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MIS140 - Introduction to Machine Learning for Business

Year

2025 unit information

Enrolment modes:Trimester 1: Burwood (Melbourne), Online
Trimester 2: Burwood (Melbourne), Online
Trimester 3: Burwood (Melbourne), Online
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Lemai Nguyen
Trimester 2: to be confirmed
Trimester 3: Lemai Nguyen
Cohort rule:Nil
Prerequisite:

Nil

Corequisite:Nil
Incompatible with: SIT112
Educator-facilitated (scheduled) learning activities - on-campus unit enrolment:

1 x 1.5 hour on-campus (live-streamed) lecture (recordings provided) and 1 x 1.5 hour on-campus seminar each week

Educator-facilitated (scheduled) learning activities - online unit enrolment:

1 x 1.5 hour recorded lecture provided and 1 x 1.5 hour online seminar (recordings provided) each week

Typical study commitment:

Students will on average spend 150-hours over the teaching period undertaking the teaching, learning and assessment activities.

Content

The overarching aim of this unit is to provide students with a strong foundation on machine learning and data analytics, required by business professionals. The unit will introduce business students to fundamental concepts in data analytics, machine learning and their applications in business context. Students will also be introduced to popular software packages for data analytics in Python programming platforms. Through this unit, students will be provided with foundational knowledge and skills to prepare themselves for more advanced analytics and Artificial Intelligence techniques for business applications in the subsequent advanced units. Students will be able to explain and apply data science concepts and develop analytical solutions to business problems and interpret the outcomes to the various stakeholders.

Learning Outcomes

ULO These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: Alignment to Deakin Graduate Learning Outcomes (GLOs)
ULO1 Apply appropriate data processing techniques and report insights.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO5: Problem solving

ULO2 Select and apply machine learning techniques to solve business problems and evaluate model performance. 

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO5: Problem solving

ULO3 Explain the application of machine learning and interpret the outcomes to the various stakeholders. 

GLO2: Communication
GLO3: Digital literacy

Assessment

Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week
Assessment 1: (Individual) Case study: Data analysis with Written Report (Analytical)  Python code + 800 words  30% Week 5
Assessment 2: (Individual) Case study: Data analysis with Written Report (Analytical)  Python code + 1000 words 35% Week 9
Assessment 3: (Individual) Case study: Written Report (Business)  Python code + 1500 words 35% Information not yet available

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

Nil

Learning resource

There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.

Unit Fee Information

Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.

Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.

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For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.