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 first major for the Bachelor of Computing and Mathematical Sciences with Honours and the Bachelor of Science. Data Analytics may also be taken as a second major, subject to academic approval of the Faculty in which the student is enrolled.
To complete Data Analytics as a single major for 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 either STATS111 or STATS121, COMPX101, STATS221, STATS226, COMPX223, and either STATS321 or COMPX305. 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 either STATS111 or STATS121, COMPX101, STATS221, STATS226, COMPX223, and either STATS321 or COMPX305. 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.
Note: Students who commenced a major in Statistics in 2017 or prior are encouraged to contact the Faculty of Computing and Mathematical Sciences for programme advice.
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||Occurrence / Location
|STATS111||Statistics for Science||18A (Hamilton), 18A (Tauranga), 18B (Hamilton), 18B (Online) & 18B (Tauranga)|
|This paper provides a first course in statistics for students in the Faculty of Science and Engineering. Microsoft Excel is used throughout. Topics include the collection and presentation of data, basic principles of experimental design, hypothesis testing, regression and the analysis of categorical data.|
||Occurrence / Location
|COMP321||Practical Data Mining||18B (Hamilton) & 18B (Tauranga)|
|This paper is a practical introduction to data mining. It covers important aspects of the data mining process such as feature selection, model building, parameter tuning and final evaluation.|