Data Mining course studies algorithms that allow computers to find patterns and regularities in data, perform prediction and forecasting, and generally improve their performance through interaction with data. It is regarded as the key elements of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data.
The process includes data selection, cleaning, coding, using different statistical, and machine learning techniques, and visualization of the generated structures. The course provides students with an appreciation of the uses of the data mining software in solving business decision problems. Students will gain knowledge of the theoretical background to several of the commonly used data mining techniques and will learn about the application of data mining as well as acquiring practical skills in the use of data mining algorithms.
2 days (16 hours)
$716.90/- (GST Inclusive)
Medium of Instruction: English
To enable learners to have an overall understanding of the landscape of Data Mining and Machine Learning.
- Introduction: Machine Learning and Data Mining
- Supervised and Unsupervised learning techniques
To give the participants the very first steps towards forming hypothesis and doing predictive analytics, learning some of the classification and regression techniques in supervised learning.
- Machine Learning and Classification
- Classification: Logistic Regression, K-Nearest Neighbour.
- Regression: Multiple Linear Regression
It aims to equip participants with the basics of how supervised machine learning models work and how evaluation and optimization can be carried out.
- Evaluation and Metrics
- Understanding and comparing model results
We will continue the journey for the learners in understanding some of the preparation and visualisation techniques which is required for knowledge discovery into their data, on this day unsupervised learning techniques will also be taught.
To equip learners in understanding some of the preparation and visualisation techniques which is required for knowledge discovery into their data.
- Data Preparation for Knowledge Discovery
- Data Mining and Visualization
To further equip learners with knowledge on other unsupervised learning techniques, and rule-based mining also be taught.
- Associations Rules
- Appraise the application of data analytics in a given context
- Recommend appropriate analytics solutions in a given context
- Preparation and visualisation of data for analytical modelling
- Construct analytics models/results as part of solutions to address business problems
- Evaluate the performance of analytics models
- Analyse the results or outputs of analytics models
Mode of Assessment
Written and Practical