Data Analytics (2018)
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 18A (Hamilton), 18A (Online), 18B (Hamilton) & 18C (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... STATS111 Statistics for Science 18A (Tauranga), 18B (Hamilton) & 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. STATS121 Introduction to Statistical Methods 18A (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 18A (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 18B (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 18A (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 18B (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. STATS260 Management Statistics 18C (Zhejiang University City College, Hangzhou China) An in-depth introduction to statistical thinking and concepts for managers. It includes understanding variability, problem solving methods, need for and use of data, analysing attribute or qualitative data, sampling estimation and margins of error, simple linear regression, multiple regression, forecasting and decision theory.
Code Paper Title 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. ENGG381 Engineering Statistics 18A (Hamilton) Aimed specifically at Engineering students, this paper covers statistical models, experimentation for quality design and control, process measurement and improvement, statistical process control and capability, and reliability. STAT321 Advanced Data Analysis 18B (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. STAT323 Design and Analysis of Experiments and Surveys 18A (Hamilton) This paper outlines the principles and practicalities of designing and analysing experiments and surveys, with emphasis on the design. STAT326 Computational Bayesian Statistics 18B (Hamilton) Bayesian inference can be performed on random samples from the posterior distribution, even when it is known only in the proportional form. A sample from a candidate distribution can be reshaped, or the sample can be drawn from a Markov chain that has the posterior as its long-run distribution (MCMC). In this paper, the emphasis is... STAT352 Statistics for Quality Improvement 18A (Hamilton) This paper covers the fundamentals of quality from a statistical point of view, statistical process control and capability, and process design and improvement, and includes the design of industrial experiments. STAT390 Directed Study 18A (Hamilton), 18B (Hamilton) & 18Y (Hamilton) No description available.
Code Paper Title Occurrence / Location STAT502 Advanced Quantitative Methods for Security and Crime Science 18B (Hamilton) This paper considers advanced quantitative techniques that can be used to identify and forecast crime event patterns. STAT521 Computational Statistics 18A (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. STAT522 Statistical Inference 18B (Hamilton) Statistical inference will be considered from both the classical and Bayesian perspectives. STAT525 Topics in Statistics 18A (Hamilton) No description available. STAT531 Multivariate Analysis 18B (Hamilton) This paper will develop skills in the use of statistical packages for data analysis and modelling and an understanding of the key inferential concepts of interval estimation, significance testing, and model selection. The emphasis is on observational rather than experimental data. STAT533 Study Design and Statistical Inference 18A (Hamilton) This paper outlines the principles and practicalities of designing and analysing experiments and surveys, with emphasis on the design. The concepts of statistical inference are presented, with an emphasis on estimation. STAT536 Bayesian Inference 18B (Hamilton) This paper extends material taught in STAT326 and studies advanced topics in Bayesian methods, including advanced MCMC methods, sequential MC methods, non-MCMC methods, and theoretical foundations of Bayesian inference.
2018 Catalogue of Papers information current as of : 19 July 2018 11:44am