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    Overview

    Explores a range of statistical techniques for data analysis, from statistical modelling of univariate data to the visualisation of patterns in multivariate data.

    An introduction to statistical modelling and multivariate analysis. The paper combines background theory with practice in applying the methods to real datasets.

    About this paper

    Paper title Statistical Techniques for Data Science
    Subject Information Science
    EFTS 0.1667
    Points 20 points
    Teaching period(s) Semester 2 (Distance learning)
    Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,535.64
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Prerequisite
    STAT 110
    Restriction
    STAT 210
    Limited to
    MBusDataSc, BCom(Hons), BSc(Hons), BA(Hons), PGDipCom, PGDipSci, PGDipArts, BAppSc(Hons), MAppSc, MSc, MBus, PGCertAppSc, PGDipAppSc
    Eligibility
    Students studying for the MBusDataSc; any student interested in techniques that can be used to model a very broad range of datasets.
    Contact

    Associate Professor Matthew Parry

    Teaching staff

    Associate Professor Matthew Parry

    Dr Xun Xiao

    Paper Structure

    Main topics:

    • Introduction to statistics
    • Linear regression
    • Analysis of variance
    • Interaction
    • Model building
    • Logistic regression
    • Time series analysis
    • Simulation
    • Sampling
    • Principal component analysis
    • Clustering
    • Classification
    • Smoothing
    • Generalised additive models
    • Penalised regression
    Textbooks

    Textbooks are not required for this paper.

    Graduate Attributes Emphasised
    Scholarship, Communication, Critical thinking, Information literacy.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    a) Apply important statistical techniques to real data;

    b) Describe the assumptions underlying use of each of these methods;

    c) Understand key statistical ideas related to the use of probabilistic models, model selection and quantification of uncertainty;

    d) Critically appraise literature in terms of the statistical methods used; 

    e) Use a standard statistical programming language (R) to analyse data.

    Timetable

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught through Distance Learning
    Learning management system
    Other

    Lecture

    Stream Days Times Weeks
    Attend
    A1 Monday 16:00-17:50 29-35, 37-42

    Tutorial

    Stream Days Times Weeks
    Attend
    A1 Friday 10:00-11:50 29-35, 37-42

    Semester 2

    Location
    Dunedin
    Teaching method
    This paper is taught On Campus
    Learning management system
    Other

    Lecture

    Stream Days Times Weeks
    Attend
    A1 Monday 16:00-17:50 29-35, 37-42

    Tutorial

    Stream Days Times Weeks
    Attend
    A1 Friday 10:00-11:50 29-35, 37-42
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