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Data analytics stage 1

Data Analytics is the discipline of analysing raw data with the purpose of drawing conclusions from it. For organisations, it can boost customer acquisition or retention, better manage supply chains, improve risk management, and more. This course brings together state-of-the-art research and practical techniques in data analytics.

About this course

This one-day course introduces the foundations of data analytics and knowledge discovery methods. You will start with an introduction to broad data analytics concepts and software for creating data science applications and services. This is followed by an exploration of CRISP-DM, a robust approach to planning a project. The course also covers data pre-processing and sampling techniques, such as importing libraries, checking for missing values and standardising data. The course concludes with the process of evaluating predictive modelling results.

Course structure


  • Foundational data mining concepts, the broader context and KNIME analytic software


  • The CRISP-DM approach to predictive modelling such as classifiers and predictors

Data and data preparation.

  • Fundamental to pre-processing and data sampling

A classifier: decision trees and their evaluation

  • Introduction to K-Nearest Neighbour classifier and predictor
  • Introduction to Decision tree predictor and classifier
  • Predictive modelling evaluation


Learning outcomes

  • Understand key terms and concepts in data mining
  • Understand how data mining fits into the business and society context
  • Be familiar with an approach for structuring data mining projects
  • Understand the scope and limitations of several state-of-the-art mining approaches


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Who is this course for?

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.

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