Data Analytics (2019)
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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Code Paper Title Occurrence / Location COMPX101 Introduction to Computer Science 19A (Hamilton), 19A (Online), 19B (Hamilton) & 19C (Zhejiang University City College, Hangzhou China) This paper introduces computer programming in C# - the exciting challenge of creating software and designing artificial worlds within the computer. It also covers concepts such as the internals of the home computer, the history and future of computers, cyber security, computer gaming, databases, mobile computing and current researc... CSMAX170 Foundations in Computing and Mathematical Sciences 19A (Hamilton) & 19B (Hamilton) 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 mathematica... STATS111 Statistics for Science 19B (Hamilton) & 19B (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. STATS121 Introduction to Statistical Methods 19A (Hamilton) An introduction to statistical data collection and analysis. Topics include general principles for statistical problem solving; some practical examples of statistical inference; and the study of relationships between variables using regression analysis.
Code Paper Title Occurrence / Location COMPX223 Database Practice and Experience 19A (Hamilton) This paper approaches the subject of databases from a practical perspective - how do I create a database and how do I retrieve/update data. Both aspects are heavily addressed in this paper. Database creation and querying, using SQL, will be introduced in lectures as you will master practical skills associated with a commercial Data... CSMAX270 Cultural Perspectives for Computing and Mathematical Sciences 19B (Hamilton) The paper provides students with an understanding of scientific and culture-specific perspectives on computing and mathematical science issues and the ability to apply these in diverse contexts. STATS221 Statistical Data Analysis 19A (Hamilton) This paper introduces students to the R programming language which is used to investigate a collection of real data sets. Analysis of variance, multiple regression, non parametric methods and time series are covered. STATS226 Bayesian Statistics 19B (Hamilton) This paper introduces statistical methods from a Bayesian perspective, which gives a coherent approach to the problem of revising beliefs given relevant data. It is particularly relevant for data analytics, statistics, mathematics and computer science.
Code Paper Title Occurrence / Location COMPX305 Practical Data Mining 19B (Hamilton) & 19B (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. STATS321 Advanced Data Analysis 19B (Hamilton) This paper covers the use of statistical packages for data analysis and modelling. The emphasis is on observational rather than experimental data. The topics covered are regression modelling and its generalisations, and multivariate analysis. STATS322 Probability and Mathematical Statistics 19A (Hamilton) This paper introduces students to probability theory and the mathematical theory of statistics. It covers formally the theoretical foundations of probability, random variables, likelihood and estimation, statistics, and statistical inference. STATS323 Design and Analysis of Experiments and Surveys 19A (Hamilton) This paper outlines the principles and practicalities of designing and analysing experiments and surveys, with emphasis on the design. STATS326 Computational Bayesian Statistics 19B (Hamilton) Bayesian approach has the potential to model any complex real life problem. In practice, Bayesian methods are implemented using various computational algorithms. This paper introduces the basics of some of the most widely used computational methods, viz the ABC method and the MCMC methods. STATS390 Directed Study 19A (Hamilton) & 19B (Hamilton) Students carry out an independent research project on an approved topic under staff supervision. STATS391 Undergraduate Research Project 19A (Hamilton), 19B (Hamilton), 19C (Hamilton) & 19Y (Hamilton) Students carry out an independent research project on an approved topic under staff supervision.
Code Paper Title Occurrence / Location STATS521 Computational Statistics 19A (Hamilton) This paper covers maximum likelihood estimation, and the fitting of advanced regression models including non-linear models, mixture models and their generalisations. It will take a practical approach stressing the use of R packages and WinBugs or OpenBugs Bayesian software. STATS522 Statistical Inference 19B (Hamilton) Statistical inference will be considered from both the classical and Bayesian perspectives. STATS525 Topics in Statistics 19C (Hamilton) No description available. STATS590 Directed Study 19C (Hamilton) Students have the opportunity to pursue a topic of their own interest under the guidance of academic staff. STATS591 Dissertation 19C (Hamilton) A report on the findings of a theoretical or empirical investigation. STATS592 Dissertation 19C (Hamilton) A report on the findings of a theoretical or empirical investigation. STATS593 Statistics Thesis 19C (Hamilton) An externally examined piece of written work that reports on the findings of supervised research. STATS594 Statistics Thesis 19C (Hamilton) An externally examined piece of written work that reports on the findings of supervised research.
2019 Catalogue of Papers information current as of : 11 October 2018 3:33pm