We brought together a group of industry leaders with years of experience and with real world case studies.

Di Cook

Professor of Business Analytics in the Monash Business School. She is well-known for her work in data visualisation, exploratory data analysis & data mining, and for developing open source software.

James Horton

is a Data Technology Advisor, Strategist and Connector. He is Managaging Director at Datanomics, an advisory and collaborative innovation practice.

Stuart Growse

is an entrepreneurial technology executive and thought leader. As Chief Digital Officer for GCS Agile, he is responsible for company vision and strategy, product development and innovation.

Dr. Dickson Lukose

is the Chief Data Scientist at GCS Agile. Prior to this, Dr. Lukose was the Senior Director of the Artificial Intelligence (AI) Lab in MIMOS Berhad (Malaysia) developing AI software for Big Data Analytics. Earlier in his career, Dr. Lukose worked as Principal Knowledge Engineer with Mindbox Inc. (USA).

Mark Stammers

is the CTO at Stoploss Logic.He has over 25 years of experience in data management, data warehousing, client server and web-based interface development projects.

Kim Marriott

is a Professor of Computer Science at Monash University. He is a co-director of the Monash FIT Research Flagship in Modelling, Optimisation and Visualisation.


Module #1

Introduction to Data Science

  • Origins of Data Science and a brief history of the Big Data revolution.
  • The Big Data landscape.
  • How much data is there really, and does it matter?
  • Un-siloing data: use paradigms for organisational data and public data.
  • Descriptive, predictive and prescriptive analysis.
  • From recommendations to insights: black-box and white-box analytics.

Module #2

Data as an Asset

  • The V's of Big Data: Volume, Velocity, Variability, Veracity.
  • Data business strategies.
  • Data sources, synergies and differentiators.

Module #3

Data Life Cycle

  • The analytics value chain.
  • Overview of the data analysis cycle, connecting data science to the business problems.
  • Work cycle of a data scientist: wrangling, modelling and validation.
  • Managing research.

Module #4

Privacy and Ethics in Big Data

  • The societal impacts of Big Data.
  • Privacy in Australia and global perspectives.
  • Big Data ethics: history and current thinking.
  • Opportunities and risks for organisations and individuals.




Dayton Brown, Project Manager, JLL

"The course was absolutely invaluable to me and came at the exact right point for where we are heading in our organisation. There was a lot of stuff I had picked up through research but without actually practicing anything it is always hard to have visibility of what does and doesn’t work. For that alone this course has probably saved JLL many times the course fee."

"I can highly recommend this course as being part of the initial group that contained a great mix of trainees from various business backgrounds with excellent industry related lecturer's /presenters that generated good engagement, discussion and learning outcomes."

Ralph Richter, Director - Richter Consulting Group


Monash Conference Centre
30 Collins Street

Melbourne VIC 3000


Thank you to all our partners that will make this event one of a kind.

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Module #5

Data Engineering for Analysis

  • Data Science engineering and its drivers for change.
  • Data volumes, data structures, and how they vary.
  • Data Science architectures: the common stages.
  • The Usual suspects: Distributed File Systems, Map Reduce, Spark

Module #7

Data Wrangling and Exploratory Analysis

  • Determining data quality. Data cleansing.
  • Entity matching.
  • Imputation.
  • Background modelling.
  • Exploratory analysis.

Module #8

Fundamentals of Statistics

  • Types of data: numerical, categorical, ordinal.
  • Statistical summaries: mean, standard deviation, quantities, correlation.
  • Simple data visualisation: histograms, boxplots, time plots and scatterplots.
  • Cross-tabulations.
  • Causality vs. association, independence.
  • Randomisation and random sampling.
  • Statistical inference using bootstrapping.

Module #9

Model Creation and Validation

  • Prediction: linear regression, nonparametric regression, k-NN.
  • Forecasting: auto.arima and Error-Trend-Seasonal exponential smoothing algorithms.
  • Hold-out sets, cross-validation, AIC.
  • Classification: logistic regression, classification trees, SVM.
  • Clustering: k-means, hierarchical clustering.
  • Supervised vs. unsupervised vs. semi-supervised learning.
  • Dimension reduction: principal components.
  • Languages and environments (e.g. R, Python, MATLAB or even Excel) and standards (PMML).

Module #6


  • Practical and effective visualisation: beyond bar charts.
  • Finding the unexpected: the role of visualisation in exploratory analysis.
  • Communicating findings: the role of visualisation in communicating Data Science outputs.
  • Standard tools: R, Tableau, D3.

Module #10

Operationalisation and the Model Life Cycle

  • Determining the needs: on how much data must decisions be taken, how often and how quickly must they be made, how often must models be refreshed?
  • Plugging into existing data paths and choosing appropriate technologies.
  • Stale models and  model refreshing.
  • Operationalisation from a business perspective: determining value and making Data Science outputs part of standard business and decision-making processes.

Module #11

Panel: Building a Data - Driven Enterprise

  • Data Science as a process, rather than as a point event.
  • The role of high-level management in enabling data-driven decisions.
  • The role of direct management: on the un-Gantt-ability of research.

Module #12

Case Study

  • Operational efficiency by predictive analytics
  • Architectural choices for integration and efficacy




Michael Brand

(Course Director)

is an Associate Professor of Data Science at Monash University, and director of the Monash Centre for Data Science. He has over 20 years of industry research experience and holds 20 issued and pending patents on Big Data processing, data analysis and information retrieval.

Dennis Claridge

is a co-founder of doubleIQ and its Business Director since 2000, focusing on delivering sophisticated marketing and analytics capabilities to customers.


why Data Science has become an intrinsic
feature of modern business.


your organisation's expert on the utilisation
of data and model life cycles.



effective business decisions throughout the 
data and model life cycles.


the value of data and its organisational availability

Hear from our participants

Monash Conference Centre
30 Collins Street

Melbourne VIC 3000

16-18 OCTOBER 2017

Monash Conference Centre
 30 Collins Street, Melbourne

Salim Naim

is the CTO Advanced Analytics & Data Science for Microsoft Services in APJ. He is responsible for envisioning, solutions design, consulting and delivery of advance analytic services to customers and partners.

Can't make it for April?

Register for October

8:30AM to 5:15PM each day


The impact of Data Science on modern business is second only to the introduction of computers. And yet, for many businesses the barrier of entry remains too high due to lack of knowhow, organisational inertia, difficulties in hiring the right manpower, and apparent need for upfront commitment, and more.

This course is designed to address these barriers, giving the necessary knowledge and skills to flesh out and manage Data Science functions within your organisation, taking the anxiety-factor out of the Big Data revolution and demonstrating how data-driven decision-making can be integrated into one’s organisation to harness existing advantages and to create new opportunities.

Assuming minimal prior knowledge, this course provides complete coverage of the key aspects, including data wrangling, modelling and analysis, predictive-, descriptive- and prescriptive-analytics, data management and curation, standards for data storage and analysis, the use of structured, semi-structured and unstructured data as well as of open public data, and the data-analytic value chain, all covered at a functional level.


The past several years have been marked by a paradigm shift in the role of data in organisations. Whereas, historically, data was retained for compliance reasons or where needed for day-to-day business operations, the advent of cheap, readily-available storage options has made organisations more inclined not to erase stored data, and the boom of equally-cheap, equally-available processing power has opened the door to advanced analytics on this stored data, unlocking the business value hidden in the bits.

Today, this trend has been taken to its extremes: data is collected by any available means, far beyond what is necessary for standard business operations or, indeed, beyond information that has clearly-defined future uses; deep analysis is done both retrospectively and on-the-fly, often driving split-second business decisions; insights are and gained by combining otherwise unrelated - and historically soiled - data sets, including ones that are publicly available or that are purchasable, such as social media archives or demographic data; and long-held rules-of-thumb are being systematically replaced by quantitatively-superior data-driven decision-making.

Use of data analysis has become ubiquitous, from traditional uses such as risk analysis by banks and insurance companies to new domains such as consumer-behaviour analysis, churn prediction and efficacy measurement and optimisation for all types of customer incentives. Also emerging are intra-organisational applications and uses, such as in The Internet of Things: Big Data analytics over telemetry data from industrial appliances and networked devices (e.g., smart meters) are now used in every vertical, from manufacturing to mining, from transportation to health, from energy to cyber-security. Wherever a digital footprint can be created, data is gathered and analysed in order to model behaviours, understand causes and effects, predict the future and allow decision optimisation for profit maximisation and cost minimisation, which is why even small and medium businesses today are accumulating Big Data and experimenting with cloud-based data analytics, and why this data is proving vital for creating and maintaining their competitive advantages.

The Data Science for Managers course, encompassing an intensive 24 hours over the course of 3 days, is an offering uniquely focused on the needs of professionals in managerial positions in the data-driven world. Drawing on Monash’s world-leading practitioners and real-world case studies, the course is designed to give professionals an understanding regarding where data relevant to decision-making can be found, how it can be harnessed, what wrangling is required to make it usable and how predictive or prescriptive models can be generated from it. The course will furthermore address how to approach data privacy, how to manage issues of data storage and accessibility, and the use of data in real-time decision-making.

Upon course completion, attendees will have

  • Gained confidence in the management of data-analytic projects,
  • Learned the skills necessary to allow their organisations a pain-free migration into the “data-driven enterprise” world and to increase their organisation’s foothold in data analysis, and
  • Acquired an understanding of the key trends in Data Science and how these are influencing the future of business.


Day 1 - Day 3

8:30am - 9:00am        :   Tea & coffee on arrival
9:00am - 10:45am      :   Module 1
10:45am - 11:00am    :   Morning tea
11:00am - 12:45pm    :   Module 2
12:45pm - 1:30pm      :   Lunch
1:30pm - 3:15pm        :   Module 3
3:15pm - 3:30pm        :   Afternoon tea
3:30pm - 5:15pm        :   Module 4

I thoroughly enjoyed the course and it provided many thought-provoking insights that will assist our team as we set off on our analytics journey.

Jan Lambrechts, Information Management Specialist Advisor - Department of Premier and Cabinet