This one-day course introduces the foundations of data mining and knowledge discovery methods, and their application to practical problems. Gain the skills to appraise data mining projects and professionally communicate with analytics experts.
Data Analytics is the discipline of analysing raw data with the purpose of drawing conclusions from it. For organisations, it can be used to boost customer acquisition or retention, better manage supply chains, improve risk management, and more. You’ll start with an introduction to broad data mining concepts and software for creating data science applications and services. This is followed by exploring CRISP-DM, a robust and well-proven approach to planning a data mining project. Data pre-processing and sampling techniques are covered, such as importing libraries, checking for missing values and standardising data. The course concludes with the process of evaluating predictive modelling results.
Businesses are embracing the power of data and technology for a multitude of areas. Course participants may include individuals and organisations looking to improve their use of big data for decision making.
By the end of this course you should be able to:
- 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 approaches
- Introduction to data mining concepts, the broader context and KNIME analytic software
- The CRISP-DM approach to predictive modelling such as classifiers and predictors
- Fundamental to pre-processing and data sampling
- Introduction to K-Nearest Neighbour classifier and predictor
- Introduction to Decision tree predictor and classifier
- Predictive modelling evaluation
About the presenter
Professor Paul Kennedy is the director of the Biomedical Data Science Laboratory in the UTS Centre for Artificial Intelligence and Head of Discipline (Data Analytics/Artificial Intelligence) in the School of Software. He has taught data analytics at UTS since 1999 and contributed to the Australasian Data Mining Conference (AusDM) since 2007 in various roles. He has actively contributed to many international program committees and reviewed for international journals and books.