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Home > Training > Data Warehousing > Data Based Decision-Making Training

Data Based Decision-Making Training

Course#: WA2355

This course, designed for business analyst, marketing and business development staff, uses a case study approach to teach the skills used in data based decision-making.  Participants learn how to: prepare strategies for gathering information; identify underlying issues related to a decision; generate and evaluate multiple alternatives; communicate recommendations and design plans to implement decision.

Why Attend this Course?

Even though there is an abundance of data available for decision-making it is still difficult to translate data into actionable information.

This course will provide participants will tools and techniques that can be used to convert raw data into well-informed decisions.

Decisions are made based on the analysis without knowing how the underlying data looks.

‘Knowing the data’ is a precursor to decision-making.  This course will introduce an organized and structured approach to looking at the raw data and understand how it is transformed to actionable information.

What Makes this Course Stand Apart?

A case study approach using examples you are likely to encounter in the workplace.

The course maintains a focus on the interpretation of the results of an analysis to make decisions.

What You Will Learn

Upon completion of this course you will be able to:

  • Describe how various analytical techniques and statistical models can help enhance 
decision making by converting data to knowledge;
  • Select the appropriate statistical tools for the business question;
  • Apply statistical models to real decision challenges; and,
  • Support decisions by effectively communicating results of data analysis.


  • Business Analyst (IT and non-IT)
  • Business Managers
  • Project Leaders
  • Systems Analyst
  • Data Analyst


The structure of the courses assumes no prior experience in statistics or data analytics. 


1 day

Outline of WA2355 Data Based Decision-Making 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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