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Predictive Analytics and Machine Learning in R and Azure – London

Altis Global

Monday, 18 September 2017 at 09:00 - Wednesday, 20 September 2017 at 17:00 (BST)

Predictive Analytics and Machine Learning in R and...

Ticket Information

Ticket Type Remaining Sales End Price Fee VAT Quantity
Early Bird 6 Tickets 29 Aug 2017 £1,500.00 £49.95 £309.99
Single Rate 6 Tickets 29 Aug 2017 £1,650.00 £52.95 £340.59

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Event Details

Use R and Azure ML Studio to build and tune advanced machine learning models.

Predictive analytics and machine learning techniques are revolutionizing business and government. Predictive Analytics and Machine Learning in R and Azure is aimed at the person who wants to have a better understanding of the mechanics behind the models and how these models are realistically applied in the business setting. In addition to covering advanced machine learning techniques in depth, the course covers the management of stakeholder expectations during predictive analytics projects and analytics project management.  Advanced machine learning methods are discussed in depth, including those used to win global data science competitions.

This course is taught in R and Azure.

Who Should Attend?

This course is suited to any professional who already understands analytics and machine learning basics and is ready to progress to higher levels of sophistication. It is also suitable to any professional who is interested in who predictive analytics projects are conceptualised, scoped and project managed.


It is recommended that students have completed an introductory R programming course or MOOC and at least one 2nd year statistics unit at the university level.

Laptop Required Specs

Intel i3 processor, 4GB RAM

Windows operating system

Unrestricted PC that has install permissions

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

For more information visit

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.


Do you have questions about Predictive Analytics and Machine Learning in R and Azure – London? Contact Altis Global

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When & Where

Kingsway Hall Hotel
66 Great Queen Street
WC2B 5BX London
United Kingdom

Monday, 18 September 2017 at 09:00 - Wednesday, 20 September 2017 at 17:00 (BST)

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Altis Global

Since 1998, Altis have been deploying our skills in Business Intelligence, Analytics and Data Management to deliver successful outcomes for our clients by helping them maximise business performance.

Much of an organisation’s operational efficiency can be gleaned from accurate and insightful reporting. Altis look to combine and analyse data in new and creative ways, giving customers the power to make better informed decisions and manage their business performance more effectively.

Altis is built on the philosophy that our people make our business and our clients' businesses successful. Our core ethos of ‘Connecting with Courage, Heart, and Insight' means the firm commitment of our team to building lasting relationships with our clients and sharing the responsibility of delivering their outcomes.

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Predictive Analytics and Machine Learning in R and Azure – London
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