The Analytics Institute offers training to our Corporate and SME members. These programmes are developed and delivered by our Associate Faculty (drawn from leading academic institutions) and by our partner training organisations.

We offer our own training programmes and programmes with partner organisations such as Independent Colleges, NUIG and the Analytics Store. Each of our programmes can be customised to meet the specific requirements of organisations and can be delivered in company or in a classroom setting. Training rates are discounted to member organisations.

Some of the programmes available to member organisations are listed below. If are interested in any of these programmes for your organisation, or if you have specific training requirements you would like to discuss with us, please complete the ‘Register Interest’ form or contact us at [email protected]

Introduction to Data Mining (click to download PDF)

Data Mining is the process of discovering patterns in large data sets using techniques which involve machine learning, statistics and databases systems. It is an essential process where intelligent methods are applied to extract data patterns. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

Introduction to Web Mining (click to download PDF)

Web mining is the integration of information gathered by traditional data mining methodologies and techniques with information gathered over the World Wide Web. Web mining is used to better understand customer behaviour. Making use of automated apparatuses to extricate data from servers and web2 reports, it permits organizations to get to both organised and unstructured information from browser activities, server logs, website and link structure, page content and different sources.

Introduction to Text Mining (click to download PDF)

Text mining is the analysis of data contained in natural language text. The purpose of Text Mining is to process unstructured information, extract meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining algorithms.
While text analytics is an emerging field, applying advanced analytics technique to text data is a powerful means to uncover information concealed in document collections.

Introduction to Statistical Analysis with R (click to download PDF)

This course will introduce the participants to R and teach them how to carry out both descriptive and inferential statistics using R structure for further use.

Key Topics Covered:

  • Introduction to R using RStudio:
    Installing packages, data types/classes, importing data, writing and running a script, creating functions, etc…
  • Descriptive Statistics:Basic visualisation (including histograms, bar-charts, scatterplots, etc…), computation of mean, median, variance, range and other statistics.
  • Inferential Statistics:
    Correlation, regression and hypothesis testing of qualitative and quantitative data (including t-tests, ANOVA, non-parametric and chi- square tests).

Segmentation and Predictive Modelling with R (cick to download PDF)

This course will teach the learner how to use R to develop segmentation and prediction models using key data mining and statistical techniques.

Key Topics Covered:

  • CRISP-DM Process:
    CRISP-DM is a commonly used data mining process, which covers all topics of a data mining project including (i) Business Understanding (ii) Data Understanding, (iii) Data Preparation, (iv) Statistical Analysis, (v) Evaluation and (vi) Deployment.
  • Segmentation Modelling:
    Application of key segmentation techniques including k-means and hierarchical clustering. Learners will be taught all aspects of segmentation modelling including how to (i) prepare the data, (ii) apply the techniques, (iii) assess quality of segmentation models, (iv) profile segments and (v) monitor accuracy of segmentation models.
  • Prediction Modelling:
    Application of key data mining and statistical techniques including classification trees, regression and neural networks. Learners will be taught all aspects of prediction modelling including how to (i) prepare the data, (ii) apply the techniques, (iii) assess the quality of prediction models and (iv) monitor the accuracy of prediction models.