Course Information

Business strategy. Plan and secure success.

Are you data-informed or data-driven? This business analytics course will help build your skills so you can perform a greater role in today’s data-driven world by using data to assist with more informed decision-making.

You will learn how to discern the best data available and ways of transforming and manipulating data to ensure that it is in the best format for processing.

You will then be trained in a range of skills that encompass descriptive, predictive and prescriptive analytics techniques. In so doing, you will also receive demonstrations on how to apply each of these types of analytics methods with contemporary software.

A key focus of the course is analytics applications, to ensure you leave the course with a set of skills and action steps for applying your newly developed analytics skills within your workplace or study.  The analytical tools covered can be applied to data generated within operations, sales and marketing, finance, human resources and customer functions.

Aims

The aims of this course are to:

•  introduce you to the world of business analytics with evidence based and data-driven decision-making
• instruct you how to perform a range of descriptive, predictive and prescriptive analytics techniques across a wide range of organisational settings
• assist you to understand the role of software in facilitating the implementation of analytics techniques
• assist you with extracting insight from data and telling an evidenced based story
• address cybersecurity, privacy and ethics considerations with the use of data
• examine the future of data extraction with Artificial Intelligence assisted technology.

Outcomes

By the end of this course, you should be able to:

• discern the best type of data for making an evidenced-based decision
• decide how to best store and access data, taking into account cybersecurity, privacy and ethics considerations
• perform a range of analytics tasks across a wide variety of organisational settings
• decide the type of software that can best assist you when applying analytics techniques to a business problem
• tell data-driven stories by extracting pertinent insight from data
• prepare for future use of data with Artificial Intelligence assisted technology.

Content

Topic 1: Descriptive, predictive, prescriptive analytics

• The distinction between Analytics, Data Science and Statistics
• The types of Analytics – Descriptive, Predictive, Prescriptive
• Descriptive, Predictive, Prescriptive Analytics – Examples across Commercial, NGO and Govt settings

Topic 2: Data types, storage and access

• Data types – structured, semi –structured, unstructured
• Data types – metric, non-metric, ordinal etc
• Data storage – data base, data hub, data lake
• Accessing data – SQL, NoSQL
• Data dictionary
• Activity 1 – Correct code with Javascript (JSON), which is a scripting language that is commonly used for web pages
• Activity 2 – Use superstore data for creating a data dictionary

Topic 3: Analytics applications

• Social media/ marketing analytics – real world examples
• Activity 3 – social media/ marketing analytics and campaign evaluation
• Financial analytics – real world examples
• Activity 4 – financial analytics and distinction between correlation and causation

Topic 4: Data Insights with analytics

• Principles of data visualisation
• Principles of story telling
• Activity 5 – Storytelling based on data visualisations
• Data insights with non-numerical data
• Text data (Meaning Cloud in Excel) - Note: Meaning Cloud is a “Software as a Service” product that enables semantic processing.
• Image data (demonstration with Orange software) – Note: Orange software is an open-source data visualisation, machine learning and data mining toolkit.
• Activity 6 – Storytelling

Topic 5: Ethics, privacy & data security

• Ethical considerations of innovation using analytics
• Demonstration with worked examples
• Cybersecurity governance considerations
  • SaaS, PaaS, IaaS, Xaas
  • Essential 8 of the Australian Signals Directorate

Topic 6: The future of analytics with AI

• Transformers/ Neural Networks
• Generalist Agents
• Perceivers
• Deep Generative Models
• Provide worked example

Intended audience

This course is intended for people without prior knowledge of analytics who are keen to use data to make better informed decisions and persuade others with insights derived from objectively prepared data.

This course will assist people interested in analytics from all backgrounds. You may be progressing towards management, be a new manager, or a manager with many years of experience across any of the following fields:

• Sales & marketing professionals
• Customer Experience Officers/Managers
• Supply chain/ Logistics/Operations Managers
• Finance Officers/Managers
• Business Managers
• Business Analysts
• General Managers
• anyone who wants to develop their data skills.

Delivery modes

• Face-to-face, presenter taught workshop
• Online workshop via the platform Zoom

Delivery style

Delivered as a one-day workshop using various techniques including lectures, analytical thinking, break-out discussions and exercises, as well as software demonstrations using practice data sets.

Materials

Course materials are distributed electronically using Dropbox.

Before the course

Although not essential, a laptop with pre-installed Excel software would be helpful.

Recommended reading

Bartlett, R 2013, A practitioner’s guide to business analytics using data analysis tools to improve your organization’s decision making and strategy, McGraw-Hill, Sydney, NSW.

EMC Education Services (Eds.) 2015, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, John Wiley & Sons, Indianapolis, US.

Dean, J 2014, Big Data, Data Mining And Machine Learning : Value Creation For Business Leaders And Practitioners Hoboken, New Jersey : John Wiley and Sons, pp. 175 – 181.

Henke, N, Levine, J & McInerney, P 2018, You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role, Harvard Business Review, https://hbr.org/2018/02/you-dont-have-to-be-a-data-scientist-to-fill-this-must-have-analytics-role 

Jain, P, Gyanchandani, M & Khare, N 2016, ‘Big data privacy: a technological perspective and review’, Journal of Big Data, pp. 3-25.

Kane, GC, Palmer, D, Phillips, AN, Kiron, D & Buckley, N 2016, ‘Aligning the Organization for Its Digital Future’, MIT Sloan Management Review and Deloitte University Press, pp. 7.

La Torre, M, Dumay, J, & Antonio Rea, M 2018, ‘Breaching intellectual capital: critical reflections on Big Data security’, Meditari Accountancy Research, Vol. 26, No.3, pp.463-482, viewed on 15 Nov 2018, https://doi.org/10.1108/MEDAR-06-2017-0154

Nill, AN & Aalberts, RJ 2014, ‘Legal and Ethical Challenges of Online Behavioural Targeting in Advertising, Journal of Current Issues & Research in Advertising, Vol. 35, No. 2, pp.126-146. 

North, M, Richardson, R, Abron, A & Gupta, S 2017, ‘A concise review of the emergence of big data and plausible trends’, Quality Access to Success - Calitatea, vol. 18, no. 159, pp. 115-122.