offered in different modes (“Attendance” mode and “Online” mode)to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.  

Attendance Mode 

In attendance mode, students will require face-to-face attendance in blocks of time determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for you to prepare and revise.  

Online Mode 

This unit utilises an active learning approach whereby students will engage in e-module activities, readings and reflections, and opportunities to collaborate with peers in an online environment. This can involve, but is not limited to, online workshops, online discussion forums, chat rooms, guided reading, and webinars. To deliver core content, pre-recorded lectures will be incorporated within the online learning environment and e-modules. In addition, electronic readings will be provided to guide students’ reading and extend other aspects of online learning. 

Assessment strategy and rationale

A range of assessment procedures will be used to meet the unit learning outcomes and develop graduate attributes consistent with university assessment requirements. The first assessment item consists of several machine learning tasks designed to develop students' AI analytical skills and prepare them for the next two assessment tasks. The second assessment item is a Microsoft Certification exam that assesses students' fundamental knowledge of Artificial Intelligence and Machine Learning. It also assesses students' understanding of using basic Microsoft Azure services related to artificial intelligence and machine learning. The final assessment is an AI and machine learning project that assesses students’ AI and machine learning knowledge for data analytics purposes with consideration of AI ethics and safety. 

The assessments for this unit are designed to demonstrate the achievement of each learning outcome. To pass this unit, you are required to obtain an overall mark of at least 50%.  

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Assessment Task 1: Developmental exercises 

The first assessment item consists of several practical exercises using Microsoft Azure AI platform. 

Submission Type: Individual 

Assessment Method: Practical Tasks 

Artefact: Program Files.


LO1, LO3

GA5, GA8, GA10

Assessment Task 2: Certification Exam

 This assessment task requires students to undertake AI-900: Microsoft AI Fundamental Certification Exam.

The exam assesses students’ foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.  

Submission Type: Individual 

Assessment Method: Exam 

Artefact: Certification.


LO1, LO2

GA5, GA10

Assessment Task 3:  Augmented Analytics Project

In this assessment students apply various workloads on Microsoft Azure platform including Machine Learning Models, Computer Vision, Natural Language Processing, and Conversational AI, to solve the problems in the context of a case study.

To ensure academic integrity student are required to present their work in class or record and submit a video presentation.

Submission Type: Individual 

Assessment Method: Practical Tasks + Written Report 

Artefact: Report + Program File + Presentation 


LO2, LO3, LO4

GA2, GA5, GA10

Representative texts and references

Sharda, R., Delen, D., Turban, E., 2020, Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, Global Edition, Pearson.

Microsoft Certified: Azure AI Fundamentals (Online – Open Access), Available at:

LaPlante, A., 2019, What Is Augmented Analytics?, O'Reilly Media, Inc.

Stuart Russell and Peter Norvig, 2020. Artificial Intelligence: A Modern Approach, 4th edition Pearson. 

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