Quantitative Life Sciences Descriptions

Errata
Item Errata Date
1.

Prerequisites have been removed for the following units:

MATH1011 Applications of Calculus
MATH1013 Mathematical Modelling

12/12/2018
2.

The following unit has been cancelled for 2019:

QBIO3001 Molecular Systems Biology

21/2/2019

QUANTITATIVE LIFE SCIENCES

Advanced coursework and projects will be available in 2020 for students who complete this major.

Quantitative Life Sciences major

A major in Quantitative Life Sciences requires 48 credit points from this table including:
(i) 6 credit points of 1000-level selective units
(ii) 6 credit points of 1000-level core units
(iii) 12 credit points of 2000-level selective units
(iv) 6 credit points of 3000-level methodology units
(v) 6 credit points of 3000-level core interdisciplinary project units
(vi) 12 credit points of 3000-level selective specialisation or interdisciplinary project selective units

Quantitative Life Sciences minor

A minor in Quantitative Life Sciences requires 36 credit points from this table including:
(i) 6 credit points of 1000-level selective units
(ii) 6 credit points of 1000-level core units
(iii) 12 credit points of 2000-level selective units
(iv) 6 credit points of 3000-level methodology units
(v) 6 credit points of 3000-level selective specialisation units

Units of study

The units of study are listed below.

1000-level units of study

Core
BIOL1007 From Molecules to Ecosystems

Credit points: 6 Teacher/Coordinator: Dr Emma Thompson Session: Semester 2 Classes: Two lectures per week and online material and 12 x 3-hour practicals Prohibitions: BIOL1907 or BIOL1997 Assumed knowledge: HSC Biology. Students who have not completed HSC Biology (or equivalent) are strongly advised to take the Biology Bridging Course (offered in February). Assessment: Quizzes (10%), communication assessments (40%), skills tests (10%), summative final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Please see unit outline on LMS
BIOL1907 From Molecules to Ecosystems (Advanced)

Credit points: 6 Teacher/Coordinator: Dr Claudia Keitel Session: Semester 2 Classes: Two lectures per week and online material and 12 x 3-hour practicals Prohibitions: BIOL1007 or BIOL1997 Assumed knowledge: 85 or above in HSC Biology or equivalent Assessment: Quizzes (10%), communication assessments (40%), skills tests (10%), summative exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
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.
Textbooks
Please see unit outline on LMS
BIOL1997 From Molecules to Ecosystems (SSP)

Credit points: 6 Teacher/Coordinator: Dr Emma Thompson Session: Semester 2 Classes: Two lectures per week and online material Prohibitions: BIOL1007 or BIOL1907 Assumed knowledge: 90 or above in HSC Biology or equivalent Assessment: One 2-hour exam (40%), project report which includes written report and presentation (60%) Practical field work: As advised and required by the project; approximately 30-36 hours of research project in the laboratory or field Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
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 a synthetic biology project investigating genetically engineered organisms. Students will have the opportunity to develop higher level generic skills in computing, communication, critical analysis, problem solving, data analysis and experimental design.
Textbooks
Please see unit outline on LMS
Selective
DATA1001 Foundations of Data Science

Credit points: 6 Teacher/Coordinator: A/Prof Qiying Wang Session: Semester 1,Semester 2 Classes: 3x1-hr lectures; 1x2-hr lab/wk Prohibitions: DATA1901 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021 or STAT1022 Assessment: RQuizzes (10%); 3 x projects (30%); final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Statistics, (4th Edition), Freedman Pisani Purves (2007)
DATA1901 Foundations of Data Science (Adv)

Credit points: 6 Teacher/Coordinator: A/Prof Qiying Wang Session: Semester 1,Semester 2 Classes: Lecture 3 hrs/week + Computer lab 2 hr/week Prohibitions: MATH1905 or ECMT1010 or ENVX2001 or BUSS1020 or DATA1001 or MATH1115 Assumed knowledge: An ATAR of 95 or more Assessment: RQuizzes (10%), Projects (30%), Final Exam (60%). Mode of delivery: Normal (lecture/lab/tutorial) day
DATA1901 is an advanced level unit (matching DATA1001) that is foundational to the new major in Data Science. 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 and masterclasses, DATA1901 develops critical thinking and skills to problem-solve with data at an advanced level. By completing this unit you will have an excellent foundation for pursuing data science, whether directly through the data science major, or indirectly in whatever field you major in. The advanced unit has the same overall concepts as the regular unit but material is discussed in a manner that offers a greater level of challenge and academic rigour.
Textbooks
All learning materials will be on Canvas. In addition, the textbook is Statistics (4th Edition) { Freedman, Pisani, and Purves (2007), which is available in 3 forms: 1) E-text $65 (www.wileydirect.com.au/buy/statistics-4th-international-student-edition/), 2) hard copy (Co-op Bookshop), and 3) the Library.
MATH1005 Statistical Thinking with Data

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1,Semester 2,Summer Main Classes: 2x1-hr lectures; 1x1-hr lab/wk Prohibitions: MATH1015 or MATH1905 or STAT1021 or STAT1022 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 Assumed knowledge: HSC Mathematics. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Assessment: RQuizzes(10%); projects (25%); final exam (65%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Statistics, (4th Edition), Freedman Pisani Purves (2007)
MATH1905 Statistical Thinking with Data (Advanced)

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prohibitions: MATH1005 or MATH1015 or STAT1021 or STAT1022 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 or DATA1001 or DATA1901 Assumed knowledge: (HSC Mathematics Extension 2) OR (90 or above in HSC Mathematics Extension 1) or equivalent Assessment: 2 x quizzes (20%); 2 x assignments (10%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
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.
Textbooks
A Primer of Statistics (4th edition), M C Phipps and M P Quine, Prentice Hall, Australia (2001)
MATH1002 Linear Algebra

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1,Summer Main Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prohibitions: MATH1012 or MATH1014 or MATH1902 Assumed knowledge: HSC Mathematics or MATH1111. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Assessment: Online quizzes (20%); 4 x assignments (15%); final exam (65%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Linear Algebra: A Modern Introduction, (4th edition), David Poole
MATH1902 Linear Algebra (Advanced)

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prohibitions: MATH1002 or MATH1012 or MATH1014 Assumed knowledge: (HSC Mathematics Extension 2) OR (90 or above in HSC Mathematics Extension 1) or equivalent Assessment: Online quizzes (10%); 4 x assignments (20%); final exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
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.
Textbooks
As set out in the Junior Mathematics Handbook
MATH1011 Applications of Calculus

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 1,Summer Main Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1001 or MATH1901 or MATH1906 or BIOM1003 or ENVX1001 or MATH1021 or MATH1921 or MATH1931 Assumed knowledge: HSC Mathematics. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Please note: this unit does not normally lead to a major in Mathematics or Statistics or Financial Mathematics and Statistics. Assessment: 2 x quizzes (30%); 2 x assignments (5%); final exam (65%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Applications of Calculus (Course Notes for MATH1011)
MATH1013 Mathematical Modelling

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 2,Summer Main Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: NSW HSC 2 unit Mathematics or equivalent or a credit or above in MATH1111 Prohibitions: MATH1003 or MATH1903 or MATH1907 or MATH1023 or MATH1923 or MATH1933 Assumed knowledge: HSC Mathematics or a credit or higher in MATH1111. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Please note: this unit does not normally lead to a major in Mathematics or Statistics or Financial Mathematics and Statistics. Assessment: 2 x quizzes (30%); 2 x assignments (5%); online homework (10%); final exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Mathematical Modelling, Leon Poladian, School of Mathematics and Statistics, University of Sydney (2011)
MATH1014 Introduction to Linear Algebra

Credit points: 3 Teacher/Coordinator: A/Prof Sharon Stephen Session: Semester 2,Summer Main Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prohibitions: MATH1012 or MATH1002 or MATH1902 Assumed knowledge: HSC Mathematics or MATH1111. Students who have not completed HSC Mathematics (or equivalent) are strongly advised to take the Mathematics Bridging Course (offered in February). Please note: this unit does not normally lead to a major in Mathematics or Statistics or Financial Mathematics and Statistics. Assessment: 2 x quizzes (30%); 2 x assignments (5%); final exam (65%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
A First Course in Linear Algebra (3rd edition), David Easdown, Pearson Education (2010)
ENVX1002 Introduction to Statistical Methods

Credit points: 6 Teacher/Coordinator: A/Prof Thomas Bishop Session: Semester 1 Classes: 3 hours per week of lectures; 2 hours per week of computer tutorials Prohibitions: ENVX1001 or MATH1005 or MATH1905 or MATH1015 or MATH1115 or DATA1001 or DATA1901 or BUSS1020 or STAT1021 or ECMT1010 Assessment: Assignments, quizzes, presentation, exam Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Available as a degree core unit only in the Agriculture, Animal and Veterinary Bioscience, and Food and Agribusiness streams
This is an introductory data science 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. The unit focuses on developing critical and statistical thinking skills for all students. It has 4 modules; exploring data, modelling data, sampling data and making decisions with 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, ENVX1002 develops critical thinking and skills to problem-solve with data.
Textbooks
Statistics, Fourth Edition, Freedman Pisani Purves

2000-level units of study

Selective
DATA2002 Data Analytics: Learning from Data

Credit points: 6 Teacher/Coordinator: A/Prof Jennifer Chan Session: Semester 2 Classes: 3x1-hr lecture; 1x2-hr computer laboratory/wk Prerequisites: [DATA1001 or ENVX1001 or ENVX1002] or [MATH10X5 and MATH1115] or [MATH10X5 and STAT2011] or [MATH1905 and MATH1XXX (except MATH1XX5)] or [BUSS1020 or ECMT1010 or STAT1021] Prohibitions: STAT2012 or STAT2912 or DATA2902 Assumed knowledge: Basic Linear Algebra and some coding Assessment: Computer practicals (10%), online quizzes (15%), group work assignment and presentation (15%), and final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Technological advances in science, business, engineering have 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 unit 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 unit, 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 your programming skills through experience with a statistical programming language. You will also be exposed to the concept of statistical machine learning and develop the skill to analyse 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.
DATA2902 Data Analytics: Learning from Data (Adv)

Credit points: 6 Teacher/Coordinator: A/Prof Jennifer Chan Session: Semester 2 Classes: Lecture 3 hrs/week + computer tutorial 2 hr/week Prerequisites: A mark of 65 or above in any of the following (DATA1001 or DATA1901 or ENVX1001 or ENVX1002) or (MATH10X5 and MATH1115) or (MATH10X5 and STAT2011) or (MATH1905 and MATH1XXX [except MATH1XX5]) or (QBUS1020 or ECMT1020 or STAT1021) Prohibitions: STAT2012 or STAT2912 or DATA2002 Assumed knowledge: Basic linear algebra and some coding for example MATH1014 or MATH1002 or MATH1902 and DATA1001 or DATA1901 Assessment: Computer practicals in-class (10%), Online quizzes (15%), Project group work assignment (5%), Project group work presentation (10%), Final exam (60%). Mode of delivery: Normal (lecture/lab/tutorial) day
Technological advances in science, business, and engineering have 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. DATA2902 is an intermediate unit in statistics and data sciences, focusing on learning advanced 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 unit, 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 analyse 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.
BIOL2022 Biology Experimental Design and Analysis

Credit points: 6 Teacher/Coordinator: A/Prof Clare McArthur Session: Semester 2 Classes: Two lectures per week and one 3-hour practical per week. Prerequisites: 6cp from (BIOL1XXX or MBLG1XXX or ENVX1001 or ENVX1002 or DATA1001 or MATH1XX5) Prohibitions: BIOL2922 or BIOL3006 or BIOL3906 or ENVX2001 Assumed knowledge: BIOL1XXX or MBLG1XXX Assessment: Practical reports/presentations (60%), one 2-hour exam (40%). Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Recommended: Ruxton, G. and Colegrave, N. 2016. Experimental design for the life sciences. 4th Ed. Oxford University Press
BIOL2922 Biol Experimental Design and Analysis Adv

This unit of study is not available in 2019

Credit points: 6 Teacher/Coordinator: A/Prof Clare McArthur Session: Semester 2 Classes: Two lectures per week and one 3-hour practical per week. Prerequisites: [An annual average mark of at least 70 in the previous year] and [6cp from (BIOL1XXX or MBLG1XXX or ENVX1001 or ENVX1002 or DATA1001 or MATH1XX5)] Prohibitions: BIOL2022 or BIOL3006 or BIOL3906 Assumed knowledge: BIOL1XXX or MBLG1XXX Assessment: Practical reports/presentations (60%), one 2-hour exam (40%). Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Required: Ruxton, G. and Colegrave, N. 2016. Experimental design for the life sciences. 4th Ed. Oxford
ENVX2001 Applied Statistical Methods

Credit points: 6 Teacher/Coordinator: Dr Floris Van Ogtrop Session: Semester 1 Classes: Two 1-hour lectures per week, one 3-hour computer practical per week Prerequisites: [6cp from (ENVX1001 or ENVX1002 or BIOM1003 or MATH1011 or MATH1015 or DATA1001 or DATA1901)] OR [3cp from (MATH1XX1 or MATH1906 or MATH1XX3 or MATH1907) and an additional 3cp from (MATH1XX5)] Assessment: One exam during the exam period (50%),three reports (10% each), ten online quizzes (2% each) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Available as a degree core unit only in the Agriculture, Animal and Veterinary Bioscience, and Food and Agribusiness streams
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.
Textbooks
No textbooks are recommended but useful reference books are:
QBIO2001 Molecular Systems Biology

Credit points: 6 Teacher/Coordinator: Dr Edward Hancock Session: Semester 1 Classes: Two 1-hour lectures; one 3-hour practical session on a weekly basis Assumed knowledge: Basic concepts in metabolism; protein synthesis; gene regulation; quantitative and statistical skills Assessment: One 3-hour final exam (50%), three 45-minute quizzes (20%), one 5-minute presentation (10%), laboratory assessment and practical book (20%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
An Introduction to Systems Biology: Design Principles of Biological Circuits, Uri Alon, (Chapman and Hall/CRC, 2007). Systems Biology, Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, and Ralf Herwig, (Wiley-Blackhall, 2009). Molecular biology of the cell, Alberts B et al (6th edition, Garland Science, 2015) Discovering Statistics Using R, Andy Field (2012, SAGE Publications Ltd). Computational and Statistical Methods for Protein Quantitation by Mass Spectrometry, Martens L et al (Wiley, 2013)

3000-level units of study

Methodology units
ENVX3002 Statistics in the Natural Sciences

Credit points: 6 Teacher/Coordinator: A/Prof Peter Thomson Session: Semester 1 Classes: One 2-hour workshop per week, one 3-hour computer practical per week Prerequisites: ENVX2001 or BIOM2001 or STAT2X12 or BIOL2X22 or DATA2002 or QBIO2001 Assessment: One computer-based exam during the exam period (50%), assessment tasks focusing on analysing and interpreting real datasets (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Interdisciplinary Unit
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 output. 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.
QBIO3001 Molecular Systems Biology

Credit points: 6 Teacher/Coordinator: Dr Edward Hancock Session: Semester 1 Classes: 2x1hr lectures, 1x3 hr lab Prerequisites: Completion of 72cp of units of study, including QBIO2001 and 6cp from (GEGE2X01 or GENE2002 or AVBS2005 or BCMB2X01 or BCHM2X72 or MEDS2003) Assessment: Quiz in Tutorial/Lab x 2 (10%+5%) = 15%, Workshop on Module 1 = 25%, Presentation = 15%, Final Exam = 45% Mode of delivery: Normal (lecture/lab/tutorial) day
Systems biology is the study of complex biological systems whose behaviour involves more than the sum of their isolated biological parts. Modern approaches to systems biology use a broad system-level analysis incorporating multiple levels of data integration and mechanistic modelling. This unit is designed for students to gain a deep understanding of data integration from large-scale Omics experimentation as well as mechanistic modelling with differential equations. The unit provides a comprehensive knowledge of large-scale Omics-based biological systems analysis, including advanced statistical methods for systems biology, Omics data visualisation, experimental methods in Omics research, data integration, and network-based analysis. In addition, the unit covers diverse aspects of mechanistic modelling of biochemical pathways with differential equations, including analysis of models of gene regulatory networks, signalling networks and metabolic regulation. In the practical program you will generate data analysis workflows and thereby 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 a predominantly team-based collaborative learning environment.
QBIO3901 to be developed for offering in 2020
Core Interdisciplinary Project
The unit of study QBIO3888 is not available in 2019
QBIO3888 Quantitative Biology Interdisciplinary Unit

Credit points: 6 Teacher/Coordinator: A/Prof Brian Jones Session: Semester 2 Classes: Lectures 2 hrs/week; Practical 3 hrs/week Prerequisites: 12cp from (ENVX2001 or BIOL2X22 or GEGE2X01 or AVBS2005 or BCMB2X01 or MEDS2003 or QBIO2001 or DATA2X02 or BCHM2X72 or GENE2002 or SOIL2005 or AGRI2001 or ENSC2001 or BIOL2X24 or BIOL2032) Assessment: Documentation/Laboratory eNotebooks (15%), Project report (50%), Team work participation evaluation (15%), Project oral presentation (20%). Mode of delivery: Normal (lecture/lab/tutorial) day
Working effectively across disciplines has become an intrinsic part of most professional and research environments. In this unit, you will develop solutions to complex real world problems. The unit is designed to foster your abilities in your chosen discipline, and for you to work towards a project goal in a supported, collaborative team environment with students from other disciplines. This unit offers you the opportunity to work on a project in one of three key areas: (1) 'omics-based modelling; (2) synthetic biology; or (3) ecosystem modelling. The projects will focus on the development of: novel clinical biomarkers; the development of innovative biological machines; or the discovery of mechanisms underlying ecosystem function. You will identify a problem, develop novel solutions and communicate your ?ndings to a diverse audience. Combined with your disciplinary skills, the ability to work with others to develop solutions to complex problems is highly valued by employers.
Specialisation units
ENVX3002 Statistics in the Natural Sciences

Credit points: 6 Teacher/Coordinator: A/Prof Peter Thomson Session: Semester 1 Classes: One 2-hour workshop per week, one 3-hour computer practical per week Prerequisites: ENVX2001 or BIOM2001 or STAT2X12 or BIOL2X22 or DATA2002 or QBIO2001 Assessment: One computer-based exam during the exam period (50%), assessment tasks focusing on analysing and interpreting real datasets (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Interdisciplinary Unit
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 output. 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.
BCHM3092 Proteomics and Functional Genomics

Credit points: 6 Teacher/Coordinator: Prof Stuart Cordwell Session: Semester 2 Classes: Two 1-hour lectures per week and one 3-hour practical per week. Prerequisites: [12cp from (BCHM2X71 or BCHM2X72 or BCMB2X01 or BCMB2X02 or DATA2002 or ENVX2001 or BIOL2X22 or GEGE2X01 or MBLG2X71 or QBIO2001)] OR [BMED2401 and BMED2405 and 6cp from (BCHM2X71 or BCMB2X02 or MBLG2X71)] Prohibitions: BCHM3992 Assessment: One 2.5-hour exam (theory and theory of prac 70%), in-semester (practical work and assignments 30%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: BMedSc degree students: You must have successfully completed BMED2401 and an additional 12cp from BMED240X before enrolling in this unit.
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.
BCHM3992 Proteomics and Functional Genomics (Adv)

Credit points: 6 Teacher/Coordinator: Prof Stuart Cordwell Session: Semester 2 Classes: Two 1-hour lectures per week and one 3-hour practical per week. Prerequisites: [An average mark of 75 or above in 12cp from (BCHM2X71 or BCHM2X72 or BCMB2X01 or BCMB2X02 or DATA2002 or ENVX2001 or BIOL2X22 or GEGE2X01 or MBLG2X71 or QBIO2001)] OR [BMED2401 and a mark of 75 or above in BMED2405 and a mark of 75 or above in 6cp from (BCHM2X71 or BCMB2X02 or MBLG2X71)] Prohibitions: BCHM3092 Assessment: One 2.5-hour exam (theory and theory of prac 70%), in-semester (practical work and assignments 30%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: BMedSc degree students: You must have successfully completed BMED2401 and an additional 12cp from BMED240X before enrolling in this unit.
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.
ENVX3001 Environmental GIS

Credit points: 6 Teacher/Coordinator: Dr Bradley Evans Session: Semester 2 Classes: Three-day field trip, (two lectures and two practicals per week) Prerequisites: 6cp from (ENVI1003 or AGEN1002) or 6cp from GEOS1XXX or 6cp from BIOL1XXX or GEOS2X11 Assessment: 15-minute presentation (10%), 3500 word prac report (35%), 1500 word report on trip excursion (15%), 2-hour exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
Burrough, P.A. and McDonnell, R.A. 1998. Principles of Geographic Information Systems. Oxford University Press: Oxford.
ENVX3003 Hydrological Monitoring and Modelling

Credit points: 6 Teacher/Coordinator: A/Prof Willem Vervoort Session: Semester 2 Classes: lecture 2hrs/week, computer practical 3hrs/wk Prerequisites: Completion of 72 credit points of units of study Prohibitions: LWSC3007 Assumed knowledge: SOIL2005 or GEOS2116 or ENVI1003 or GEOS1001 or ENSC2001 Assessment: Three individual assignments (25%), group based field report (25%), 2 hr final exam (50%). Practical field work: 3 days fieldwork near Cootamundra Mode of delivery: Normal (lecture/lab/tutorial) day
Globally, and in Australia in particular, water quantity and quality problems are growing due to increasing human use and a changing climate. In this unit, you will engage with field-based and quantitative problems related to water quantity and quality. This includes a multi-day field trip to regional NSW to collect samples and engage with field-based activities. During these activities, you will develop field-based skills for collection of hydrological data. The data will be used later in the unit to analyse and map the water quantity and quality issues in the catchment, relating this to landscape, management and climate. The second part of the unit focusses on developing an insight into model building, model calibration, validation and sensitivity analysis. It links back to the field experience by using long-term data collected by previous student cohorts and focussing on the identified landscape issues. This part of the study will allow you to directly engage with numerical approaches in prediction and forecasting in landscape hydrological models. The unit of study is specifically designed to extend your field hydrological knowledge and to strengthen your analytical and numerical skills in this area.
AMED3002 Interrogating Biomedical and Health Data

Credit points: 6 Teacher/Coordinator: Prof Jean Yang Session: Semester 1 Classes: face to face 5 hrs/week; online 2 hrs/week; individual and/or group work 3-6 hrs/week Assumed knowledge: Exploratory data analysis, sampling, simple linear regression, t-tests, confidence intervals and chi-squared goodness of fit tests, familiar with basic coding, basic linear algebra. Assessment: in-semester exam, assignments, presentation Mode of delivery: Normal (lecture/lab/tutorial) day
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.
STAT3014 Applied Statistics

This unit of study is not available in 2019

Credit points: 6 Session: Semester 2 Classes: Three 1 hour lectures, one 1 hour tutorial and one 1 hour computer laboratory per week. Prerequisites: DATA2002 or STAT2X12 Prohibitions: STAT3914 or STAT3002 or STAT3902 or STAT3006 Assumed knowledge: STAT3012 or STAT3912 Assessment: One 2 hour exam, assignments and/or quizzes, and computer practical reports (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
STAT3914 Applied Statistics Advanced

Credit points: 6 Session: Semester 2 Classes: Three 1 hour lectures and one 1 hour computer laboratory per week plus an extra hour each week which will alternate between lectures and tutorials. Prerequisites: STAT2912 or (a mark of 65 or above in STAT2012 or DATA2002) Prohibitions: STAT3014 or STAT3907 or STAT3902 or STAT3006 or STAT3002 Assumed knowledge: STAT3012 or STAT3912 or STAT3022 or STAT3922 Assessment: Written exam (40%), major project (50%), computer labs (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
GEGE3004 Computational Genomics

Credit points: 6 Teacher/Coordinator: Prof Claire Wade Session: Semester 2 Classes: Lectures 2 hours per week, Practical 3hours per week during standard semester. Prerequisites: 6cp of (GEGE2X01 or QBIO2XXX or DATA2X01 or GENE2XXX or MBLG2X72 or ENVX2001 or DATA2X02) Prohibitions: ANSC3107 Assumed knowledge: Genetics at 2000 level, Biology at 1000 level, algebra Assessment: The assessment will consist of one intra-semester examination (20%), group work assignment (30%)[ including assessment both of a project report (20%) and the team process (10%)], individual assignment (10%) and final examination (40%). Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: This unit must be taken by all students in the Genetics and Genomics major.
The average mammalian genome is 3 billion nucleotides long and some other organisms have genomes that are even larger. Working with DNA at the nucleotide level on an organismal scale is impossible without the assistance of high performance computing. This unit will investigate strategies to manipulate genomic data on a whole organism scale. You will learn how scientists use high performance computing and web-based resources to compare and assemble genomes, map genes that cause specific phenotypes, and uncover mutations that cause phenotypic changes in organisms that influence health, external characteristics, production and disease. By doing this unit you will develop skills in the analysis of big data, you will gain familiarity with high performance computing worktop environments and learn to use bioinformatics tools that are commonly applied in research.
Interdisciplinary Project Selective
SCPU3001 Science Interdisciplinary Project

Credit points: 6 Teacher/Coordinator: Pauline Ross Session: Intensive December,Intensive February,Intensive January,Intensive July,Semester 1,Semester 2 Classes: The unit consists of one seminar/workshop per week with accompanying online materials and a project to be determined in consultation with the partner organisation and completed as part of team with academic supervision. Prerequisites: Completion of 2000-level units required for at least one Science major. Assessment: group plan, group presentation, reflective journal, group project Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is designed for students who are concurrently enrolled in at least one 3000-level Science Table A unit of study to undertake a project that allows them to work with one of the University's industry and community partners. Students will work in teams on a real-world problem provided by the partner. This experience will allow students to apply their academic skills and disciplinary knowledge to a real-world issue in an authentic and meaningful way. Participation in this unit will require students to submit an application to the Faculty of Science.