Paradigm shifts in biology have changed the emphasis from single biomolecule studies to complex systems of biomolecules, cells and their interrelationships in ecosystems of life. Such an integrated understanding of cells, biomolecules and ecosystems is key to innovations in biology. Life relies on organisation, communication, responsiveness and regulation at every level. Understanding biological mechanisms, improving human health and addressing the impact of human activity are the great challenges of the 21st century. This unit will investigate life at levels ranging from cells, and biomolecule ecosystems, through to complex natural and human ecosystems. You will explore the importance of homeostasis in health and the triggers that lead to disease and death. You will learn the methods of cellular, biomolecular, microbial and ecological investigation that allow us to understand life and discover how expanding tools have improved our capacity to manage and intervene in ecosystems for our own health and organisms in the environment that surround and support us . You will participate in inquiry-led practicals that reinforce the concepts in the unit. By doing this unit you will develop knowledge and skills that will enable you to play a role in finding global solutions that will impact our lives.

Paradigm shifts in biology have changed the emphasis from single biomolecule studies to complex systems of biomolecules, cells and their interrelationships in ecosystems of life. Such an integrated understanding of cells, biomolecules and ecosystems is key to innovations in biology. Life relies on organisation, communication, responsiveness and regulation at every level. Understanding biological mechanisms, improving human health and addressing the impact of human activity are the great challenges of the 21st century. This unit will investigate life at levels ranging from cells, and biomolecule ecosystems, through to complex natural and human ecosystems. You will explore the importance of homeostasis in health and the triggers that lead to disease and death. You will learn the methods of cellular, biomolecular, microbial and ecological investigation that allow us to understand life and discover how expanding tools have improved our capacity to manage and intervene in ecosystems for our own health and organisms in the environment that surround and support us . This unit of study has the same overall structure as BIOL1007 but material is discussed in greater detail and at a more advanced level. The content and nature of these components may vary from year to year.

Paradigm shifts in biology have changed the emphasis from single biomolecule studies to complex systems of biomolecules, cells and their interrelationships in ecosystems of life. Such an integrated understanding of cells, biomolecules and ecosystems is key to innovations in biology. Life relies on organisation, communication, responsiveness and regulation at every level. Understanding biological mechanisms, improving human health and addressing the impact of human activity are the great challenges of the 21st century. This unit will investigate life at levels ranging from cells, and biomolecule ecosystems, through to complex natural and human ecosystems. You will explore the importance of homeostasis in health and the triggers that lead to disease and death. You will learn the methods of cellular, biomolecular, microbial and ecological investigation that allow us to understand life and intervene in ecosystems to improve health. The same theory will be covered as in the advanced stream but in this Special Studies Unit, the practical component is a research project. The research will be either a synthetic biology project investigating genetically engineered organisms or organismal/ecosystems biology. Students will have the opportunity to develop higher level generic skills in computing, communication, critical analysis, problem solving, data analysis and experimental design.

DATA1001 is a foundational unit in the Data Science major. The unit focuses on developing critical and statistical thinking skills for all students. Does mobile phone usage increase the incidence of brain tumours? What is the public's attitude to shark baiting following a fatal attack? Statistics is the science of decision making, essential in every industry and undergirds all research which relies on data. Students will use problems and data from the physical, health, life and social sciences to develop adaptive problem solving skills in a team setting. Taught interactively with embedded technology, DATA1001 develops critical thinking and skills to problem-solve with data. It is the prerequisite for DATA2002.

MATH1015 is designed to provide a thorough preparation in statistics for students in the Biological and Medical Sciences. It offers a comprehensive introduction to data analysis, probability and sampling, inference including t-tests, confidence intervals and chi-squared goodness of fit tests.

In a data-rich world, global citizens need to problem solve with data, and evidence based decision-making is essential is every field of research and work.
This unit equips you with the foundational statistical thinking to become a critical consumer of data. You will learn to think analytically about data and to evaluate the validity and accuracy of any conclusions drawn. Focusing on statistical literacy, the unit covers foundational statistical concepts, including the design of experiments, exploratory data analysis, sampling and tests of significance.

This unit is designed to provide a thorough preparation for further study in mathematics and statistics. It is a core unit of study providing three of the twelve credit points required by the Faculty of Science as well as a Junior level requirement in the Faculty of Engineering. This Advanced level unit of study parallels the normal unit MATH1005 but goes more deeply into the subject matter and requires more mathematical sophistication.

MATH1002 is designed to provide a thorough preparation for further study in mathematics and statistics. It is a core unit of study providing three of the twelve credit points required by the Faculty of Science as well as a Junior level requirement in the Faculty of Engineering.
This unit of study introduces vectors and vector algebra, linear algebra including solutions of linear systems, matrices, determinants, eigenvalues and eigenvectors.

This unit is designed to provide a thorough preparation for further study in mathematics and statistics. It is a core unit of study providing three of the twelve credit points required by the Faculty of Science as well as a Junior level requirement in the Faculty of Engineering. It parallels the normal unit MATH1002 but goes more deeply into the subject matter and requires more mathematical sophistication.

This unit is designed for science students who do not intend to undertake higher year mathematics and statistics. It establishes and reinforces the fundamentals of calculus, illustrated where possible with context and applications. Specifically, it demonstrates the use of (differential) calculus in solving optimisation problems and of (integral) calculus in measuring how a system accumulates over time. Topics studied include the fitting of data to various functions, the interpretation and manipulation of periodic functions and the evaluation of commonly occurring summations. Differential calculus is extended to functions of two variables and integration techniques include integration by substitution and the evaluation of integrals of infinite type.

MATH1013 is designed for science students who do not intend to undertake higher year mathematics and statistics.
In this unit of study students learn how to construct, interpret and solve simple differential equations and recurrence relations. Specific techniques include separation of variables, partial fractions and first and second order linear equations with constant coefficients. Students are also shown how to iteratively improve approximate numerical solutions to equations.

This unit is an introduction to Linear Algebra. Topics covered include vectors, systems of linear equations, matrices, eigenvalues and eigenvectors. Applications in life and technological sciences are emphasised.

This is an introductory statistics unit for students in the agricultural, life and environmental sciences. It provides the foundation for statistics and data science skills that are needed for a career in science and for further study in applied statistics and data science. In the first portion of the unit the emphasis is on describing data using statistical and graphical summaries, and probability models. In the second part the focus is on formal hypothesis testing on experimental data using statistical tests. The final part of the unit is on finding patterns in biological and environmental data, through the use of linear and non-linear functions. In the practicals the emphasis is on applying theory to analysing real datasets using the spreadsheet package Excel and the statistical software package R. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.

Technological advances in science, business, engineering has given rise to a proliferation of data from all aspects of our life. Understanding the information presented in these data is critical as it enables informed decision making into many areas including market intelligence and science. DATA2002 is an intermediate course in statistics and data sciences, focusing on learning data analytic skills for a wide range of problems and data. How should the Australian government measure and report employment and unemployment? Can we tell the difference between decaffeinated and regular coffee ? In this course, you will learn how to ingest, combine and summarise data from a variety of data models which are typically encountered in data science projects as well as reinforcing their programming skills through experience with statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skill to analyze various types of data in order to answer a scientific question. From this unit, you will develop knowledge and skills that will enable you to embrace data analytic challenges stemming from everyday problems.

This unit provides foundational skills essential for doing research in biology and for critically judging the research of others. We consider how biology is practiced as a quantitative, experimental and theoretical science. We focus on the underlying principles and practical skills you need to explore questions and test hypotheses, particularly where background variation (error) is inherently high. In so doing, the unit provides you with an understanding of how biological research is designed, analysed and interpreted using statistics. Lectures focus on sound experimental and statistical principles, using examples in ecology and other fields of biology to demonstrate concepts. In the practical sessions, you will design and perform, analyse (using appropriate statistical tools) and interpret your own experiments to answer research questions in topics relevant to your particular interest. This unit of study provides a suitable foundation for senior biology units of study.

The content of BIOL2922 will be based on BIOL2022 but qualified students will participate in alternative components at a more advanced level. The content and nature of these components may vary from year to year.

This unit builds on introductory 1st year statistics units and is targeted towards students in the agricultural, life and environmental sciences. It consists of two parts and presents, in an applied manner, the statistical methods that students need to know for further study and their future careers. In the first part the focus is on designed studies including both surveys and formal experimental designs. Students will learn how to analyse and interpret datasets collected from designs from more than than 2 treatment levels, multiple factors and different blocking designs. In the second part the focus is on finding patterns in data. In this part the students will learn to model relationships between response and predictor variables using regression, and find patterns in datasets with many variables using principal components analysis and clustering. This part provides the foundation for the analysis of big data. In the practicals the emphasis is on applying theory to analysing real datasets using the statistical software package R. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.

Experimental approaches to the study of biological systems are shifting from hypothesis driven to hypothesis generating research. Large scale experiments at the molecular scale are producing enormous quantities of data ("Big Data") that need to be analysed to derive significant biological meaning. For example, monitoring the abundance of tens of thousands of proteins simultaneously promises ground-breaking discoveries. In this unit, you will develop specific analytical skills required to work with data obtained in the biological and medical sciences. The unit covers quantitative analysis of biological systems at the molecular scale including modelling and visualizing patterns using differential equations, experimental design and data types to understand disease aetiology. You will also use methods to model cellular systems including metabolism, gene regulation and signalling. The practical program will enable you to generate data analysis workflows, and gain a deep understanding of the statistical, informatics and modelling tools currently being used in the field. To leverage multiple types of expertise, the computer lab-based practical component of this unit will be predominantly a team-based collaborative learning environment. Upon completion of this unit, you will have gained skills to find meaningful solutions to difficult biological and disease-related problems with the potential to change our lives.

This unit of study is designed to introduce students to the analysis of data they may face in their future careers, in particular data that are not well behaved. The data may be non-normal, there may be missing observations, they may be correlated in space and time or too numerous to analyse with standard models. The unit is presented in an applied context with an emphasis on correctly analysing authentic datasets, and interpreting the ouput. It begins with the analysis and design experiments based on the general linear model. In the second part, students will learn about the generalisation of the general linear model to accommodate non-normal data with a particular emphasis on the binomial and poisson distributions. In the third part linear mixed models will be introduced which provide the means to analyse datasets that do not meet the assumptions of independent and equal errors, for example data that is correlated in space and time. The units ends with an introduction to machine learning and predictive modelling. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.

This unit of study is designed to introduce students to the analysis of data they may face in their future careers, in particular data that are not well behaved. The data may be non-normal, there may be missing observations, they may be correlated in space and time or too numerous to analyse with standard models. The unit is presented in an applied context with an emphasis on correctly analysing authentic datasets, and interpreting the ouput. It begins with the analysis and design experiments based on the general linear model. In the second part, students will learn about the generalisation of the general linear model to accommodate non-normal data with a particular emphasis on the binomial and poisson distributions. In the third part linear mixed models will be introduced which provide the means to analyse datasets that do not meet the assumptions of independent and equal errors, for example data that is correlated in space and time. The units ends with an introduction to machine learning and predictive modelling. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.

This unit of study will focus on the high throughput methods for the analysis of gene structure and function (genomics) and the analysis of proteins (proteomics), which are at the forefront of discovery in the biomedical sciences. The course will concentrate on the hierarchy of gene-protein-structure-function through an examination of modern technologies built on the concepts of genomics versus molecular biology, and proteomics versus biochemistry. Technologies to be examined include DNA sequencing, nucleic acid and protein microarrays, two-dimensional gel electrophoresis of proteins, uses of mass spectrometry for high throughput protein identification, isotope tagging for quantitative proteomics, high-performance liquid chromatography, high-throughput functional assays, affinity chromatography and modern methods for database analysis. Particular emphasis will be placed on how these technologies can provide insight into the molecular basis of changes in cellular function under both physiological and pathological conditions as well as how they can be applied to biotechnology for the discovery of biomarkers, diagnostics, and therapeutics. The practical component is designed to complement the lecture course and will provide students with experience in a wide range of techniques used in proteomics and genomics.

This unit of study will focus on the high throughput methods for the analysis of gene structure and function (genomics) and the analysis of proteins (proteomics) which are at the forefront of discovery in the biomedical sciences. The course will concentrate on the hierarchy of gene-protein-structure-function through an examination of modern technologies built on the concepts of genomics versus molecular biology, and proteomics versus biochemistry. Technologies to be examined include DNA sequencing, nucleic acid and protein microarrays, two-dimensional gel electrophoresis of proteins, uses of mass spectrometry for high throughput protein identification, isotope tagging for quantitative proteomics, high-performance liquid chromatography, high-throughput functional assays, affinity chromatography and modern methods for database analysis. Particular emphasis will be placed on how these technologies can provide insight into the molecular basis of changes in cellular function under both physiological and pathological conditions as well as how they can be applied to biotechnology for the discovery of biomarkers, diagnostics, and therapeutics. The practical component is designed to complement the lecture course and will provide students with experience in a wide range of techniques used in proteomics and genomics.
The lecture component of this unit of study is the same as BCHM3092. Qualified students will attend seminars/practical classes in which more sophisticated topics in proteomics and genomics will be covered.

This unit will provide students an opportunity to apply the knowledge and practice the skills acquired in the prerequisite and qualifying units, in the context of designing and building a substantial bioinformatics application. Working in groups, students will carry out the full range of activities including requirements capture, analysis and design, coding, testing and documentation.

This unit is designed to impart knowledge and skills in spatial analysis and geographical information science (GISc) for decision-making in an environmental context. The lecture material will present several themes: principles of GISc, geospatial data sources and acquisition methods, processing of geospatial data and spatial statistics. Practical exercises will focus on learning geographical information systems (GIS) and how to apply them to land resource assessment, including digital terrain modelling, land-cover assessment, sub-catchment modelling, ecological applications, and soil quality assessment for decisions regarding sustainable land use and management. A three day field excursion during the mid-semester break will involve a day of GPS fieldwork at Arthursleigh University farm and two days in Canberra visiting various government agencies which research and maintain GIS coverages for Australia. By the end of this UoS, students should be able to: differentiate between spatial data and spatial information; source geospatial data from government and private agencies; apply conceptual models of spatial phenomena for practical decision-making in an environmental context; apply critical analysis of situations to apply the concepts of spatial analysis to solving environmental and land resource problems; communicate effectively results of GIS investigations through various means- oral, written and essay formats; and use a major GIS software package such as ArcGIS.

This unit of study is designed to allow students to examine advanced hydrological modeling focusing on catchment level responses and uncertainty. Students will learn how to develop their own simulation model of catchment hydrological processes in R and using SWAT and review the possibilities and impossibilities of using simulation models for catchment management. Students will further investigate landuse change impacts and climate change impacts the variability in hydrological responses. At the end of this unit, students will be able to calibrate and evaluate a catchment model, articulate advantages and disadvantages of using simulation models for catchment management, justify the choice of a simulation model for a particular catchment management problem, identify issues in relation to uncertainty in water quality and quantity The students will gain research and inquiry skills through research based assignments, information literacy and communication skills through laboratory reports and a presentation and personal and intellectual autonomy through working in groups.

Biotechnological advances have given rise to an explosion of original and shared public data relevant to human health. These data, including the monitoring of expression levels for thousands of genes and proteins simultaneously, together with multiple databases on biological systems, now promise exciting, ground-breaking discoveries in complex diseases. Critical to these discoveries will be our ability to unravel and extract information from these data. In this unit, you will develop analytical skills required to work with data obtained in the medical and diagnostic sciences. You will explore clinical data using powerful, state of the art methods and tools. Using real data sets, you will be guided in the application of modern data science techniques to interrogate, analyse and represent the data, both graphically and numerically. By analysing your own real data, as well as that from large public resources you will learn and apply the methods needed to find information on the relationship between genes and disease. Leveraging expertise from multiple sources by working in team-based collaborative learning environments, you will develop knowledge and skills that will enable you to play an active role in finding meaningful solutions to difficult problems, creating an important impact on our lives.

This unit has three distinct but related components: Multivariate analysis; sampling and surveys; and generalised linear models. The first component deals with multivariate data covering simple data reduction techniques like principal components analysis and core multivariate tests including Hotelling's T^2, Mahalanobis' distance and Multivariate Analysis of Variance (MANOVA). The sampling section includes sampling without replacement, stratified sampling, ratio estimation, and cluster sampling. The final section looks at the analysis of categorical data via generalized linear models. Logistic regression and log-linear models will be looked at in some detail along with special techniques for analyzing discrete data with special structure.

This unit is an Advanced version of STAT3014. There will be 3 lectures per week in common with STAT3014. The unit will have extra lectures focusing on multivariate distribution theory developing results for the multivariate normal, partial correlation, the Wishart distribution and Hotelling's T^2. There will also be more advanced tutorial and assessment work associated with this unit.