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Data Mining for Business (Virtual Instructor-Led Training)

Data Mining for Business (Virtual Instructor-Led Training)

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  • 495.00



Course Summary

Data mining, often referred to as data science is the  hot new  discipline providing a unique  competitive  advantage for businesses  today. Its  growing importance is recognized by  Harvard Business professor Thomas Davenport’s best selling business book ‘Competing on Analytics’ which is a testimonial to the ever-growing importance of this discipline. Intuitive-based decisions and one's prior experience is becoming more the exception rather than the rule. Businesses are now adopting the discipline of data mining  to provide a more quantitative approach in their decision-making.

With the explosion of information, businesses are now able to develop  solutions  and evaluate their performance after their deployment. But how is data mining used to develop solutions and more importantly how do companies both action and evaluate these  solutions. As with any discipline, though, there is a process and approach that is critical in creating the necessary steps for building successful solutions. This course is about how institutions become entrenched in this discipline utilizing the four step data mining methodology. Adopting this four step approach, both simple as well as advanced solutions are presented as valid business outcomes depending on the business problem. But the course' focus on data reinforces the notion that data is the key in building successful data mining solutions.

Within this data mining process and through many years of experience in building analytical solutions, much learning has amassed on what works and what does not work. Through this course, this prior learning is leveraged as learners are provided with a comprehensive perspective on how to both build and deploy predictive analytics within their respective organizations. Numerous case studies within organizations demonstrate the increasing significance of data mining  as a core business discipline.

 

 

Course Contents

Program

Objectives

Duration

Data Mining 1

An introduction to Data Mining and its growing significance as a corporate imperative

What is data mining and what are the benefits and associated business applications.

The practical use of data mining within Big Data vs. Small Data environments with concrete examples of solutions both within social media and mobile applications.  

Identification of the most basic data mining solutions such as customer profitability, RFM and other loyalty measures and examples of their use in solving business problems.

3 modules of 90 minutes each

Data Mining 2


Understanding Data: The real core to data mining success

 

What is a data audit and how is it used in defining what data is useful in a data mining exercise.

Differences between internal and external data and how they are used in data mining solutions.

Understanding the different data types (binary, ordinal, and interval) and how raw data is manipulated in variable creation or the feature engineering process.

Creating the analytical file, which is the final deliverable before the analysis stage.

How data is linked and summarized in order to create this analytical file.

3 modules of 90 minutes each

Data Mining 3


Applying the right analytical tools to build the data mining solution

 

Applying various advanced analytical techniques and when they are used within the data mining process.

Differences between predictive analytics solutions and segmentation, and how they can  be integrated into the overall solution.

Case studies of how these analytics solutions have been applied across different industry verticals (retail, banking, insurance, travel, etc.).

Best practices in building data mining solutions.

Do’s and Don’ts of Data Mining.

3 modules of 90 minutes each

 

Course Instructor: Richard Boire

Richard's experience in database marketing and predictive analytics dates back to 1983, when he received an MBA from Concordia University in Finance and Statistics. His initial experience at organizations such as Reader’s Digest and American Express allowed him to become a pioneer in the application of predictive modelling technology for all direct marketing programs. This extended to the introduction of models which targeted the acquisition of new customers based on return on investment.
Richard is a recognized authority on predictive analytics and is among a very few, select top 5 experts in this field in Canada, with expertise and knowledge that is difficult, if not impossible to replicate in Canada. This expertise has evolved into international speaking assignments and workshop seminars in the U.S. , England, Eastern Europe, and  Southeast Asia.
He has co-authored white papers on the following topics: ‘Best Practices in Data Mining’ as well as ‘Customer Profitability:  The State of Evolution among Canadian Companies’.  In Oct. of 2014, his new book on “Data Mining for Managers - How to use Data (Big and Small) to Solve Business Problems” was published by Palgrave Macmillian.

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