Big Data in Business

Table of postgraduate units of study: Commerce

Errata
Item Errata Date
1.

Assessment has changed for the following unit:

QBUS5001 Quantitative Methods for Business: Assessment: Weekly homework (10%), assignment (20%), mid-semester exam (20%), final exam (50%)

12/12/2018

The information below details the unit of study descriptions for the units listed in the Table of postgraduate units of study: Commerce.

Timetabling information for the current year is available on the Business School website. Students should note that units of study are run subject to demand.

Big Data in Business

Achievement of a specialisation in Big Data in Business requires 30 credit points from this table comprising:
(i) 6 credit points in foundational units of study
(ii) 6 credit points in compulsory units of study
(iii) 18 credit points in elective units of study.

Units of study for the specialisation

Foundational units of study

QBUS5001 Quantitative Methods for Business

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 3hr lecture and 1x 1hr tutorial per week Prohibitions: ECMT5001 or QBUS5002 Assumed knowledge: Basic calculus; basic concepts of probability & statistics Assessment: weekly homework (10%), assignment (20%), mid-semester exam (30%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
This unit highlights the importance of statistical methods and tools for today's managers and analysts, and demonstrates how to apply these methods to business problems using real-world data. The quantitative skills that students learn in this unit are useful in all areas of business. Through taking this unit students learn how to model and analyse the relationships within business data; how to identify the appropriate statistical technique in different business environments; how to compute statistics by hand and using special purpose software; how to interpret results in the context of the business problem; and how to forecast using business data. The unit is taught through data-driven examples, exercises and business case studies.

Compulsory units of study

BUSS6002 Data Science in Business

Credit points: 6 Session: Semester 1,Semester 2 Classes: seminars: 3 hours per week x 13 weeks Prerequisites: QBUS5001 or QBUS5002 Assumed knowledge: Basic knowledge of statistics, probability and linear algebra Assessment: assessment 1 (15%); assessment 2 (25%); midterm exam (20%); final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
Growing volumes of data and, more importantly, the computation power to analyse it are now widely recognised as key business assets. No single discipline has the tools to make the most of these assets. Instead successful "big data" capability requires (a) the ability to understand how data can (and often cannot) be used to generate new insights into substantive problems (b) knowledge of how data are generated and used and (c) the ability to understand connections between variables captured in data. This unit provides an overview of principles from the disciplines of Business Information Systems and Business Analytics, applied in the context of Marketing problems, relevant for using 'big data' in business planning, decision-making and operations.

Elective units of study

INFS6018 Managing Business Intelligence

Credit points: 6 Session: Semester 1 Classes: 1 x 3hr seminar per week Assumed knowledge: Understanding the major functions of a business and how those business functions interact internally and externally so the company can be competitive in a changing market. How information systems can be used and managed in a business. How to critically analyse a business and determine its options for transformation. (ii) Desirable Experience as a member of a project team. Assessment: mid-semester exam (35%); project report (30%); project presentation (10%); reflective summary (25%) Mode of delivery: Normal (lecture/lab/tutorial) evening Faculty: Business (Business School)
Business Intelligence (BI), increasingly known as Business Analytics, is a major source of competitive advantage in the Information Age and is therefore a leading business priority globally. In recent times, this field has evolved from a technology topic to a management priority, creating an unprecedented demand for new management skills. Taking a business rather than technology perspective, this unit covers all aspects of the enterprise BI ecosystem in the context of strategic and operational BI, including all five stages of BI evolution. Topics include assessment and management of organisational data quality, multidimensional data modelling and integration, management of structured and unstructured data (including those created by social media), business aspects of data warehousing, innovation through advanced analytics, BI driven performance management, business process intelligence, active enterprise intelligence, and management of complex BI projects. Access is provided to the largest world-wide community of BI academics and industry practitioners called TUN (www.TeradataUniversityNetwork.com). The hands-on experience in using a commercial BI platform, combined with in-depth analytical skills, will enable students completing the unit to help any organization (regardless of its size and industry domain) to derive more intelligence from its data and compete on analytics. This unit does not require programming experience; it is suitable for both current and aspiring BI practitioners as well as general business practitioners from any functional area interested to learn how to start and lead BI-related initiatives.
INFS6023 Data Visualisation

Credit points: 6 Session: Semester 1 Classes: 3hr workshop (once per week), weeks 1-13. Assessment: workshop participation (10%); individual project journal (15%); project report (35%); project presentation (15%); reflective summary (25%) Mode of delivery: Normal (lecture/lab/tutorial) evening Faculty: Business (Business School)
Data visualisation, story-telling, and scenario development have been identified as the most prominent analytical practices of tomorrow. This unit seeks to equip students with necessary knowledge and data visualisation skills, acquired though real-life project inspired by the leading industry practices. Students will also develop a 'holistic' view of data visualisation in practice and will acquire 'thinking tools' to deal with its organisational and societal challenges. This unit focuses on business/organisational decision makers and their use of data visualisation. As such this unit does not require any prior IT, computer science or data science experience.
ITLS6107 Applied GIS and Spatial Data Analytics

Credit points: 6 Session: Semester 2 Classes: 7 x 2 hr lectures, 7 x 4 hr computer labs Prohibitions: TPTM6180 Assessment: individual projects (40%); group project (20%); group presentation (10%); final exam (30%) Mode of delivery: Normal (lecture/lab/tutorial) evening Faculty: Business (Business School)
Note: This unit assumes no prior knowledge of GIS; the unit is hands-on involving the use of software, which students will be trained in using.
The world is increasingly filled with systems, devices and sensors collecting large amounts of data on a continual basis. Most of these data are associated with locations that represent everything from the movement of individuals travelling between activities to the flow of goods or transactions along a supply chain and from the location of companies to those of their current and future customers. Taking this spatial context into account transforms analyses, problem solving and provides a powerful method of visualising the world. This is the essence of Geographic Information Systems (GIS) and this unit. This unit starts by introducing students to the 'building blocks' of GIS systems, including data structures, relational databases, spatial queries and analysis. The focus then moves on to sources of spatial data including Global Positioning System (GPS), operational systems such as smartcard ticketing and transaction data along with web-based sources highlighting both the potential and challenges associated with integrating each data source within a GIS environment. The unit is hands-on involving learning how to use the latest GIS software to analyse several problems of interest using real 'big data' sources and to communicate the results in a powerful and effective way. These include identifying potential demand for new services or infrastructure, creating a delivery and scheduling plan for a delivery firm or examining the behaviour of travellers or consumers over time and locations. This unit is aimed at students interested in the spatial impact of decision-making and on the potential for using large spatial datasets for in-depth multi-faceted analytics.
MKTG6001 Marketing Research Concepts

Credit points: 6 Session: Semester 1 Classes: 1x 3hr seminar per week Assessment: in-semester exams(s) (20%), final exam (30%), project (stage 1) (20%), project (stage 2) (20%), class participation (10%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
This unit provides an introduction to marketing research and an overview of the industry. The major components of marketing research projects are discussed and students gain an insight into understanding and structuring research problems. The unit also gives an overview of primary, secondary and internal sources of data as well as advanced methods and techniques of research.
MKTG6018 Customer Analytics and Relationship Management

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 3hr seminar per week Assessment: Mid-term exam (20%), final exam (30%), team presentation (10%), CRM program report (20%), case write-up (10%), attendance and in-class discussion (10%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
There have been two fundamental shifts in the focus of business and marketing strategy. On the one hand, companies have become more focused on managing relationships with their customers over an extended period of time. On the other hand, more than any time in history companies' decisions become more data-driven due to the exponential increase in the volume of data on customers, competitors and markets. To obtain, retain and grow a customer base, it is crucial to know how to obtain customer information and how to make sense of it. This unit introduces students to fundamental concepts of customer relationship management and state-of-art analytics and how to apply these to real world business problems. The unit covers topics including understanding customer relationships, implementing strategic customer relationship management, handling and analysing customer-related databases, increasing customer profitability based on actionable insights gained from customer data, and giving more value to data through visualisation. Students also gain statistical skills, however, no prior knowledge of statistics is required.
QBUS6810 Statistical Learning and Data Mining

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 2hr lecture and 1x 1hr tutorial per week Prerequisites: ECMT5001 or QBUS5001 Assessment: group project (30%); online quizzes (20%); final exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
It is now common for businesses to have access to very 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 visualization and the analysis of business and market data. It provides the tools necessary to extract information required for specific tasks such as credit scoring, prediction and classification, market segmentation and product positioning. Emphasis is given to business applications of data mining using modern software tools.
QBUS6840 Predictive Analytics

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1 x 2hr lecture and 1 x 1hr tutorial Prerequisites: QBUS5001 or ECMT5001 Assessment: group assignment (30%), homework (15%), mid-semester exam (20%), final exam (35%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this unit, students gain skills required to succeed in today's highly analytical and data-driven economy. The unit introduces the basics of data management, business forecasting, decision trees, logistic regression, and predictive modelling. The unit features corporate case studies and hands-on exercises to demonstrate the concepts presented.
QBUS6850 Machine Learning for Business

Credit points: 6 Session: Semester 1,Semester 2 Classes: 1x 2hr lecture per week (13 weeks) and 1x 1hr tutorial (lab) per week (12 weeks) Prerequisites: QBUS6810 Assessment: assignment 1 (10%); assignment 2 (10%); group project (20%); mid-semester exam (20%); final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
Machine Learning is a fundamental aspect of data analytics that automates analytical model building in modern business. In the big data era, managers are able to use very large and rich data sources and to make business decision based on quantitative data analysis. Machine Learning covers a range of state-of-the-art methods/algorithms that iteratively learn from data, allowing computers to find hidden patterns and relationships in such data so as to support business decision. This unit introduces modern machine learning techniques and builds skills in using data for everyday business decision making. Topics include: Machine Learning Foundation; Modern Regression Methods; Advanced Classification Techniques; Latent Variable Models; Support Vector Machines (SVM) and Kernel Methods; Artificial Neural Networks; Deep Learning; and Machine Learning for Big Data. Emphasis is placed on applications involving the analysis of business data. Students will practise applying machine learning algorithms to real world datasets by using an appropriate computing package.
QBUS6860 Visual Data Analytics

Credit points: 6 Session: Semester 1,Semester 2 Classes: 13 interactive lectures x 2 hours each, plus 13 workshops driven by student work x 1 hour each, plus 10 week x 1 hour tutorials on software training (e.g. for Tableau, Gephi, Google Charts, Google Big Query) Prerequisites: QBUS5001 or QBUS5002 Assumed knowledge: The unit assumes knowledge of statistics and confidence in working with data. Assessment: Weekly assignments (20%), group projects (40%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day Faculty: Business (Business School)
Accurate and effective analysis of data is a crucial skill in today's data-rich business environment. Visual Data Analytics (VDA) is an indispensable scientific tool for analysing all sorts of business-related data and, in particular, complex high-dimensional data. Applications include the visualisation of financial statements, capital market data, marketing data, supply chain data and many others. VDA has the ability to encode vast amounts of information into a small space that can be then intuitively interpreted for decision-making. This unit draws upon statistics, computer science, behavioural psychology and information design for visualising numerical and text data. It presents statistical and data analysis methods that are necessary for description, exploration, inference and diagnosis using data reduction, visual mining, smoothing, clustering and validation techniques. Upon completion of the unit, students should be proficient in producing high integrity visuals that enables fast and precise business decision-making. Students will also learn about the limitations of visual perception and how to design powerful visuals that can tap into our natural cognitive predisposition in favouring visual types of information.