Course #:TP2821

Machine Learning for Business Analyst Training

Machine Learning 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, artificial intelligence, information retrieval, pattern recognition and bioinformatics. Machine learning for predictive analytics is widely used in many domains, such as retail, finance, telecommunication and social media.

This course provides an overview of various machine learning 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 machine learning issues using the techniques described and to recognize opportunities within their organization.

Note: This course uses a visually oriented, open source software package to process the data.  The class is not intended to be a programming class.  Instead, the software is used to examine the impact of different data mining decisions.


Upon completion of this course you will be able to:

  • Identify machine learning options available to solve business questions.
  • Plan for common data challenges.
  • Apply machine learning techniques relevant to the business question.


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


One Day.

Outline of Machine Learning for Business Analyst Training

Chapter 1. Introduction to Machine Learning

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

Chapter 2. Data Preparation

  • Integrating Data Sets
  • Data Reduction
  • Inconsistencies, Missing Data & Outliers

Chapter 3. Methods & Tools

  • Linear Regression
  • Logistic Regression
  • Classification & Regression Trees
  • Clustering
  • Association Rules
  • Neural Networks
  • Text Mining
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.