Introduction to Statistical Computing (MAST90101)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2017 Handbook:
Or view archived Handbooks
You’re currently viewing the 2017 version of this subject

Overview

Availability
Semester 1
FeesLook up fees

The aim of this subject is to provide students with the knowledge and skills required to undertake moderate to high level data manipulation and management in preparation for statistical analysis of data typically arising in health and medical research. In particular, students gain experience in data manipulation and management using two major statistical software packages (Stata and R) and acquire fundamental programme skills for efficient use of each of these software packages.

Intended learning outcomes

The specific learning outcomes are:-






  • Gain experience in data manipulation and management using two major statistical software packages (Stata and R).
  • Learn how to display and summarise data using statistical software.
  • Become familiar with the checking and cleaning of data.
  • Learn how to link files through use of unique and non-unique identifiers.
  • Acquire fundamental programming skills for efficient use of software packages.
  • Learn key principles regarding confidentiality and privacy in data storage, management and analysis.

Generic skills

  • Independent problem solving,
  • Facility with abstract reasoning,
  • Clarity of written expression,
  • Sound communication of technical concepts

Last updated: 3 November 2022

Eligibility and requirements

Prerequisites

None

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 3 November 2022

Assessment

Additional details

  • Practical exercise (approx 4 hours of work, approx 600 words, no more than 4 pages) due in Week 2 (10%)
  • Written assignment 1 (approx 10 hours of work, approx 1800 words, no more than 10 pages) due in Week 6 (30%)
  • Written assignment 2 (approx 10 hours of work, approx 1800 words, no more than 10 pages) due in Week 8 (30%)
  • Written assignment 3 (approx 10 hours of work, approx 1800 words, no more than 10 pages) due in Week 12 (30%)

Last updated: 3 November 2022

Dates & times

Time commitment details

170 hours

Last updated: 3 November 2022

Further information

  • Texts

    Prescribed texts

    Resources Provided to Students online: Course notes and assignments.

    Special Computer Requirements: Stata and R (open access) statistical software.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseGraduate Diploma in Biostatistics
    CourseMaster of Biostatistics
    CourseDoctor of Philosophy - Engineering
    CourseMaster of Philosophy - Engineering
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

Last updated: 3 November 2022