DATA SCIENCE FOR MANAGERS

SECURE YOUR SEAT

THE SPEAKERS

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

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.

COURSE STRUCTURE

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.

THE TICKETS

LIMITED SEATS AVAILABLE - EACH TICKET INCLUDES A 3 DAY ACCESS PASS

SECURE YOUR SEAT

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

THE VENUE

Monash Conference Centre
30 Collins Street

Melbourne VIC 3000

IN ASSOCIATION WITH

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

Copyright © 2016 Monash University. ABN 12 377 614 012 - Accessibility - Caution - Privacy
Monash University CRICOS Provider Number: 00008C, Monash College CRICOS Provider Number: 01857J 

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 visualization: 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

Visualisation

  • Practical and effective visualization: beyond bar charts.
  • Finding the unexpected: the role of visualization in exploratory analysis.
  • Communicating findings: the role of visualization 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

DAY 1 / MON 26 APRIL

DAY 2 / TUES 27 APRIL

DAY 3 / WED 28 APRIL

Michael Brand

Is an Associate Professor of Data Science at Monash University. He currently holds 8 patents and has 10+ pending on topics ranging from Big Data analysis to information retrieval and other sectors.

Dennis Claridge

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

LEARN

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

BECOME

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

 

MAKE

effective business decisions throughout the 
data and model life cycles.

UNDERSTAND

the value of data and its organisational availability

Hear from our participants

Monash Conference Centre
30 Collins Street

Melbourne VIC 3000

3 DAY EXECUTIVE
SHORT COURSE
26-28 APRIL 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