FIT5147 - Data exploration and visualisation - 2018

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.

Faculty

Information Technology

Chief examiner(s)

Professor Kimbal Marriott

Unit guides

Offered

Caulfield

  • First semester 2018 (On-campus)

Monash Online

  • Teaching Period 2 2018 (Online)

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 with R is required.

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 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 and other tools will also be presented.

Outcomes

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

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

Assessment

In-semester assessment: 100%

Workload requirements

Minimum total expected workload equals 144 hours per semester comprising:

  1. Contact hours for on-campus students:
    • One hour per week lecture
    • Two hours per 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 9 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

Additional information on this unit is available from the faculty at: