MIS140 - Introduction to Machine Learning for Business
Unit details
| 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: Hao Liu Trimester 2: Arman Kaldi Trimester 3: Hao Liu |
| 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 |
| ULO2 | Select and apply machine learning techniques to solve business problems and evaluate model performance. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Explain the application of machine learning and interpret the outcomes to the various stakeholders. | GLO2: Communication |
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.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.