This unit entry is for students who completed this unit in 2016 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.

FIT1043 - Introduction to data science

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Information Technology

Offered

Clayton

Second semester 2016 (Day)

Malaysia

Second semester 2016 (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

At the completion of this unit, students should be able to:

explain the role of data in different styles of business;
demonstrate the size and scope of data storage and data processing, and classify the basic technologies in use;
identify tasks for data curation and management in an organisation;
classify participants in a data science project: such as statistician, archivist, analyst, and systems architect;
classify the kinds of data analysis and statistical methods available for a data science project;
locate suitable 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:

Contact hours for students:

Two hours lectures
Two hours laboratories

Additional requirements:

A minimum of 8 hours of personal study per week in order to satisfy the reading, tute, prac and assignment expectations.

Chief examiner(s)

Dr Wray Buntine

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

Data science

Prerequisites

VCE Mathematics Methods or Specialist Mathematics units 3 & 4 with a study score of 25 or MTH1010.
Note: For 2016 Further Mathematics with a study score of 35 will be accepted.

Prohibitions

FIT5145

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

http://www.infotech.monash.edu.au/units/