units

FIT5149

Faculty of Information Technology

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.

print version

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.

LevelPostgraduate
FacultyFaculty of Information Technology
OfferedNot offered in 2015

Notes

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.

Synopsis

This unit aims to provide students with the necessary analytical and data modelling skills for the roles of a data scientist or business analyst. Students will be introduced to established and contemporary techniques for data exploration, analysis and presentation using widely available analysis software. The applicability of the modelling and analysis techniques taught to data from a wide variety of sources make this unit relevant to students considering a wide range of careers. In particular the unit is relevant to studying any discipline where access to large data sets arising from observation, experimentation or human activity is likely. Thus, students from the physical and social sciences; business and economics, especially finance and risk modelling, as well as medical and biomedical research and related fields would benefit from this unit.

Outcomes

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

  • analyse data sets using a range of statistical, graphical and machine-learning tools;
  • validate and critically assess the results of analysis;
  • interpret the results of analysis and communicate these to a broad audience;
  • employ open source and proprietary software for data analytics;
  • critically assess the appropriateness of analytical methods for a given task;
  • critically evaluate the limitations and benefits of data analytics;
  • formulate how to transform a given real world problems into one that can then be solved using data analytics techniques.

Assessment

In-semester assessment: 100%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

(a.) Contact hours for on-campus students:

  • Two hours/week lectures
  • Two hours/week laboratories

(b.) 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.

(c.) 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

Prerequisites