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Home > Training > Data Warehousing > Data Mining - A Practitioner's Course Training

Data Mining - A Practitioner's Course Training

Course#: WA2356

This course provides an overview of various data mining techniques with examples of how they are used in various organizations such as retail, finance, biotechnology and social media.  Case studies are used to allow participants to work through several data mining issues using the techniques described.

Data mining is the process of discovering interesting knowledge from large amounts of data. It is an interdisciplinary field with contributions from many areas, such as statistics, machine learning, information retrieval, pattern recognition and bioinformatics. Data mining is widely used in many domains, such as retail, finance, telecommunication and social media.

What Makes this Course Stand Apart?

The course presents up-to-date data mining methods currently in use by organizations.

A broad set of examples will help participants recognize opportunities to apply data mining within their organization.

What You Will Learn

Upon completion of this course you will be able to:

  • State the business opportunity as an analytics question.
  • Identify the data mining options available to solve the business question.
  • Plan for common data challenges.
  • Apply data mining techniques relevant to the business question.


  • BI and Analytics Managers
  • Business Analyst (IT and non-IT)
  • Data Analyst
  • Database Administrators
  • Project Leaders
  • Systems Analyst
  • Data Analyst


This course will be accessible to students without prior training in quantitative research methods. However, students with a background in basic descriptive and inferential statistics will, most likely, get more out of the course.


2 Days

Outline of WA2356 Data Mining - A Practitioner's Course Training

1. Introduction to Data Mining

  • Descriptive and Predictive
  • Models and Algorithms
  • Regression vs. Classification
  • Supervised/Unsupervised Learning
  • Data Mining Examples

2. Data Analytics Framework

  • Following a Process
  • Potential Data Problems
  • Analyzing and Exploring the Data

3. Data Mining Methods

  • Descriptive Methods
  • Clustering
  • Association Rules
  • Sequence Rules
  • Predictive Methods
  • Classification
  • Regression
  • Deviation Detection
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
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