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Data Mining – Stage 1 (Introduction)

Data Mining – Stage 1 is an introduction to the foundations of data mining and knowledge discovery methods and their application to practical problems. Part of a three (3) stage Program this short course brings together the state-of-the-art research and practical techniques in data mining, providing students with the necessary knowledge to appreciate data mining projects and to professionally communicate with analytics experts.
This program is particularly useful for
All those involved in Data Mining for their organisation:

Industry Practitioners wanting to get into data mining
Managers wanting to know what data mining is about
Students, Researchers, Academic
Stage 1 Short Course topics

Introduction to data mining concepts and the broader context
The CRISP-DM approach to data mining
Basics of data
Classification and evaluating classifiers using KNIME
Classification with decision trees

Course outcomes
Upon completion of this course students will:

Understand how data mining fits into the business and society context
Understand key terms and concepts in data mining
Be familiar with an approach for structuring data mining projects
Understand the basics of working with data
Understand the scope and limitations of several state-of-the-art mining
Presenters
Siamak Tafavogh – Research Associate, School of Software Dr. Siamak Tafavogh: Siamak Tafavogh has a PhD (Software Engineering) from UTS. He is currently a Research Fellow and Post Doctorate in Faculty of Engineering and Information Technology. Dr. Tafavogh has taught data mining and data analytics at undergraduate, postgraduate and research level at UTS for the past 3 years. He also runs data analytics short courses for Common Wealth Bank. Dr. Tafavogh’s research interests are in the area of big data, data analytics and visualization of large complex data sets, particularly those in the biomedical and finance domain, but also in customer sales and text mining. For the past 4 years he has been developing approaches to better diagnose and treat childhood cancer sufferers (acute lymphoblastic leukaemia and neuroblastoma). Recently, he has been developing bioinformatics approaches for detecting abnormal patterns within genomic sequence of human.

Prerequisites: Pre-reading

Wikipedia Data Mining

Cross Industry Standard Process for Data Mining

Reference Books:

Witten, I. H., Frank, E. and Hall, M. E. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, CA, 2011.
KNIME

Fees

Standard 1 day $800.00 (GST free)

UTS Alumni and staff- 10% discount
2 or more from the one organisation – 15% discount
Interstate participant – 25% discount
Enrol in any 2 stages – 5% discount in addition to discount already eligible (upfront payment)
Enrol in all 3 stages – 10% discount in addition to discount already eligible (upfront payment)
Please select discount option with drop down box before entering your credit card details. Only 1 discount applicable per person.
Group bookings must be made individually with the 15% discount selected and credit card processed separately for each person.

Contact Information
For specific queries regarding course content, please contact Colin Wise, Advanced Analytics Institute Tel: +61 (02) 9514 9267 or email colin.wise@uts.edu.au with questions relating to this course.

https://shortcourses-bookings.uts.edu.au/ClientView/Schedules/ScheduleDetail.aspx?ScheduleID=1930

When

11th August 2015 09:00 am
To 11th August 2015 05:00 pm

Where

Sydney
235 Jones Street, Ultimo, NSW 2007
Sydney 2007

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