Table R - Higher Degree By Research

Unit outlines will be available through Find a unit outline.

Table R - Quantitative Analysis

This table lists Table R - Higher Degree by Research units of study
BMET9925 AI, Data, and Society in Health

Credit points: 6 Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prohibitions: BMET2925 Assumed knowledge: Familiarity with general mathematical and statistical concepts. Online learning modules will be provided to support obtaining this knowledge. Assessment: Refer to the assessment table in the unit outline Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Unprecedented growth in computing power, the advent of artificial intelligence (AI)/machine learning technologies, and global data platforms are changing the way in which we approach real-world healthcare challenges. This interdisciplinary unit will introduce students from different backgrounds to the fundamental concepts of data analytics and AI, and their practical applications in healthcare. Throughout the unit, students will learn about the key concepts in data analytics and AI techniques, and obtain hands-on experience in applying these techniques to a broad range of healthcare problems. At the same time, they will develop an understanding of the ethical considerations in health data analytics and AI, and how their use impacts society: from the patient, to the doctor, to the broader community. A key element of the learning process will be a team-based Datathon project where students will deploy their knowledge to address an open-ended healthcare problem, in particular developing a practical solution and analysing how it's use may change things in the healthcare domain. Upon completion of this unit, students will understand and be able to enlist data analytics and AI tools to design solutions to healthcare problems.
BSTA5002 Principles of Statistical Inference

Credit points: 6 Teacher/Coordinator: Ms Liz Barnes (Semester 1); Dr Erin Cvejic (Semester 2) Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: BSTA5023 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Online
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
The aim of this unit is to provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in health research. Content covered includes: review of the key concepts of likelihood and construction of Normal­theory confidence intervals; frequentist theory of estimation including hypothesis tests; methods of inference based on likelihood theory, including information and likelihood ratio; Wald and score tests; an introduction to the Bayesian approach to inference; an introduction to distribution­free statistical methods.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
BSTA5007 Linear Models

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly (Semester 1), Dr Timothy Schlub (Semester 2) Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: BSTA5023 and (BSTA5011 or PUBH5010 or CEPI5100) Corequisites: BSTA5002 Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Online
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
The aim of this unit is to enable students to apply methods based on linear models to biostatistical data analysis, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results. This unit will cover: the method of least squares; regression models and related statistical inference; flexible nonparametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of dummy variables, parametrisation, interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects.
NOTE: Linear Models is an important foundation unit. Students who do not develop a strong grasp of this material will struggle to become successful biostatisticians.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
BSTA5023 Probability and Distribution Theory

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Online
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit will focus on applying the calculus-based techniques learned in Mathematical Background for Biostatistics (MBB) to the study of probability and statistical distributions. These two units, together with the subsequent Principles of Statistical Inference (PSI) unit, will provide the core prerequisite mathematical statistics background required for the study of later units in the Graduate Diploma or Masters degree. Content: This unit begins with the study of probability, random variables, discrete and continuous distributions, and the use of calculus to obtain expressions for parameters of these distributions such as the mean and variance. Joint distributions for multiple random variables are introduced together with the important concepts of independence, correlation and covariance, marginal and conditional distributions. Techniques for determining distributions of transformations of random variables are discussed. The concept of the sampling distribution and standard error of an estimator of a parameter is presented, together with key properties of estimators. Large sample results concerning the properties of estimators are presented with emphasis on the central role of the Normal distribution in these results. General approaches to obtaining estimators of parameters are introduced. Numerical simulation and graphing with Stata is used throughout to demonstrate concepts.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
BUSS7902 Quantitative Business Research Methods

Credit points: 6 Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Basic knowledge of statistical concepts Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit introduces Business School HDR students to quantitative techniques for research. It provides students with a review or introduction to the types of quantitative analyses that they may be required to know, discuss or conduct, both during their PhD and in their future working lives. It aims to provide a basic training with a focus on statistical and business analysis methods.
CIVL5458 Numerical Methods in Civil Engineering

Credit points: 6 Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assessment: Refer to the assessment table in the unit outline. Mode of delivery: Normal (lecture/lab/tutorial) day
The objective of this unit is to provide students with fundamental knowledge of finite element analysis and how to apply this knowledge to the solution of civil engineering problems at intermediate and advanced levels.
At the end of this unit, students should acquire knowledge of methods of formulating finite element equations, basic element types, the use of finite element methods for solving problems in structural, geotechnical and continuum analysis and the use of finite element software packages. The syllabus comprises introduction to finite element theory, analysis of bars, beams and columns, and assemblages of these structural elements; analysis of elastic continua; problems of plane strain, plane stress and axial symmetry; use, testing and validation of finite element software packages; and extensions to apply this knowledge to problems encountered in engineering practice.
On completion of this unit, students will have gained the following knowledge and skills:
1. Knowledge of methods of formulating finite element equations. This will provide students with an insight into the principles at the basis of the FE elements available in commercial FE software.
2. Knowledge of basic element types. Students will be able to evaluate the adequacy of different elements in providing accurate and reliable results.
3. Knowledge of the use of finite element methods for solving problems in structural and geotechnical engineering applications. Students will be exposed to some applications to enable them to gain familiarity with FE analyses.
4. Knowledge of the use of finite element programming and modeling.
5. Extended knowledge of the application of FE to solve civil engineering problems.
ECMT5001 Principles of Econometrics

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1,Semester 2 Classes: 1x3hr lecture/week, 1x1hr non-compulsory online tutorial/week Assessment: Online quizzes equivalent to 500wd (10%), 1xGroup assignment equivalent to 1000wd (15%), 1x1hr Mid-semester test (20%), 1x2hr Final exam (55%), Mode of delivery: Normal (lecture/lab/tutorial) day
The unit develops the basic principles of data description and analysis, the idea of using the concept of probability to model data generation, and the statistical concepts of estimation and statistical inference, including hypothesis testing. It then develops these concepts and techniques in the context of the linear regression model to show how econometric models can be used to analyse data in a wide range of potential areas of application in economics, business and the social sciences. The unit combines theory and application. The emphasis is upon the interpretation of econometric estimation results and requires software for hands-on experience.
ECON5005 Quantitative Tools for Economics

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1,Semester 2 Classes: 1x3hr seminar/week, 1x1hr non-compulsory online tutorial/week Assessment: 5x200wd Online Quizzes (15%), 1x1.5hr Mid-semester Test (35%), 1x2hr Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study aims to enhance mathematical ability to provide a skill set that enables students to thrive in their study of economics. Themes such as algebra, the plotting of points, lines, and functions in two and three dimensional space, differential calculus and simultaneous equations are the basis on which the skills are taught.
ECON6703 Mathematical Methods of Econ Analysis A

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1,Semester 2 Classes: 1x3hr lecture/seminar/week Prohibitions: ECON6003 Assessment: x1000wds equivalent Assignments (10%), 1x1.5hr Mid-semester exam (30%), 1x2hr Final exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is an introduction to mathematical economics. It has three purposes. First, to introduce students to the mathematical concepts and methods that are central to modern economics. Second, to give a set of economic applications of the mathematical methods. Third, to develop the students' ability to formulate logical arguments with the degree of precision and rigour demanded in modern economics. The mathematical topics covered include introductory analysis and topology, convex analysis, linear algebra, calculus of functions of several variables, optimisation, and introduction to dynamic programming and dynamical systems.
EDPK5002 Quantitative Methods

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 2 Classes: 1x2hr seminar/week Assessment: portfolio of quantitative research methods (40%) and research analysis using SPSS (40%) and presentations (10%); and 2 multiple choice class tests (10%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit introduces students to the basic principles and procedures of quantitative research. Both experimental and survey research strategies are considered; starting with design and development of the research tools (measures, questionnaires, interviews, observation) and progressing to basic analytical statistical methods. The unit provides a thorough introduction to simple statistics and often looks at real research data examples. By the end of the semester students will have developed various research skills as well as a critical perspective on the appropriate application of those skills.
MATH5311 Topics in Algebra (Alt)

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Familiarity with abstract algebra (e.g., MATH4062 or equivalent) and commutative algebra (e.g., MATH4312 or equivalent). Please consult with the coordinator for further information. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day
Algebra is one of the broadest fields of mathematics, underlying most aspects of mathematics. It is sometimes considered "the mathematics of symmetry" or the "language of mathematics". In its most general description, algebra includes number theory, algebraic geometry and the classical study of algebraic structures such as rings and groups as well as their representations. Advanced algebra intersects other fields of modern mathematics, for instance via algebraic topology, homological algebra and categorical representation theory; and modern physics, via Lie groups and Lie algebras. You will learn about fundamental concepts of a branch of advanced algebra and its role in modern mathematics and its applications. You will develop problem-solving skills using algebraic techniques applied to diverse situations. Learning an area of pure mathematics means building a mental framework of theoretical concepts, stocking that framework with plentiful examples with which to develop an intuition of what statements are likely to be true, testing the framework with specific calculations, and finally gaining the deep understanding required to create technically sophisticated proofs of general results. The selection of topics is guided by their relevance for current research. Having gained an abstract understanding of symmetry, you will discover the manifestation of algebraic structures everywhere!
MATH5431 Mathematical Models for Natural Phenomena Alt

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Familiarity with the modelling and analysis using differential equations (e.g., MATH3063, MATH4063, MATH3078, MATH4078 or MATH4074) and the ability to write code and numerical schemes to solve standard applied mathematical problems (e.g., MATH4076 or MATH3076 or MATH4411 or equivalent). Please consult with the coordinator for further information. Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day
"Mathematical modelling applies mathematical frameworks, such as ordinary and partial differential equations, to capture the dynamics of natural phenomena, including fluid dynamics, Newtonian and relativistic mechanics, climate, ecology, and physiology. Modelling often falls into two styles, mechanistic and phenomenological. Mechanistic modelling seeks to understand how large-scale phenomena are driven by simple, local dynamics usually governed by physical or biological laws or properties. On the other hand, phenomenological modelling seeks to capture large-scale trends of a system, such as growth, decay, and oscillations, without necessarily accounting for smaller-scale dynamics. In practice, most models combine elements of both styles. In this unit you will learn about how these mathematical frameworks are constructed and applied for particular types of phenomena which may include mathematical oncology, high Reynolds number fluid flow, stellar atmosphere, terrestrial climates, populations of cells or organisms or other areas of mathematical interest. You will analyse both classical and new models and critique their applicability and use their predictions to explore aspects of the natural world. Inspired by these ideas, you will have the opportunity to create new models in tutorials and assignments and to use them to solve complex mathematical and scientific problems. By doing this unit, you will learn how mathematics is applied in both simple and complicated models and explore the ways that mathematical analysis creates insight into natural phenomena. "
OLET5120 Understanding And Using ABS Data

Credit points: 2 Session: Intensive April Classes: 4x3hr workshops. This 2 cp OLE for HDR students is designed to be run as a mix of online, blended and f2f learning. Assessment: 2x100wd equivalent online quizzes (15%), 1x300wd equivalent online analysis (10%), 1x800wd equivalent analysing ABS data (40%), 1x700wd eqyivalent presenting ABS data (35%) Mode of delivery: Online
This unit will provide HDR students with conceptual understanding of the role of statutory data in national planning and policy making and a working knowledge of how ABS data is constructed and can be used in social science research. It will provide students with a working knowledge of what data is available through the ABS, the strengths and weaknesses of different data collection methods (survey, census and panel data) and how they can access this data for their own research. It will revolve around practical research tasks designed to show how ABS data can be deployed to answer specific research and public policy questions. Simple data visualisation techniques will also be taught.
OLET5402 Basics of Quantitative Research Design

Credit points: 2 Teacher/Coordinator: Dr Tatjana Seizova-Cajic Session: Intensive June Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
All research questions are about variables and the building blocks of all studies are variables. This unit will help you think about variables in a disciplined and abstract manner regardless of your field of research. We will describe their types, based on several criteria (including level of measurement, role in the study, level of control over variables), and issues that arise when deciding how to measure variables. We will also introduce different terms used for the same basic concepts in different areas of study.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
OLET5606 Data Wrangling

Credit points: 2 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Intensive July Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: Basic exploratory data analysis, basic coding in R Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Data comes in many and varied formats, it can be tall or wide, big or small, structured or unstructured. Regardless of where you get your data from, it will almost always require some wrangling. Data wrangling is the convolution, alignment and preparation of data before use. This unit provides an overview of best practices in organising your research data from the point of discovery through to its use for scientific applications. You will learn the principles of data handling and how to maintain rigour and integrity of your data throughout your research, including documenting data provenance, how to access major databases, and data licensing. After calculating summary statistics to aid in the identification of outliers and missing values, you will learn how to clean and wrangle data in a reproducible manner in R, at a variety of scales. You will "wrangle" your research data using R, identifying outliers and missing values and ensuring provenance.
Textbooks
Data Wrangling with R (Boehmke, B, 2016)
OLET5608 Linear Modelling

Credit points: 2 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Intensive May Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prohibitions: DATA2002 or DATA2902 or ENVX2001 Assumed knowledge: Exploratory data analysis, sampling, simple linear regression, t-tests and confidence intervals. Ability to perform data analytics with coding, basic linear algebra. E.g. DATA1001 and OLET5606 (Data wrangling). Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Linear models form the bedrock of many real-world data analyses. They are versatile, interpretable and easily implemented. This unit provides an overview of two of the most common methods of statistical analysis of data: analysis of variance and regression. You will generate data visualisation and diagnostics plots to interpret and discover the limitations of linear models and identify when more complex approaches may be needed. You will learn to code your analyses and perform reproducible research using the open source statistical package R. A key component of this unit involves generating visualisations, estimating and selecting appropriate linear models using your data. By doing this unit you will learn how to generate, interpret, visualise, discover and critique linear models applied to your original research.
Textbooks
Faraway, J. (2014). Linear models with R. Second Edition. Chapman and Hall/CRC.
OLET5610 Multivariate Data Analysis

Credit points: 2 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Intensive June Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Online
When undertaking research and critically judging the research of others with many variables, a key approach is use of multivariate data analysis. This online unit provides comprehensive skills essential for postgraduate students doing multivariate data analysis and for critically judging the research of others. We focus on the underlying principles you need to explore multivariate data sets and test hypotheses. In so doing, the unit provides you with an understanding of how multivariate research is designed, analysed and interpreted using statistics. The unit will cover the commonly used multivariate data analyses of principal components analysis, cluster analysis, discriminant functions analysis and non-metric multidimensional scaling, as well as parametric and permutational hypothesis testing techniques. Examples of data will be cross-disciplinary, enabling students from many disciplines to appreciate the techniques. Analyses will use the R statistical environment, furthering student skills in this programming environment. By doing this unit, you will be able to use multivariate data analyses using a wide-range of data and present in a format for publication.
PUBH5018 Introductory Biostatistics

Credit points: 6 Teacher/Coordinator: Dr Timothy Schlub, Dr Erin Cvejic Session: Semester 1 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) evening, Normal (lecture/lab/tutorial) day, Online
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit introduces students to statistical methods relevant in medicine and health. Students will learn how to appropriately summarise and visualise data, carry out a statistical analysis, interpret p-values and confidence intervals, and present statistical findings in a scientific publication. Students will also learn how to determine the appropriate sample size when planning a research study. Students will learn how to conduct analyses using calculators and statistical software.
Specific analysis methods of this unit include: hypothesis tests for one-sample, two paired samples and two independent samples for continuous and binary data; distribution-free methods for two paired samples, two independent samples; correlation and simple linear regression; power and sample size estimation for simple studies; and introduction to multivariable regression models;.
Students who wish to continue with their statistical learning after this unit are encouraged to take PUBH5217 Biostatistics: Statistical Modelling.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
PUBH5215 Analysis of Linked Health Data

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly Session: Intensive June,Intensive November Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Corequisites: (PUBH5010 or BSTA5011 or CEPI5100) and (PUBH5211 or PUBH5217 or BSTA5004) Assumed knowledge: Basic familiarity with SAS computing syntax and methods of basic statistical analysis of fixed-format data files Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Block mode
Note: Department permission required for enrolment
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
This unit introduces the topic of analysing linked health data. The topic is very specialised and is relevant to those who are familiar with writing a basic SAS program, who wish to further develop their knowledge and skills in managing and analysing linked health data, eg. hospital admissions, cancer registry, births and deaths.
Contents include: an overview of the theory of data linkage methods and features of comprehensive data linkage systems, sufficient to know the sources and limitations of linked health data sets; design of linked data studies using epidemiological principles; construction of numerators and denominators used for the analysis of disease trends and health care utilisation and outcomes; assessment of the accuracy and reliability of data sources; data linkage checking and quality assurance of the study process; basic statistical analyses of linked longitudinal health data; manipulation of large linked data files; writing syntax to prepare linked data files for analysis, derive exposure and outcome variables, relate numerators and denominators and produce results from statistical procedures at an introductory to intermediate level.
The unit is delivered as a workshop over 5 consecutive days. Lectures are delivered in the morning sessions and the afternoon sessions are computer labs where students gain hands-on experience using large health datasets.
The unit is usually offered twice a year, once in mid-June and once in mid-November.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
PUBH5217 Biostatistics: Statistical Modelling

Credit points: 6 Teacher/Coordinator: Associate Professor Patrick Kelly, Associate Professor Kevin McGeechan Session: Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Prerequisites: PUBH5018 Prohibitions: (PUBH5211 or PUBH5212 or PUBH5213) Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Practical field work: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) day, Online
Note: Refer to the unit of study outline https://www.sydney.edu.au/units
In this unit, you will learn how to analyse health data using statistical models. In particular, how to fit and interpret the results of different statistical models which are commonly used in medicine and health research: linear models, logistic models, and survival models. This unit is ideal for those who wish to further develop their research skills and/or improve their literacy in reading and critiquing journal articles in medicine and health.
The focus of the unit is very applied and not mathematical. Students gain hands on experience in fitting statistical models in real data. You will learn how to clean data, build an appropriate model, and interpret results. This unit serves as a prerequisite for PUBH5218 Advanced Statistical Modelling.
Textbooks
Refer to the unit of study outline https://www.sydney.edu.au/units
SSPS6001 Quantitative Methods in the Social Sciences

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1 Classes: 1x2hr seminar/lab per week Assessment: 1x2hr in-class exam (I) (35%), 1x2hr in-class exam (II)(35%), 3x660wd homework tasks (30%) Mode of delivery: Normal (lecture/lab/tutorial) day
Quantitative methods are vital to social science. This unit introduces students to commonly used techniques for collecting and analysing numerical data to answer empirical questions about social, cultural, and political phenomena. It addresses the description of data with graphs and tables, descriptive statistics, statistical models, hypothesis testing, and other topics. The unit is appropriate for beginners, who will gain perspective and confidence conducting their own quantitative research and critically understanding that of others. It is taught in a computer lab, giving students practical experience with statistical software.
STAT5002 Introduction to Statistics

Credit points: 6 Teacher/Coordinator: Refer to the unit of study outline https://www.sydney.edu.au/units Session: Semester 1,Semester 2 Classes: Refer to the unit of study outline https://www.sydney.edu.au/units Assumed knowledge: HSC Mathematics Assessment: Refer to the unit of study outline https://www.sydney.edu.au/units Mode of delivery: Normal (lecture/lab/tutorial) evening
The aim of the unit is to introduce students to basic statistical concepts and methods for further studies. Particular attention will be paid to the development of methodologies related to statistical data analysis and Data Mining. A number of useful statistical models will be discussed and computer oriented estimation procedures will be developed. Smoothing and nonparametric concepts for the analysis of large data sets will also be discussed. Students will be exposed to the R computing language to handle all relevant computational aspects in the course.
Textbooks
All of Statistics, Larry Wasserman, Springer (2004)