units

FIT1043

Faculty of Information Technology

Undergraduate - 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.

LevelUndergraduate
FacultyFaculty of Information Technology
OfferedClayton Second semester 2015 (Day)

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. explain the interaction between business processes and data in an organisation;
  2. describe the role of data in different styles of business and in different parts of an organisation: health, retail, science, government;
  3. demonstrate the size and scope of data storage and data processing, and classify the basic technologies in use;
  4. describe tasks for data curation and management in an organisation;
  5. classify participants in a data science project: such as statistician, archivist, analyst, and systems architect;
  6. classify the kinds of data analysis and statistical methods available for a data science project;
  7. summarise and compare resources, software and tools for a data science project.

Assessment

Examination (3 hours): 60%; In-semester assessment: 40%

Workload requirements

Minimum total expected workload equals 12 hours per week comprising:

  1. Contact hours for students:
    • Two hours lectures
    • Two hours laboratories

  1. Additional requirements:
    • A minimum of 8 hours of personal study per week for completing lab/tutorial activities, assignments, private study and revision.

See also Unit timetable information

Chief examiner(s)

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