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Diploma in Predictive Data Analytics

Duration: 11 wks (PT), one evening per week, 6.30pm-9.00pm

Location: Wellington Quay Only

Start Date: 25th September 2018

Days: Tuesday


Accredited By: IBAT

Fee: €1250

More Info:

Flexible payment plans are available. Contact Admissions team for more information.

Course Overview

Predictive Analytics is widely used in marketing, financial services, retail, travel and many other areas. And the field is growing fast, fueled by availability of new data sets and sources, increases in computing power, and the ongoing, relentless search for competitive advantage. 

Companies such as Google, Twitter and LinkedIn are recruiting predictive analytics professionals to mine the ever-growing mass of consumer behavior data to gain a competitive edge in the marketplace. Today’s business challenges demand timely marketing and financial information that enables executives, managers, and front-line employees to make better decisions, take action, and correct problems before they affect the company’s financial and marketing performance. Predictive Analytics is about improvement that comes from knowing more than you did before. You set out to eliminate the bad, highlight the good, and maybe move to a position where you can semi-automate your decision-making.

"Data really powers everything that we do."

Jeff Weiner, chief executive of LinkedIn

Predictive Analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. It uses a number of advanced techniques, including data mining and  modeling to help decision makers with future forecasts.  It aims to predict the probability of the occurrence of future events such as customer churn, loan defaults, and stock market fluctuations. 

"Information is the oil of the 21st century, and analytics is the combustion engine."

Peter Sondergaard, Vice President Gartner Research
"Putting customers at the core of everything we do and leveraging data to help our customers to better manage their finances is simply good business which benefits everyone." Olivier Van Parys, Head of Analytics, Bank of Ireland
"Data Anaytics is evolving rapidly – those who don't embrace it fully will be left behind – it's that simple. So, for competitive edge and to plan for a successful future, it's all about expanding the use of real insights and analytics to generate increased revenues." Richard Harris, ,Head of Online Marketing and Customer Intelligence

Predictive Analytics involves everything from sophisticated statistical modeling to relatively simple data mining and  is transforming virtually every industry as it provides the ultimate competitive advantage. 


Percentage of high-performing companies using real-time Predictive Analytics


Starting Salary for entry-level Data Analyst


Hottest skills ranking for 2016, Statistical Analysis and Data Mining - LinkedIn Ireland

Learning Outcomes

Predictive Analytics Course

The Diploma in Data Predictive Analytics will introduce you to many of the popular advanced statistical techniques used in the field of marketing and financial predictive analytics. On the course you will learn:

  • Multivariate techniques used to transform information from large data sets data into actionable insights
  • Hands-on experience using RapidMiner, a leading data analytics tool
  • Models such as multiple linear regression, logistic regression and decision trees, which are frequently used in solving predictive analytics problems, and
  • Regression models, to help you understand the relationships among these variables, and how you can exploit their relationships and make decisions.
Predictive Data Analytics
Predictive Data Analytics
Predictive Data Analytics
Predictive Data Analytics
Predictive Data Analytics
Predictive Data Analytics 


These skills will enable you to shift roles from ‘trusted observer’, to a more commercial role as strategist, analyst and adviser. You can move to a role where you actively partner with management and operations to influence the direction and decisions of the business on a day-to-day basis. Leaners will also be introduced to both ‘big’ and ‘small’ data, how to manipulate, analyse and interpret it in sophisticated ways and to present this in a persuasive and influential manner. This course will teach you how to identify situations in which predictive analytics can add value by better meeting customer needs, smarter allocation of marketing budgets and improving financial decisions.

This course will teach you how to identify situations in which predictive data analytics can add value by better meeting customer needs, smarter allocation of marketing budgets and improving the financial performance of the company.

  • You will know when to use the following modelling techniques and understand their pros and cons:
  • Factor analysis for data reduction
  • Multiple regression for prediction purposes
  • Multiple discriminant analysis for classification
  • Cluster analysis for better segmentation
  • Each week, in-class time will be provided for you to work with RapidMiner leaning how to analyse and interpret a different multivariate statistical technique using a real-world dataset.

It important to note that this is not a statistics course; even though all the techniques are based on the theory of statistics, the approach taken here is logic-based, rather than formula-based. Therefore, you will not be a statistics expert at the end of this course; however, you will know and apply all the techniques in a practical manner.

In the end, you will be able to apply all learned theory into practice and start building a more data-driven culture within your organisation.


  • Module 1 - Business Intelligence and Data: +

    • Structured and unstructured data
    • Types of variables
    • The normal distribution
    • Types of analytics
    • Difference between business intelligence (BI) and predictive analytics
  • Module 2 - Business and Predictive Analytics: +

    • Benefits of Business Analytics and Predictive Analytics.
    • The basic principles of predictive analytics (similarity, distance)
    • Understanding terms like data mining, machine learning and predictive modelling
    • Overview of Big Data and common technology to use Big Data
    • Introduction and overview of Hadoop, MapReduce and related technologies and techniques for the analysis of Big Data
  • Module 3 - Data Mining and Data Analytics: +

    • Trends leading to Data Flood
    • Data Mining – Big Picture
    • Goals of Data Mining
    • Basic Data Mining Tasks
    • Data Mining vs. KDD
    • Data Mining Process – Big Picture
    • Data Mining – Business Case Studies
  • Module 4 - Types of Analytics: +

    • Descriptive Analytics
    • Predictive Analytics
    • Predictive Analytics Project Phases
  • Module 5 - Introduction to RapidMiner: +

    • Exploring RapidMiner framework
    • Features of RapidMiner
  • Module 6 - Data Preparation and Cleaning: +

    • CRISP-DM and the modelling life cycle
    • Understand the process of data preparation
    • Data cleaning and its importance
    • Data cleaning techniques
  • Module 7 - Data Mining and Statistical Models: +

    • Classification and Prediction
    • Assessing classification models
      -Confusion matrix
      -Misclassification costs
      -K-Nearest-Neighbors (KNN)
      -Measuring distance
      -Generating classifications and predictions
      -Bayesian Classifiers
  • Module 8 - Cluster and Association Analysis: +

    • Using RapidMiner cluster analysis to create data segments
    • Understanding cluster analysis
    • Algorithms and techniques in clustering
    • Hierarchical Clustering
    • K-Means Clustering
    • Association Rules
      -Apriori algorithm
      -FP-Growth algorith
  • Module 9 - Predictive Modeling with Regression: +

    • Using RapidMiner for regression analysis
    • Linear regression for descriptive modeling
    • Linear regression for predictive modeling
    • Logistic regression for descriptive modeling
    • Logistic regression for classification
    • Understanding multiple regression analysis
    • Using RapidMiner correspondence analysis to visualize relationships between variables
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Who Should Attend

This course is suited for a variety of different experience levels. We invite anyone who is eager to learn more about data analytics and predictive modelling to join us.
More specific groups include:

  • Business managers who want to have a better grasp and understanding of data models and how to build a strategy based on this data.
  • Marketing and Financial Professionals who wish to improve their analytical skills
  • Individuals with a technical background interested in marketing or financial data analysis
  • Individuals with no technical background interested in exploring careers in data analytics

Career Opportunities

Data Analysts are in strong demand from industry; diverse as: pharmaceuticals, finance and insurance, as well as cloud
computing. Prospective employers include any company that requires detailed, robust analysis of data sets; some examples include:

  • ICT companies (e.g. Google, eBay, Facebook, Amazon, Paddy Power),
  • The pharmaceutical industry (e.g. Jansen, Merck, GSK),
  • The financial services industry (e.g. Bank of Ireland, AXA, EY, Accenture, Deloitte)


The course will introduce you to RapidMiner for predictive analytics which radically reducing the time to unearth opportunities and risks. If you have never used RapidMiner before, don't worry, we will help you along the way! We will show you how to install the software and explain corresponding concepts as we move through the material. However, we require you to understand basic business statistical concepts, including:
• Mean/average
• Standard deviation
• Normal distribution

Programme Accreditation

Upon successful completion of this course, students will be awarded an IBAT College Dublin Diploma in Predictive Analytics.

Do I need to access my own company's data?

No. A real-world consumer dataset will be provided for in-class data analysis.


To be awarded the Diploma in Predictive Analytics participants will be required to successfully complete project work (100%).
The project work will be based on real world case studies and data-sets.

What is the fee for this Predictive Analytics Course?

The Predictive Analytics course fees are €1250.

What do I get for my fee?

I would like to apply for the Diploma in Predictive Data Analytics course, what should I do?

Application can be made using the buttons above. Alternatively, please feel free to contact Richard O'Brien our Admissions Advisor for IT courses on 01 807 5055 to set up a meeting to discuss the course further. You can also email us at This email address is being protected from spambots. You need JavaScript enabled to view it.. Please feel free to visit the college anytime and a member of the Admissions Office will be more than willing to discuss any queries you may have. We look forward to seeing you.


Shared Student Experiences on this Course

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  • IBAT Student - Rosa Cordoba Cuartero

    I loved learning the processes in Rapidminer.

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ICT and Computing Lecturer Profiles

  • Mark Dean – Web Design / IS Lecturer

    Mark is an IT professional with over 20 years industry experience. Over this period he has accumulated knowledge and skills from numerous sectors ranging from education through to manufacturing and banking. He has been involved in many IT roles including software developer, team leader, systems analyst, and corporate IT trainer. Mark holds a Bachelor of Science from Trinity College and has lectured on both undergraduate and postgraduate programmes.

    Mark Dean, Web Design / IS Lecturer
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You can visit the campus and speak to a Course Specialist Mon-Fri 9am-5.30pm, no appointment needed. Or make an appointment on enquiry@ibat.ie