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    An introduction to the ICT industry, including the study of two of the following topics: foundations of information systems, web design, computer programming and human-computer interaction.

    The paper contains a series of industry visits, where students have the opportunity to gain understanding about the ICT industry landscape and learn to explain and discuss different roles of the employees in this industry. The main part of the paper, however, is spent introducing students to two topics and synthesising those in the context of possible professional roles in the IT industry. The choice of the two topics will be influenced by (a) a student's existing background and (b) his/her chosen pathway within the programme.

    About this paper

    Paper title ICT Fundamentals
    Subject Computer and Information Science
    EFTS 0.125
    Points 15 points
    Teaching period Not offered in 2024 (On campus)
    Domestic Tuition Fees ( NZD ) $977.75
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    Limited to
    Schedule C
    Arts and Music, Commerce, Science
    Available only for the Information and Communications Technology endorsement for DipGrad.

    Enrolments for this paper require departmental permission. View more information about departmental permission.


    Teaching staff

    Academic Advisers:
    Claudia Ott
    Veronica Liesoputra


    Depending on which two subjects are chosen, workbooks are available free online or no textbook is required.

    Graduate Attributes Emphasised
    Global perspective, Interdisciplinary perspective, Lifelong learning, Critical thinking, Cultural understanding, Ethics, Information literacy, Research.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    The paper covers different aspects of the ICT fundamentals. Depending on students, existing professional background, and their learning goals within the programme each student will study two ICT related topics in parallel, which may consist of: foundations of information systems, data science, computer architecture, computer programming and human-computer interaction.

    The following are the learning outcomes for some of these topics.

    Foundations of Information Systems

    • Explain the distinctions between data, information and knowledge
    • Understand basic concepts of computational approaches to information processing (e.g. binary encodings, algorithms and complexity, tool chains to develop computer programs)
    • Perform elementary processes of data collection, and identify issues relating to data quality, including ethics, privacy and security
    • Understand basic concepts of modelling, implementing and using relational databases, and be able to read and write basic SQL statements to manipulate relational databases
    • Explain the basic components of information systems, and the role of information systems in supporting an organisation’s strategic and operational needs

    Human-Computer Interaction

    • Explain concepts and principles of user experience and usability in user-centred design
    • Explain, apply and critique techniques and processes used to develop and evaluate user experience in information systems
    • Understand and communicate user-centred and goal-directed design in the context of IT innovation and entrepreneurship
    • Critically and constructively discuss emerging technologies and HCI and UX trends (lifelong learning, scholarship, research, global perspective)
    • Design and implement a prototypical user interface (scholarship, interdisciplinary perspective)

    Computer Programming

    • Understand fundamental concepts relating to computer programming
    • Demonstrate the ability to write small computer programs
    • Develop an understanding of the basic needs of scientific programming including but not limited to: data input and output, data manipulation, and data visualisation

    Data Science

    • Introduction to programming in R and RStudio
    • Importing and tidying data in R ("Data Wrangling")
    • Plotting and visualising data in R
    • Data aggregation and summarisation in R
    • Semi-structured data manipulation using Web Scraping as an example
    • Building models in R


    Not offered in 2024

    Teaching method
    This paper is taught On Campus
    Learning management system
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