Predictive Data Analytics Course using RapidMiner for Business Analytics and Data Mining

Diploma in Predictive Data Analytics

Duration:
11 wks (PT), one evening per week, 6.30pm-9.00pm
Start Date:
19th February 2019
Days:
Tuesday
Accredited By:
IBAT
Reference Code:
CDIPPDA1P
Price:
€1250

Why Predictive Data Analytics

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

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 modelling 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.

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


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.

"Data really powers everything that we do."

Jeff Weiner, chief executive of LinkedIn 

"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

Reasons why this course is for you:

  • Are looking to change your role from 'observer' to 'strategist' and 'analyst' by applying Predictive Data Analytics tools and techniques to increase business performance?
  • Do you want to apply theory into practice and start building a more data-driven culture within your organisation?
  • Do you want to understand the art and science of predictive analytics to define clear actions that result in improved decisions making and business performance?
  • Do you want to develop actionable plans from existing corporate data and initiatives to increase sales, reduce marketing costs, and improve customer retention?
  • Do you want to select, prepare, construct, integrate, structure, and format data to be most effective to ensure the predictive model meets the business goals?
  • Do you want to understand the use and assist in the selection of industry standard analytics tools?
  • Do you want to integrate powerful and traditionally untapped sources of information including social data, unstructured text and Big Data sets?

Course Overview

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 the multivariate techniques used to transform information from large data-sets into actionable insights. You will gain hands-on experience using RapidMiner [a software platform for data preparation and predictive model generation] and prepare to be a part of this growing field.

The skills obtained through this programme 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 functions to influence the direction and decisions of the business on a day-to-day basis.

On the programme you will also be introduced to both ‘big’ and ‘small’ data; learn how to manipulate, analyse and interpret it in sophisticated ways; and realise how to present this in a persuasive and influential manner. It will teach you how to identify situations in which predictive analytics can add value by better meeting customer needs, through the smarter allocation of marketing budgets and by improving financial decisions.

Please Note: 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 be able to 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.

 

Course Content

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

 You will know when to use the following modelling techniques, and understand their relevant 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, class time will be allocated for you to work with RapidMiner, learning how to analyse and interpret a different multivariate statistical technique using a real-world dataset.

On completion of the programme, you will be able to apply learned theory and start building a more data-driven culture within your organisation.

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
      -Lift
      -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

Assessment

Participants will be required to successfully complete project work (100%), the project work will bebased on a real world case studies and data-sets.

Career Opportunities

According to Brian Mooney (Irish Times, 07 March 2017)

Data analytics was the fastest-growing skill in demand in 2015 and demand is set to continue in the years ahead.

Data Analysts are in strong demand in industries as diverse as: pharmaceuticals, marketing, finance and insurance, as well as cloud computing.=

Prospective employers, therefore, 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)

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.

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. Hence, 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.

Testimonials

My favourite aspect of the course was working & learning rapid miner.

Adrian Basiewicz Diploma in Predictive Data Analytics Graduate

Learned new skills, especially around Rapidminer and datasets.

Mr O’Neill Diploma in Predictive Data Analytics Graduate

Lecturer Profile

John Rowley
John Rowley/ Lecturer Data Predictive Data Analytics /
John Rowley's career spans web development (LAMP), Web Manager and Corporate spokesman (Ryanair), 11 years in software management and development (Symantec, Corel), technical training (PHP, MySQL, Perl, XML, C#, CSS Javascript), technical writing (C++, Java), documentation management and news journalism. He was the first PHP developer in Ireland to achieve Zend PHP Certification and the first technical writer in Ireland to document C++ compiler and application frameworks. He has worked as a consultant / developer for Irish Rail, Suzuki, Nestle, and Eli Lilly. He currently works as a .NET information architect for the ECDL foundation where he develops the centre and candidate administration systems for the ECDL programs in Ireland. On a lighter note, he's also an accomplished stand-up comedian, performing regularly in Manchester and Dublin

Entry Requirements

This programme is suited for individuals with a variety of different experience levels, and while there is no academic requirement for entry, all applicants should demonstrate an interest in and an aptitude for data analytics.

If you are eager to learn more about data analytics and predictive modelling, you should consider this programme. Specifically, if you are

  • a business manager, who wants to have a better grasp and understanding of data models and how to build a strategy based on this data
  • a marketing or financial professional, who wishes to improve your analytical skills
  • an individual with a technical background who is interested in marketing or financial data analysis
  • an individual with no technical background and are interested in exploring careers in data analytics.

The programme will introduce you to RapidMiner for predictive analytics which radically reduces the time needed to unearth opportunities and risks. If you’ve never used RapidMiner before we will help you to get started. We will show you how to install the software and explain corresponding concepts as we move through the material. However, it should be noted that while this is not a statistics course, all the techniques are based on the theory of statistics and you will need to understand basic business statistical concepts, including:

  • mean/average
  • standard deviation
  • normal distribution

How to Apply

Don’t miss out because you’re unsure.

You can contact or apply to us directly using the the "Contact Us" or "Apply Online" options below or at the top of the page, this will reserve your place and also give us the key details to start your application, a member of our admissions team will then contact you with further details, or alternatively you can simply "Ask us a Question" using the form below.

You can also "Book a One-to-One" with one of our course specialists who will provide you with additional guidance on your application, you can set up a meeting by:

  • Calling us at:  +353 1 8075 055 
  • Email us at:  This email address is being protected from spambots. You need JavaScript enabled to view it. 

We also have flexible payment plans, please contact us to discuss your options or visit the college anytime and our course specialists will be more than willing to discuss any queries you may have.

We look forward to meeting you.

Why IBAT College?

Career Potential
Maximise your career potential as you remain working while obtaining a professionally relevant, career enhancing, qualification at the same time.

Excellent Lecturing Staff
Learn from friendly and highly qualified academic staff, with industry experience, in small class-sizes, and guest lecturers who are experts in their fields.

Student Experience
Being part of a culturally diverse student nationality mix at IBAT College Dublin, with learners from over 30 different countries, gives you the opportunity to learn culturally different working methods first-hand, and the fundamentals to succeed in a globally connected network once you graduate.

City Centre Location
With our campus located in Dublin city centre, in Temple Bar, you are close to all major transport hubs making it easy to attend classes in between work and home.

Programme Material
Printed course notes are supplied for the first lecture. All subsequent notes are then available to download/view/print through the student virtual learning (Moodle) platform.

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Percentage of high-performing companies using real-time Predictive Analytics.
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Starting Salary for entry-level Data Analyst.
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Hottest skills ranking for 2018, Statistical Analysis and Data Mining - LinkedIn Ireland.

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