units

FIT5145

Faculty of Information Technology

18 September 2017 22 November 2024
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.

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
OfferedCaulfield Second semester 2015 (Day)
Monash Online Teaching Period 5 2015 (Online)
Monash Online Teaching Period 6 2015 (Online)

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 looks at processes and case studies to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed.

Outcomes

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

  1. analyse the role of data in different styles of business;
  2. demonstrate the size and scope of data storage and data processing, and classify the basic technologies in use;
  3. assess tasks for data curation and management in an organisation;
  4. classify participants in a data science project: such as statistician, archivist, analyst, and systems architect;
  5. classify the kinds of data analysis and statistical methods available for a data science project;
  6. locate and assess resources, software and tools for a data science project.

Assessment

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

  1. 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.

  1. 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)

Dr Wray Buntine

Prerequisites

(FIT5131 or FIT9131) and (FIT5132 or FIT9132) or equivalent