# University of Sydney Handbooks - 2018 Archive

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# Business Analytics Descriptions

##### Errata
Item Errata Date
1. ENVX1002 Introduction to Statistical Methods: Prohibitions have changed. They now read
N: ENVX1001, MATH1005, MATH1905, MATH1015, MATH1115, DATA1001, BUSS1020, STAT1021 and EMCT1010
20/2/2018

#### 1000-level units of study

BUSS1020 Quantitative Business Analysis

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1 x 2hr lecture and 1 x 2hr tutorial per week Prohibitions: ECMT1010 or MATH1005 or MATH1905 or MATH1015 or STAT1021 or ENVX1001 or ENVX1002 or DATA1001 or MATH1115 Assessment: quiz 1 (15%), quiz 2 (15%), weekly homework (15%), written assignment (20%), final exam (35%) Mode of delivery: Normal (lecture/lab/tutorial) day
All graduates from the BCom need to be able to use quantitative techniques to analyse business problems. This ability is important in all business disciplines since all disciplines deal with increasing amounts of data, and there are increasing expectations of quantitative skills. This unit shows how to interpret data involving uncertainty and variability; how to model and analyse the relationships within business data; and how to make correct inferences from the data (and recognise incorrect inferences). The unit will include instruction in the use of software tools (primarily spreadsheets) to analyse and present quantitative data.
DATA1001 Foundations of Data Science

Credit points: 6 Teacher/Coordinator: Dr Di Warren Session: Semester 1,Semester 2 Classes: lecture 3 hrs/week; computer tutorial 2 hr/week Prohibitions: MATH1005 or MATH1905 or MATH1015 or MATH1115 or ENVX1001 or ENVX1002 or ECMT1010 or BUSS1020 or STAT1021 Assessment: assignments, quizzes, presentation, exam 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, Fourth Edition, Freedman Pisani Purves
ECMT1010 Introduction to Economic Statistics

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x2hr lecture/week, 1x2hr workshop/week Prohibitions: ECMT1011 or ECMT1012 or ECMT1013 or MATH1015 or MATH1005 or MATH1905 or STAT1021 or ECOF1010 or BUSS1020 or ENVX1001 Assessment: homework (15%), quizzes (30%), assignment (15%) and 1x2hr Final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit emphasises understanding the use of computing technology for data description and statistical inference. Both classical and modern statistical techniques such as bootstrapping will be introduced. Students will develop an appreciation for both the usefulness and limitations of modern and classical theories in statistical inference. Computer software (e.g., Excel, StatKey) will be used for analysing real datasets.
ENVX1002 Introduction to Statistical Methods

Credit points: 6 Teacher/Coordinator: A/Prof Thomas Bishop Session: Semester 1 Classes: Two 1-hour lectures per week, one 1-hour tutorial per week, one 2-hour computer practical per week Prohibitions: ENVX1001 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 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.
Textbooks
No textbooks are recommended but useful reference books are:
MATH1005 Statistical Thinking with Data

Credit points: 3 Session: Semester 2,Summer Main,Winter Main Classes: Lectures 2 hrs/week; Practical 1 hr/week Prohibitions: MATH1015 or MATH1905 or STAT1021 or STAT1022 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 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: One 1.5 hour examination, assignments and quizzes (100%) 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
Freedman, Pisani and Purves, Statistics, Norton, 2007
MATH1015 Biostatistics

Credit points: 3 Session: Semester 1 Classes: Two 1 hour lectures and one 1 hour tutorial per week. Prohibitions: MATH1005 or MATH1905 or STAT1021 or STAT1022 or ECMT1010 or BIOM1003 or ENVX1001 or ENVX1002 or BUSS1020 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: One 1.5 hour examination, assignments and quizzes (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Textbooks
As set out in the Junior Mathematics Handbook
MATH1115 Interrogating Data

Credit points: 3 Session: Semester 1,Semester 2,Winter Main Classes: 2-hr lab; and 1x1-hr lecture per week Prerequisites: MATH1005 or MATH1015 Prohibitions: DATA1001 or STAT1021 or ECMT1010 or ENVX1001 or BUSS1020 or ENVX1002 or MATH1905 Assessment: projects/presentations, final exam 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 foundational statistical thinking to interrogate data. Focusing on statistical literacy, the unit covers foundational statistical concepts such as visualising data, the linear regression model, and testing significance using the t and chi-square tests. Based on a flipped learning approach, you will experience most of your learning in weekly collaborative 2 hour labs, supplemented by 1 hour lectures. Working in teams, you will explore three real data stories across different domains, with associated literature. The combination of MATH1005/1015 and MATH1115 is equivalent to DATA1001, allowing you to pathway to the Data Science, Statistics, or Quantitative Life Sciences majors.
Textbooks
Freedman, Pisani and Purves, Statistics, 2007
MATH1905 Statistical Thinking with Data (Advanced)

Credit points: 3 Session: Semester 2 Classes: Two 1 hour lectures and one 1 hour tutorial per week. Prohibitions: MATH1005 or MATH1015 or STAT1021 or STAT1022 or ECMT1010 or ENVX1001 or ENVX1002 or BUSS1020 Assumed knowledge: (HSC Mathematics Extension 2) OR (90 or above in HSC Mathematics Extension 1) or equivalent Assessment: One 1.5 hour examination, assignments and quizzes (100%) 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
As set out in the Junior Mathematics Handbook
QBUS1040 Foundations of Business Analytics

Credit points: 6 Session: Semester 2 Classes: 1 x 2hr lecture and 1 x 2hr tutorial per week Prerequisites: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH 1000-level units which must include MATH1905 Assessment: assignment (30%), mid-semester exam (25%), final exam (45%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit provides students with the necessary foundations and skills to undertake second year units in business analytics and successfully complete the Business Analytics major. Theoretical models discussed are motivated by real life business applications and decision problems. The unit provides a grounding in linear algebra (matrix properties) and calculus and applies these methods to regression models with multiple variables. Topics covered include logistic regression, interaction and nonlinear effects. The unit also introduces the key ideas of optimization (particularly for quadratic problems) and shows how optimisation models can be used to make statistical estimates. At the same time as building understanding of the mathematical foundations needed in business analytics, the unit helps students to build programming skills to solve practical problems from the business area. The unit makes use of the modern programming languages such as Python.

#### 2000-level units of study

QBUS2310 Management Science

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 2hr lecture and 1x 1hr tutorial per week Prerequisites: 2018 Commencing students: QBUS1040; 2018 continuing students: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: ECMT2620 Assessment: assignment 1 (15%), assignment 2 (15%), mid-term exam (30%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
The ability to understand and mathematically formulate decision problems is a fundamental skill for managers in any organisation. This unit focuses on basic management science modelling techniques used in capacity planning, production management, and resource allocation. Students learn to approach complex real life problems, formulate appropriate models and offer solution procedures to ensure an optimal use of resources. Methods include linear programming, integer programming, quadratic programming, and dynamic programming.
QBUS2810 Statistical Modelling for Business

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 2hr lecture and 1x 1hr tutorial per week Prerequisites: 2018 Commencing students: QBUS1040; 2018 continuing students: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: ECMT2110 Assumed knowledge: This unit relies on mathematical knowledge at the level of the Maths in Business program, including calculus and matrix algebra. Students who do not meet this requirement are strongly encouraged to acquire the needed mathematical skills prior to enrolling in this unit. Assessment: individual assignment 1 (5%); individual assignment 2 (10%); individual assignment 3 (5%); group project (25%); mid-semester exam (20%); final exam (35%) Mode of delivery: Normal (lecture/lab/tutorial) day
Statistical analysis of quantitative data is a fundamental aspect of modern business. The pervasiveness of information technology in all aspects of business means that managers are able to use very large and rich data sets. This unit covers a range of methods to model and analyse the relationships in such data, extending the introductory methods in BUSS1020. The methods are useful for detecting, analysing and making inferences about patterns and relationships within the data so as to support business decisions. This unit offers an insight into the main statistical methodologies for modelling the relationships in both discrete and continuous business data. This provides the information requirements for a range of specific tasks that are required, e.g. in financial asset valuation and risk measurement, market research, demand and sales forecasting and financial analysis, among others. The unit emphasises real empirical applications in business, finance, accounting and marketing, using modern software tools.
QBUS2820 Predictive Analytics

Credit points: 6 Session: Semester 2 Classes: 1x 2hr lecture and 1x 1hr tutorial per week Prerequisites: QBUS2810 or ECMT2110 or DATA2002 Assumed knowledge: This unit assumes mathematical knowledge at the level of the Maths in Business program (including calculus and matrix algebra) and basic computer programming skills at the level of QBUS2810. Assessment: assignment 1 (20%), assignment 2 (20%), mid-term exam (20%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Predictive analytics are a set of tools to enable managers to exploit the patterns found in transactional and historical data. For example major retailers invest in predictive analytics to understand, not just consumers' decisions and preferences, but also their personal habits, so as to more efficiently market to them. This unit introduces different techniques of data analysis and modelling that can be applied to traditional and non-traditional problems in a wide range of areas including stock forecasting, fund analysis, asset allocation, equity and fixed income option pricing, consumer products, as well as consumer behaviour modelling (credit, fraud, marketing). The forecasting techniques covered in this unit are useful for preparing individual business forecasts and long-range plans. The unit takes a practical approach with many up-to-date datasets used for demonstration in class and in the assignments.

#### 3000-level units of study

QBUS3310 Advanced Management Science

Credit points: 6 Session: Semester 1 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: QBUS2310 Prohibitions: ECMT3610 or ECMT3710 Assessment: assignment 1 (10%), assignment 2 (10%), mid term exam (30%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit gives guidelines for the formulation of management science models to provide practical assistance for managerial decision making. Optimisation methods are developed, and the complexity and limitations of different types of optimisation model are discussed, so that they can be accounted for in model selection and in the interpretation of results. Linear programming methods are developed and extended to cover variations in the management context to logistics, networks, and strategic planning. Other topics may include decision analysis, stochastic modelling and game theory. The unit covers a variety of case studies incorporating the decision problems faced by managers in business.
QBUS3320 Supply Chain Management

Credit points: 6 Session: Semester 1 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: ECMT2640 or QBUS2330 or QBUS2350 or QBUS340 or QBUS3350 Assessment: simulation (10%), group case study (20%), EXCEL homework (20%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
The supply chain is the network of companies or organisational components that together deliver a product or service to the final customer. The objective of supply chain management is to effectively coordinate the flows of materials, information and capital in supply chains. This unit will introduce the important concepts and tools used in Supply Chain management. The topics covered may include: Inventory management and risk pooling; supply chain dynamics; network planning; supply chain integration; and global logistics. In addition, the unit will discuss the design of contracts within the supply chain to achieve good outcomes.
QBUS3330 Methods of Decision Analysis

Credit points: 6 Session: Semester 2 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: QBUS2320; ECMT2630; ENGG1850; CIVL3805 Assessment: assignment 1 (10%), assignment 2 (10%), mid-semester exam (30%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This introductory unit on decision analysis addresses the formal methods of decision making. These methods include measuring risk by subjective probabilities; growing decision trees; performing sensitivity analysis; using theoretical probability distributions; simulation of uncertain events; modelling risk attitudes; estimating the value of information; and combining quantitative and qualitative considerations. The primary goal of the unit is to demonstrate how to build models of real business situations that allow the decision maker to better understand the structure of decisions and to automate the decision process by using computer decision tools.
QBUS3340 Operations Management

Credit points: 6 Session: Semester 1 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Prohibitions: QBUS2330 Assessment: individual assignment 1 (10%), individual assignment 2 (5%), group project (15%), mid-semester exam (25%), final exam (45%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers the fundamentals of operations management, an exciting area that has a profound effect on the productivity of both manufacturing and services. The techniques of operations management apply throughout the world to virtually all productive enterprises (i.e. offices, hospitals, restaurants, department stores and factories) - the production of goods and services requires operations management. The efficient production of goods and services requires effective application of the concepts, tools, and techniques introduced in this unit. These include: quality management, capacity planning, location and layout strategies, supply chain management and inventory control.
QBUS3350 Project Planning and Management

Credit points: 6 Session: Semester 2 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prohibitions: QBUS2350 Assumed knowledge: BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905. Assessment: group project (20%), homework 1 (10%), homework 2 (10%), homework 3 (10%), final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
Project management provides organisations with a powerful set of tools to improves their ability to plan, implement, and manage activities to accomplish specific organisational objectives. Project management is more than just a set of tools; it is a results-oriented management style that places a premium on building collaborations among a diverse cast of characteristics. This unit introduces students to the planning and management of projects by focusing on a variety of practical topics including project network, PERT, resource scheduling, learning curves, cost and time management in projects, and the use of project management support systems. It also discusses the organisational, leadership, cultural, technological challenges that project managers might face.
QBUS3820 Machine Learning and Data Mining in Business

Credit points: 6 Session: Semester 1 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: QBUS2810 or ECMT2110 or DATA2002 Assessment: individual assignment 1 (10%), individual assignment 2 (10%), weekly online problems (10%), basic skills assessment (5%), mid-semester exam (25%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Advances in information technology have made available rich information data sets, often generated automatically as a by-product of the main institutional activity of a firm or business unit. Data Mining deals with inferring and validating patterns, structures and relationships in data, as a tool to support decisions in the business environment. This unit offers an insight into the main statistical methodologies for the visualisation and the analysis of business and market data, providing the information requirements for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to empirical applications using modern software tools.
QBUS3830 Advanced Analytics

Credit points: 6 Session: Semester 2 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: QBUS2810 or DATA2002 or ECMT2110 Assessment: project (20%), weekly online problems (10%), basic skills (5%), mid-term exam (25%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit is designed to equip students with advanced tools for estimation and testing in relevant business statistical models. In particular, the unit covers maximum likelihood, Bayesian estimation and inference, and hypothesis testing. The unit acknowledges the importance of learning computing skills as helpful for job applications and special emphasis is made throughout the unit to learn numerical methods such as Monte Carlo simulations and Bootstrapping. Special topics in advanced statistical modelling, such as nonlinear estimators and time series regression, are also covered. The materials taught are essential as preparation for honours in Quantitative Business Analysis.
QBUS3600 Business Analytics in Practice

Credit points: 6 Session: Semester 2 Classes: 1 x 2hr lecture and 1 x 1hr tutorial per week Prerequisites: 2018 commencing students: completion of at least 120 credit points including QBUS2310, QBUS2810 and QBUS2820. 2018 continuing students: completion of at least 120 credit points including QBUS2310 and QBUS2810 Assessment: individual assignment (30%), group project (30%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: This unit should only be undertaken by students in their final semester of the Business Analytics major.
This capstone unit bridges the gap between theory and practice by integrating knowledge and consolidating key skills developed across the Business Analytics major. The problem-based approach to learning in this unit offers vital tools and techniques for business decision makers in the big data era through the use of very large and rich data sources. The unit casts the knowledge of statistical learning in modern machine learning context and exposes business students to a range of state-of-the-art machine learning topics with the emphasis on applications involving the analysis of business data. Machine Learning is a fundamental aspect of business analytics that automates analytical modelling and decision making. Students ensure their career-readiness by demonstrating their ability to apply concepts, theories, methodologies, and programming skills to authentic problems and challenges faced in the field of business analytics.

#### 4000-level units of study

BUSS4000 Honours in Business

Session: Semester 1,Semester 2 Prerequisites: BUSS4001 AND 2 x Honours coursework units in the specialisation area ((BUSS4112 and BUSS4113) or (BUSS4212 and BUSS4213) or (BUSS4312 and BUSS4313) or (BUSS4412 and BUSS4413) or (BUSS4512 and BUSS4513) or (BUSS4612 and BUSS4613) or (BUSS4712 and BUSS4713) or (BUSS4812 and BUSS4813)). Corequisites: BUSS4104 Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
This unit is administrative only and serves as a consolidation for all marks to represent a single final mark for students undertaking Honours. Marks will be calculated as follows: BUSS4001 (20%); BUSS4X12 (10%); BUSS4X13 (10%); BUSS4104 (60%).
BUSS4001 Business Honours Research Methods

Credit points: 12 Session: Semester 1 Prerequisites: Students must meet the entry requirements to the Honours program, including completion of a pass undergraduate degree and a major in the specialisation area Assessment: research methodology assessment (50%), research proposal development (50%) Mode of delivery: Block mode
This unit is an introduction to research methods used in business disciplines. The unit provides students with an understanding of the range of methods that may be used to answer research questions, their strengths and weakness and underlying philosophical assumptions. Key elements of the research process are addressed, including the purpose of the research; devising the research questions and hypotheses; selecting a research strategy; methods and procedures for data collection and analysis; and interpreting and reporting the results. Students learn important research terminology, how to write a research proposal and ethical considerations in conducting research.
BUSS4312 Business Analytics Honours A

Credit points: 6 Session: Semester 1 Prerequisites: Students must meet the entry requirements to the Honours program, including completion of a pass undergraduate degree and a major in the specialisation area Corequisites: BUSS4001; BUSS4313 Assessment: homework (20%), assignment A (40%), assignment B (40%) Mode of delivery: Block mode
This unit covers advanced research-integrated coursework topics in optimisation and stochastic processes, such as convex optimisation, duality, approximation, statistical estimation, random walks and Markov chains, and Poisson and other stochastic processes.
BUSS4313 Business Analytics Honours B

Credit points: 6 Session: Semester 1 Prerequisites: Students must meet the entry requirements to the Honours program, including completion of a pass undergraduate degree and a major in the specialisation area Corequisites: BUSS4001: BUSS4312 Assessment: homework (15%), assignment (25%), presentation (15%), and final exam (45%) Mode of delivery: Block mode
This unit aims to provide advanced knowledge on linear models and methods for economic and financial time-series analysis and panel data models. The unit focuses on estimation and inference. It covers some of the basics of time series model including stationary processes, AR, MA and ARMA processes, spectral analysis, structural change, nonstationarity, VAR and VECM, state-space models and Kalman filter.
BUSS4104 Business Honours Thesis

Credit points: 24 Session: Semester 2 Prerequisites: BUSS4001 + 2 x Honours coursework units in the specialisation area Corequisites: BUSS4000 Assessment: individual thesis (100%), oral thesis communication (0%) Mode of delivery: Supervision
This unit comprises the research and writing of a supervised thesis on an approved topic in business. A written Honours Thesis and presentation of the research work is undertaken.