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
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.
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.
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
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:
• Standard deviation
• Normal distribution
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?
- Full set of bound notes
- Access to your online student portal (view student portal site)
- Content Expert Lecturer
- Excellent college learning and information facilities (view college photos)
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