Hands on Machine Learning for Business Intelligence Professionals

The world of the business intelligence (BI) professional has changed rapidly in the last five years…and is set to change further. As a seasoned BI professional, you know your way around relational databases, SQL and tools for ETL and reporting. More likely than not, you’ve heard about data science and machine learning, and are keen to find out more about the exciting possibilities they offer.

Look no further.  Our 3 to 5 day Hands on Machine Learning for Business Intelligence Professionals is intended for BI professionals who are looking for a quick, practical introduction to machine learning.  Our course will not only teach you enough to get started on doing data science in your workplace, we also cover important aspects of the craft of data science that most of our competitors overlook.

How we Differ From our Competitors?

An important point to keep in mind when shopping around for training is that practical data science is more than techniques and algorithms, one also has to know how to formulate good problems and, more important, gain buy-in for data science in the senior echelons of the organisation.

Most data science courses teach technical stuff, but not address the all-important question of how to get started and gain management support.

That’s our edge. In addition to covering the fundamentals of data science in a code-centric, practice-oriented way we:

Offer practical, proven tips on how to gain buy-in for data science projects from senior management.
Teach participants how to  formulate data science problems based on their data and business problems.

Please contact us to find out more about our unique approach to helping you build  data science competencies in a way that takes into account your unique organisational context.

1-3 Day Courses

Day 1

  • Introduction & Overview
  • What is predictive analytics, data science & machine learning?
  • Different types of machine learning
  • The process of building & testing a machine learning model
  • Introduction to your coding environment

Day 2

  • Practical exercises in regression models
  • Practical exercises in classification models
  • Practical exercises in clustering models
  • Other algorithms as relevant.

Day 3

  • Capstone project – bring together what you have learned! Students can bring their own data and a work problem to develop a solution for.
  • Where to from here? Successfully bringing your new skills into the workplace.