Predictive Modelling and Data Mining Training - Canberra
by
Presciient
Date and time
Location
Training Choice - Canberra
Level 4, 54 Marcus Clarke St
Canberra, Australian Capital Territory 2601
Australia
Refund policy
Description
Predictive Modelling And Data Mining
Data Mining and Predictive Modelling skills are of vital and growing importance in commercial, government, commercial and not-for-profit organisations. Those in the Management, Product, Risk and IT functions benefit from skills and literacy in this area.
This two-day course introduces a range of data mining tools and techniques as they are commonly used in business, and provides practical experience in their use.
Course Outline
This course will provide a conceptual overview and practical hands-on experience of a wide range of key tools, techniques and processes.
At the heart of the data mining toolkit is the suite of predictive modelling methods. Accordingly, the course will develop attendees' literacy in the strengths, characteristics and correct application of a range of predictive modelling methods, from relatively simple linear models through to complex and powerful Random Forests, Support Vector Machines, Decision Trees, Gradient Boosting Machines and Neural Networks will be covered along the way.
It will also teach the correct framing of predictive modelling problems, suitably preparing data, evaluating model accuracy and stability, interpreting results and interrogating models.
The two key styles of predictive modelling - operational for targeting and explanatory for insights - will be described and distinguished.
As well as predictive modelling, the course will cover a range of other key data mining tools, including:
- Data exploration and visualisation: univariate summaries, correlation matrices, heat maps, hierarchical clustering
- Principal Components Analysis - used to segment and interpret multivariate data
- Cluster analysis - used for customer segmentation and anomaly detection
- Other "unsupervised" outlier detection tools
- Frequent item set analysis
- Association analysis - used in retail market basket analysis and the assessment of risk groupings.
- Link and network analysis visualisation - which provide a simple and compelling way to communicate and analyse relationships, and are commonly applied in forensics, human resources and law enforcement.
- This course will use R as the basic learning tool, utilising a range of R packages, including Rattle, a graphical user interface for data mining in R.
Who should attend?
This course is suitable for anyone in management, administrative, product, marketing, finance, risk and IT roles who work with data and want to become acquainted with modern data analysis tools.
Course Outcomes
Attendees will, by the end of the course:
Understand the fundamentals of predictive modelling.
Have developed the ability to assess the effectiveness and fitness for purpose of any predictive modelling tool or technique.
Understand a range of unsupervised data mining techniques.
Know how to effectively prioritise analytical resources in a data mining context.
Testimonial
"Thank you very much for the information I gathered at the Predictive Modeling course I attended recently. As a beginner in R, I thought that it might be a bit overwhelming. But I was wrong! Eugene did a fantastic job at explaining the concepts and all practical work was engaging and easy to follow. Entertaining, informative and most importantly relevant - it has already proven valuable in my work.”
Data Manager - Sanja Djekic
Course Instructor
The course will be led by Presciient Director, Dr Eugene Dubossarsky. He is the head of the Sydney Users of R Forum.Eugene is also Principal Founder of Analyst First, an international analytics industry organisation. He is a founder of the Institute of Analytics Professionals of Australia (IAPA); Director, University of New South Wales School of Mathematics and Statistics Industry Advisory Board; and a recognised industry leader in Business Analytics. Eugene is an experienced, analytics professional of 20 years' experience programming in R and its parent language, S.
Prerequisites
Attendees are recommended to have completed Presciient's 'Introduction to R' two-day course, or equivalent. This is a helpful but non-essential prerequisite.
Feedback
Use the 'Contact the Host' button on this page to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.
Presciient Training Coaching, Mentoring and Capability Development for Analytics
Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice and other services to build your organisation's strategic and operational analytics capability.
Our Canberra May Courses
Data Analytics for Fraud and Anomaly Detection, Forensics and Security :
http://forencanmay.eventbrite.com.au/
Predictive Modelling and Data Mining
http://predcanmay.eventbrite.com.au/
Introduction to R
Our courses include:
- Introduction to R
- Predictive Modelling and Data Mining
- Forecasting and Trend Analysis
- Data Visualization
- Data Analytics of Fraud Detection, Forensics and Security
- Data Analytics for Campaign Marketing, Targeting and Insights
- Data Analytics for Insurance Claims analysis
- Data Analytics for Retail Marketing and Pricing
- Working with Data : Analysis and Report Writing for Everybody
