Predictive Analytics and Machine Learning for Business

Predictive analytics has revolutionised business across a range of industry verticals.

Predictive analytics has revolutionised business across a range of industry verticals, including banking, insurance, healthcare, retail, telecom, media and e-commerce. Despite the returns on predictive analytics being well documented, companies often proceed cautiously when presented with new predictive analytics initiatives. And with good reason – poorly built predictive models are costly and useless to the organisation. With a strong emphasis on the proven machine learning models used by leading companies such as Google, Walmart and AIG, this course provides a solid grounding in machine learning methods including classification, random forests and decision trees.

This course is taught in R and Azure.

Who Should Attend?

This course builds core skills for those who require a grounding in predictive analytics. Candidates who will benefit from the course include:

  • Graduate hires who are on track toward a career in analytics or data science
  • Managers and executives who wish to use data to predict business unknowns
  • Candidates already working in analyst positions, including (but not limited to) positions involved with data analytics, digital and marketing analytics, customer and market analysis
  • Any other business professional who would like to make decisions on the basis of data


It is recommended that students have completed Introduction to Data Science and Analytics, or have an undergraduate degree in statistics or mathematics.

Laptop Required Specs

Intel i3 processor, 4GB RAM

Either Mac or Windows operating system

Software Requirements

Excel 2010 / 2013 / 2016

R or RStudio Latest version

A free trial or paid subscription to Microsoft Azure ML Studio

Course Objectives

  • Provide participants with a full understanding of multiple linear regression, logistic regression, market basket analysis and decision trees
  • Provide participants with a full understanding of model diagnostics and underlying assumptions for a range of predictive models that are appropriate in the business context
  • Provide participants with a framework for selecting an appropriate predictive model depending on the task at hand
  • Provide participants with a qualitative and quantitative methodology for assessing the performance of predictive models
  • Provide participants with a firm grounding in the business case for Market Basket Analysis and the mathematics underlying this technique
  • Provide participants with guidelines on how to present predictive analytics results in the corporate setting (taught via simulated boardroom presentations in class)

Upon successful completion of this course, participants will be able to:

  • Build and use predictive models to inform decision making in the face of uncertainty
  • Build robust predictive models that predict sales, revenues, employee attrition, customer churn or other events
  • Know when to apply a classification algorithm instead of a continuous prediction algorithm
  • Understand the limitations and benefits of predictive models and how to communicate these limitations and benefits to senior decision makers
  • Separate esoteric and academic predictive models from those that are proven to be robust in the business setting
  • Understand how to calculate the ROA (return on analytics) for predictive analytics projects
  • Recognise business opportunities where the investment in predictive analytics is justified in the context of project risk and expected benefits
  • Understand how to manage predictive analytics project risk proactively whilst preventing time and cost overruns

Here’s an outline of what will be covered during this 3-day workshop

Day 1


Day 1

I. Introductions (9:00am – 9:30am)

II. Using Multiple Variables for Prediction: Multivariate Regression I (9:30am – 10:15am)

  • Why Excel when we could be using R, Python, SPSS or SAS?
  • Extending the linear model from Course 2 (‘Introduction to Data Science and Analytics’)
  • Ordinary least square for multivariate regression and coefficient selection
  • Prediction using multivariate regression

III. Q&A / Break (10:15am – 10:30am)

IV. Multivariate Regression II (10:30am – 11:30am)

  • Model selection methods
  • Forward selection
  • Backward selection
  • Other methods

V. LUNCH (11:30am – 12:30pm)

VI. Workshop: Team Activity (12:30pm – 1:30pm)

VII. Multivariate Regression III (1:30pm – 2:30pm)

  • Model Diagnostics for MLR
  • Multivariate Regression and Big Data (the impact of high volume, high variety
  • Training and Test Datasets – a short Introduction to Machine Learning using to the multivariate case data on multiple regression models) MLR

VIII. Q&A / Break (2:30pm – 2:45pm)

IX. Workshop: Team Activity and Presentation (2:45pm – 4:30pm)

X. Group Feedback and Day 1 Wrap Up (4:30pm – 5:00pm)

Day 2


I. Predicting Success or Fail Events – Logistic Regression I (9:00am – 10:15am)

  • Where does logistic regression sit in the family of binary classifier models?
  • How are coefficients estimated in logistic regression? Maximum Likelihood
  • Business Applications of Logistic Regression – Predicting credit default

II. Q&A / Break (10:15am – 10:30am)

III. Logistic Regression II (10:30am – 11:30am)

  • Model selection methods
  • Deviance
  • The drop in deviance test
  • Business Applications of Logistic Regression – Predicting attrition

IV. LUNCH (11:30am – 12:30pm)

V. Workshop: Team Activity (12:30pm – 1:30pm)

VI. Logistic Regression III (1:30pm – 2:30pm)

  • Diagnostics for logistic regression
  • Test and Training Sets for Logistic Regression
  • Evaluation of logistic regression fit
  • True positives, true negatives, false positives and false negatives
  • ROC curves for assessing model fit
  • A brief overview of other binary classification models
  • Decision trees
  • Random forests
  • Bayesian Networks
  • Support Vector Machines
  • Neural Networks

VII. Q&A / Break (2:30pm – 2:45pm)

VIII. Workshop: Team Activity and Presentation (2:45pm – 4:30pm)

IX. Group Feedback and Day 2 Wrap Up (4:30pm – 5:00pm)

Day 3


I. Market Basket Analysis and Affinity Analysis I (9:00am – 10:15am)

  • What is association rule mining?
  • What is the business case for Market Basket Analysis and Affinity Analysis
  • Support, lift and confidence – Interpreting the results of an association rules analysis

II. Q&A / Break (10:15am – 10:30am)

III. Market Basket Analysis and Affinity Analysis II (10:30am – 11:30am)

  • Calculating support, life and confidence by hand with your favourite Fast Food Restaurant
  • Visualising Market Basket and Affinity Results
  • Business Applications of Market Basket Analysis – Supermarket Example

IV. LUNCH (11:30am – 12:30pm)

V. Workshop: Team Activity (12:30pm – 1:30pm)

VI. Decision Trees Analysis for Prediction (1:30pm – 2:30pm)

  • What are decision trees?
  • The decision tree as a predictive model
  • Decision tree algorithms
  • The limitations of decision trees
  • Prediction with decision trees
  • Comparison of decision trees and logistic regression
  • Model interpretability
  • Data preparation
  • Data handling (binary vs continuous data)
  • Validation
  • Robustness
  • Performance with high volume data

VII. Q&A / Break (2:30pm – 2:45pm)

VIII. Workshop: Team Activity and Presentation (2:45pm – 4:30pm)

IX. Group Feedback, Course Wrap Up and Awarding of Certificates (4:30pm – 5:00pm)

$2,400 $2,200


Sydney CBD

24th to 26th July 2017

Single Rate $2,400*

Early Bird Rate $2,200* (expires 19th of June 2017)

* prices exclude GST

Meet our Instructor

Isaac Reyes

Isaac Reyes

Isaac is Head of Data Science at Altis Consulting. A passionate data science educator, Isaac has lectured in analytics and statistical theory at the Australian National University, AIM and the University of Canberra.

Isaac shared his vision for Data Science with perhaps the biggest teaching stage of them all – TEDx. “Speaking about the intersection of Data Science and world issues at a TED event was something that I’ve always wanted to do. My TED talk focused on how we can use Data Science to measure how much we really care about the issues that matter.”

In his previous roles, Isaac has worked as a Data Scientist at leading data consulting firms including Datapharm, Quantium and PricewaterhouseCoopers. He holds a Master of Statistics from the Australian National University and has over 3,000 hours of combined teaching experience. A top Kaggler, his vision is to train 1,000 Data Scientists by 2018.