Mark Whitehorn specialises in Analytics and Data Science.

Mark works with national and international companies, designing Data Science solutions.  In addition to his consultancy practice he has also acted as an expert witness in cases of patent infringement and for the police in cases of computer fraud.


He is a well-recognised commentator on the computer world.  He is a regular contributor to The Register, has written numerous white papers and also eleven books on databases and analytics. The first one, Inside Relational Databases has been selling well since it was published in 1997 and is now in its third edition. It has also been translated into three languages. 

Mark is also an associate with QA Ltd.  He has developed several of the company's courses (data science and big data course, database analysis and design, MDX, Dimensional modelling) and teaches them all.

On the academic side, Mark is the emeritus Professor of Analytics at the University of Dundee where he designed and runs a Masters course in Data Science.  There he also works with the prestigious Lamond labs. applying Analytics and Data Science to proteomics

For relaxation he collects, restores and races historic cars which keeps him out of too much trouble. He only wears a tie under duress, doesn't possess a suit that fits and unashamedly belongs to the beard-and-sandals school of computing.


Why and when denormalisation makes sense.
This talk looks at MDX and DAX, examining their similarities and differences. It won’t turn you into an expert in either but it will help you to decide, given your particular career plans, if either or both are worth learning.
There are some little-known but very useful ways of extracting information from data. This session will cover: • Monte Carlo simulations • Nyquist’s Theorem • Simpson’s paradox • Benford’s Law These will rock your world (they certainly rocked mine).
Analytics is all about having a good set of analytical tools. Last year at Sqlbits I outlined 4 such tools, this session will expand on that set. We’ll cover Dark Data, Probability calculations, RFI
Algorithms like Neural Nets, Deep Learning, SVM and KNN are dominating the Machine Learning world. You don’t need to know the maths behind them but you do need to know how they work in order to use them effectively. This talk will explain just that.
Graph databases – Origins, how they work, strengths and weaknesses
Introducing the ROC curve and its use in Azure ML.