Machine Learning for Business Analyst Training

Duration: 
1 days
Codes: 
TP2821

Overview

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.

Audience

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

Skills Gained

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.

Prerequisites

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.

Course Outline

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

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

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