Data Analytics (2022)
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) - see the Prescriptions for the BCompSc.
Data Analytics is available as a first major for the Bachelor of Climate Change (BCC), 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 single major for the BCC, 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 or ENGEN103, STATS111 or STATS121, COMPX223, STATS221, STATS222, and 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 BCC, 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 or ENGEN103, STATS111 or STATS121, COMPX223, STATS221, STATS222, and 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.
On this page
- Prescriptions for the BCompSc
- Prescriptions for the GradCert(DataA) and GradDip(DataA)
- 100 Level
- 200 Level
- 300 Level
- 500 Level
Prescriptions for the BCompSc
To complete Data Analytics as a specified programme for the BCompSc, students must take the following papers:
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.
Prescriptions for the GradCert(DataA) and GradDip(DataA)
A Graduate Certificate and Graduate Diploma are available to graduates who have not included Data Analytics at an advanced level in their first degree.
For further details, contact the Division of Health, Engineering, Computing and Science Office.
Code Paper Title Points Occurrence / Location COMPX101 Introduction to Programming 15.0 22A (Hamilton), 22A (Online), 22B (Hamilton), 22B (Internet Waikato College) & 22X (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 15.0 22A (Hamilton), 22A (Internet Waikato College), 22A (Tauranga) & 22B (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 mathematical sci... ENGEN103 Engineering Computing 15.0 22A (Hamilton), 22A (Tauranga) & 22G (Hamilton) This paper introduces computer programming in languages such as the MATLAB language. It provides the basis for the programming skills required in more advanced papers. STATS111 Statistics for Science 15.0 22B (Hamilton), 22B (Tauranga) & 22C (Internet Waikato College) 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. STATS121 Introduction to Statistical Methods 15.0 22A (Hamilton) & 22A (Online) 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 Points Occurrence / Location COMPX223 Database Practice and Experience 15.0 22A (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 15.0 22B (Hamilton) & 22B (Tauranga) The paper provides students with an understanding of scientific and culture-specific perspectives on issues in computing and mathematical sciences. Students will learn how these perspectives can be applied in diverse cultural, international, ethical, and professional contexts. STATS221 Statistical Data Analysis 15.0 22A (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. STATS222 Principles of Probability and Statistics 15.0 22B (Hamilton) This paper introduces the theoretical background that underpins modern probability and statistics. Topics include discrete probability and mathematical statistics from a frequentist and Bayesian viewpoint.
Code Paper Title Points Occurrence / Location COMPX310 Machine Learning 15.0 22B (Hamilton) & 22B (Tauranga) This paper introduces Machine Learning (ML) which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while processing large datasets. This paper will use standard Python-based ML toolkits to teach the fundamentals of ML. STATS321 Advanced Data Analysis 15.0 22A (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 Stochastic Processes 15.0 22A (Hamilton) This paper introduces students to probability theory and stochastic processes. It covers formally the theoretical foundations of probability, random variables, statistics, stochastic processes and Markov chains. STATS323 Design and Analysis of Experiments and Surveys 15.0 22A (Hamilton) This paper outlines the principles and practicalities of designing and analysing experiments and surveys, with emphasis on the design. STATS326 Computational Bayesian Statistics 15.0 22B (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 15.0 22A (Hamilton) & 22B (Hamilton) Students carry out an independent research project on an approved topic under staff supervision. STATS391 Undergraduate Research Project 30.0 22A (Hamilton), 22B (Hamilton), 22D (Hamilton) & 22X (Hamilton) Students carry out an independent research project on an approved topic under staff supervision. STATS397 Work-Integrated Learning Directed Study 15.0 22X (Hamilton) Students carry out an independent work-related project on an approved topic under staff supervision.
Code Paper Title Points Occurrence / Location COMPX521 Machine Learning Algorithms 15.0 22A (Hamilton) This paper exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms. STATS505 Optimization 15.0 22B (Hamilton) This paper teaches students a toolbox of optimization techniques. It covers traditional approaches, such as linear programming and Newton's method, and heuristic methods such as simulated annealing and evolutionary algorithms. STATS520 Dissertation 45.0 22X (Hamilton) A directed investigation and report on an approved project or study topic. STATS525 Topics in Statistics 30.0 22A (Hamilton) This paper will discuss advanced topics in statistics. The exact topics covered could change subject to the preference and research expertise of the academic staff. Students preferences may also be taken into account. STATS590 Directed Study 30.0 22B (Hamilton) Students have the opportunity to pursue a topic of their own interest under the guidance of academic staff. STATS591 Dissertation 30.0 22X (Hamilton) A report on the findings of a theoretical or empirical investigation. STATS592 Dissertation 60.0 22X (Hamilton) A report on the findings of a theoretical or empirical investigation. STATS593 Statistics Thesis 90.0 22X (Hamilton) An externally examined piece of written work that reports on the findings of supervised research. STATS594 Statistics Thesis 120.0 22X (Hamilton) An externally examined piece of written work that reports on the findings of supervised research.
2022 Catalogue of Papers information current as of : 22 October 2021 7:02pm