Faculty of Information Technology

Monash University

Postgraduate - Unit

This unit entry is for students who completed this unit in 2015 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

FacultyFaculty of Information Technology
OfferedMonash Online Teaching Period 6 2015 (Online)


Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data ScienceGraduate Diploma in Data Science (http://online.monash.edu/course/graduate-diploma-data-science/?Access_Code=MON-GDDS-SEO2&utm_source=seo2&utm_medium=referral&utm_campaign=MON-GDDS-SEO2) via Monash Online.


This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. Motivated by case studies and working through real examples, this unit covers the mathematical and statistical basis with an emphasis on using the techniques in practice. It introduces data collection, sampling and quality. It considers analytic tasks such as statistical hypothesis testing and exploratory versus confirmatory analysis. It presents basic probability distributions, random number generation and simulation as well as estimation methods and effects such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation. Basic information theory and dependence models such as Bayesian networks and log-linear models are also presented, as well as the role of general modelling such as inference and decision making, predictive models, experts and assessing probabilities.


Upon successful completion of this unit, it is expected that students will be able to:

  1. perform the general roles of exploratory, confirmatory and decision analysis applied to data;
  2. explain how the source of data affects analysis;
  3. summarise the role of domain experts in supporting analysis and the difficulties they may have;
  4. implement a computational model for statistical analysis of simple problems and evaluate the results;
  5. conduct statistical analysis using the concepts of entropy, likelihood, correlation, and independence;
  6. interpret the challenges involved in estimation from data;
  7. describe basic methods of random sampling, simulation, and hypothesis testing;
  8. write basic programs for analysing data.


In-semester assessment: 100%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

  1. Contact hours for on-campus students:
    • Two hours/week lectures
    • Two hours/week laboratories
  2. Contact hours for Monash Online students:
    • Two hours/week online group sessions.
    • Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.
  3. Additional requirements (all students):
    • A minimum of 8 hours per week of personal study (22 hours per week for Monash online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

See also Unit timetable information

Chief examiner(s)


Students need to have the equivalent of first year undergraduate university mathematics as taught in an analytics degree such as Engineering, Finance, Physics and some Computer Science degrees.