FIT5149 - Applied data analysis - 2019

6 points, SCA Band 2, 0.125 EFTSL

Postgraduate - Unit

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


Information Technology

Chief examiner(s)

Dr Lan Du

Unit guides



  • First semester 2019 (On-campus)
  • Second semester 2019 (On-campus)

Monash Online

  • Teaching Period 6 2019 (Online)


FIT5197 or (ETC5252 plus experience with programming in R)


Monash Online offerings are only available to students enrolled in the Graduate Diploma in Data ScienceGraduate Diploma in Data Science ( via Monash Online.


This unit aims to provide students with the necessary analytical and data modeling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary Machine Learning techniques for data analysis and presentation using widely available analysis software. They will look at a number of characteristic problems/data sets and analyse them with appropriate machine learning and statistical algorithms. Those algorithms include regression, classification, clustering and so on. The unit focuses on understanding the analytical problems, machine learning models, and the basic modeling theory. Students will need to interpret the results and the suitability of the algorithms


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

  1. analyse data sets with a range of statistical, graphical and machine-learning tools;
  2. evaluate the limitations, appropriateness and benefits of data analytics methods for given tasks;
  3. design solutions to real world problems with data analytics techniques;
  4. assess the results of an analysis;
  5. communicate the results of an analysis for both specific and broad audiences.


NOTE: From 1 July 2019, the duration of all exams is changing to combine reading and writing time. The new exam duration for this unit is 2 hours and 10 minutes.

For Monash Online: In-semester assessment: 100%

On-campus: Examination (2 hours and 10 minutes): 50%; In-semester assessment: 50%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

Contact hours for on-campus students:

  • Two hours/week lectures.
  • Two hours/week tutorials.

Contact hours for Monash Online students:

  • Two hours/week online group sessions.
  • Online students generally do not attend the lecture, tutorial and laboratory sessions, however, should plan to spend equivalent time working through resources and participating in discussions.

Additional requirements:

  • 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

This unit applies to the following area(s) of study

Advanced data analytics

Data science