units

FIT5147

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 introduces statistical and visualisation techniques for the exploratory analysis of data. It will cover initial data preparation, how to obtain data, clean, subset, convert and fuse it into formats suitable for analysis. It will also cover the role of data visualisation in data science and its limitations. Visualisation of qualitative, quantitative, temporal and spatial data will be presented. What makes an effective data visualisation, interactive data visualisation, and creating data visualisations with R will also be presented.

Outcomes

On successful completion of this unit a student should be able to:

  1. perform exploratory data analysis using Python and R;
  2. demonstrate the role of data visualisation in data science and its limitations;
  3. critically evaluate data and its provenance;
  4. critically evaluate and interpret a data visualisation;
  5. distinguish standard visualisations for qualitative, quantitative, temporal and spatial data;
  6. choose an appropriate data visualisation;
  7. implement data visualisations using R.

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 per week lectures
  • Two hours per 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

Some of the material relies on a basic knowledge of statistics (mean, standard deviation, median) and a basic knowledge of geometry. A secondary/high-school level understanding of these concepts is sufficient.

Some knowledge of programming (scripting in Python) is required.