Data Analytics Papers at Tauranga (2020)

Data Analytics is the science of collecting and interpreting data subject to uncertainty. We live in a world where variability is everywhere. To make informed decisions we must understand the nature of variability, and make use of meaningful information. Without data we have to resort to hunches or guesses, neither of which can be relied on. Data Analytics tells us how to deal with variability, and how to collect and use data so that we can make good decisions.

Data Analytics is available as a specified programme for the Bachelor of Computer Science (BCompSc). Data Analytics is available as a first major for the Bachelor of Computing and Mathematical Sciences with Honours (BCMS(Hons)) and the Bachelor of Science (BSc). Data Analytics may also be taken as a second major or minor, subject to approval of the Division in which the student is enrolled.

To complete Data Analytics as a specified programme for the BCompSc, students must take the following papers:

*Year 1:* COMPX101, COMPX102, CSMAX170, MATHS135, STATS111 or STATS121, ENGEN101 or MATHS165 or PHILO102, DIGIB101 or MGSYS101, and ENGEN103.

*Year 2:* COMPX201 or COMPX241, COMPX202 or COMPX242, COMPX203, COMPX204, COMPX222, COMPX223, CSMAX270 and STATS221.

*Year 3:* COMPX301, COMPX324, COMPX361 or COMPX307, either (15 points from any 300 level COMPX and one of COMPX374 or COMPX397) or COMPX398, COMPX205 or STATS321, COMPX310 and 15 points from any 300 level STATS paper.

To complete Data Analytics as a single major for the BCMS(Hons) or the BSc, students must gain 135 points from papers listed for Data Analytics, including 105 points above 100 level, and 60 points above 200 level. Students must complete COMPX101, either STATS111 or STATS121, COMPX223, STATS221, STATS226, and either COMPX305 or STATS321. Students in the BCMS(Hons) will also need to take at least 60 points in the subject of Data Analytics at 500 level, including STATS520.

To complete Data Analytics as part of a double major for the BCMS(Hons), BSc or other undergraduate degree, students must gain 120 points from papers listed for Data Analytics, including 90 points above 100 level, and 45 points above 200 level. Students must complete COMPX101, either STATS111 or STATS121, COMPX223, STATS221, STATS226, and either COMPX305 or STATS321. Students in the BCMS(Hons) will also need to take at least 60 points in the subject of their first major at 500 level, including STATS520 if Data Analytics is the first major.

To complete a minor in Data Analytics, students must complete 60 points from the papers listed for the Data Analytics major, including at least 30 points at 200 level or above consisting of at least one STATS-coded paper and at least one COMPX-coded paper.

Note: Students who commenced a major in Statistics in 2017 or prior are encouraged to contact the Division of Health, Engineering, Computing and Science for programme advice.



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  • 100 Level

    Code Paper Title Occurrence / Location
    CSMAX170Foundations in Computing and Mathematical Sciences20A (Hamilton), 20A (Tauranga), 20B (Hamilton), 20B (Waikato Pathways College), 20P (Hamilton) & 20P (Online)
    The objective of this paper is to provide students with the academic foundations for computing and mathematical sciences. The paper will cover the following areas: -Effective academic reasoning and communication -Information literacy and research skills -Academic integrity -Techniques and tools in the computing and mathematical sci...
    STATS111Statistics for Science20B (Hamilton) & 20B (Tauranga)
    An introductory paper in statistics that uses Microsoft Excel. Topics include the collection and presentation of data, basic principles of experimental design, hypothesis testing, regression and the analysis of categorical data.
  • 300 Level

    Code Paper Title Occurrence / Location
    COMPX305Practical Data Mining20B (Hamilton) & 20B (Tauranga)
    This paper introduces students to techniques for automatically finding and exploiting patterns in datasets, covering basic techniques applied in data analytics, data mining, machine learning, and big data. The well-known, locally-made Weka software will be used as the software environment for this paper.

2020 Catalogue of Papers information current as of : 14 August 2020 10:33am

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