FIT2086 Modelling for data analysis
Faculty of Information Technology
FIT2086 Modelling for data analysis is a level 2, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2024 in Semester 2 at Clayton and Malaysia. It needs (FIT1053 or FIT1045) and (ENG1005, MAT1841, MTH1030 or MTH1035) and unlocks 6 units, leading on to 9 units in all.
- Credit points
- 6
- Offered in 2024
- Semester 2
- Clayton, Malaysia
- Assessment
- Exam 50%
- and 3 other tasks
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2027 entry.
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Requisites
Before FIT2086
Prerequisites
Pass these before you enrol.
After FIT2086
6 units list FIT2086 as a prerequisite or corequisite.
Overview
This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. It covers:
- Data: collection and sampling, data quality.
- Analytic tasks: statistical hypothesis testing, exploratory and confirmatory analysis.
- Probability distributions: dependence and independence, multivariate Gaussian, Poisson, Dirichlet, random number generation and simulation of distributions, simulation of samples (bootstrap).
- Predictive models: linear and logistic regression, and Bayesian classification.
- Estimation: parameter and function estimation, maximum likelihood and minimum cost estimators, Monte Carlo estimators, inverse probabilities and Bayes theorem, bias versus variance and sample size effects, cross validation, estimation of model performance.
Offerings in 2024
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | Flexible |
| Second semester | Malaysia | On campus |
Assessment
- Assignment 1AssignmentThreshold hurdle10%
- Assignment 2AssignmentThreshold hurdle20%
- Assignment 3AssignmentThreshold hurdle20%
- Scheduled final assessmentExamThreshold hurdle50%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Perform exploratory data analysis with descriptive statistics on given datasets;
- 2
Construct models for inferential statistical analysis;
- 3
Produce models for predictive statistical analysis;
- 4
Perform fundamental random sampling, simulation and hypothesis testing for required scenarios;
- 5
Implement a model for data analysis through programming and scripting;
- 6
Interpret results for a variety of models.
Workload and teaching
- Studio activities24 hours
- Seminars24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.
Learning resources
Technology resources
You are required to regularly check Moodle for announcements regarding the subject.
(For Clayton Campus ONLY) Please note: This is a bring your own device unit. You will be expected to bring a web-connected device (i.e., laptop or tablet) to class to access specialist software. The applications for your class can be accessed at the website move.monash.edu. For more information, visit monash.edu/move
Where it fits
FIT2086 is part of 4 areas of study in the 2024 handbook.
Contacts
- Chief Examiners
- Dr Daniel Schmidt
- Unit Coordinators
- Dr Bisan Alsalibi
Common questions
What are the prerequisites for FIT2086?
You need (FIT1053 or FIT1045) and (ENG1005, MAT1841, MTH1030 or MTH1035) before you enrol.
What can I take after FIT2086?
FIT2086 is a prerequisite or corequisite for 6 units, including ADS3001, FIT3152, FIT3154, FIT3158, FIT3163 and FIT3181. Those lead on to 9 units in all.
When is FIT2086 offered?
In 2024, FIT2086 runs in Semester 2 at Clayton and Malaysia.
How much work is FIT2086?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT2086 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 3 other tasks.
Which majors and minors include FIT2086?
FIT2086 is part of Computational science and Data science.