Data and Decision Making for Business Analysts and Marketing Professionals

The world of business is changing rapidly, and the change is driven primarily by the ready availability of data. The problem for the present-day business professional is not the lack of data, but surfeit of it. Flooded by data, it is often hard to figure out what to is important and what’s not.  If you’ve been struggling with this, look no further: our Data and Decision Making class is aimed at addressing this issue.

Our experience in teaching a wide-range of business professionals has convinced us that the issue is not that there is too much data, but that there is a lack of understanding about what data is needed to address the pressing issues faced by an organisation. To this end, we cover a range of sensemaking techniques that can help organisations figure out what data they need in order to address their most important problems. What is sensemaking? In short, it is the art of problem formulation. See this video for an example of sensemaking method.

Sensemaking is only part of the story

Business professionals also need an understanding of the newer analytical techniques of data science and machine learning. To this end we spend about half the course (1.5 to 2 days) on teaching the basics of machine learning in an intuitive, practice-oriented way. Starting with a familiar method (linear regression), we take participants through more complex regression techniques and other methods such as decision trees, random forests and (time permitting) neural networks. The aim is to give professionals an understanding of what these techniques can do for them and, just as important, what they cannot.

Sensemaking and analytics are complementary aspects of decision making in organisations. It is important that business professionals understand which of the two are needed for the problem they have at hand. Using analytical methods where sensemaking is required (or vice versa) will end in tears…and a lot of wasted dollars.

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.