Event Speakers

Data Science, Machine Learning, and Artificial Intelligence are revolutionizing decision-making and optimizing business processes in nearly every commercial and government sector. Drawing on decades of practical experience, our speakers make analytics understandable and accessible within your organization.  

Need a Keynote Speaker for Your Event? 

Our speakers draw on decades of industry and academic experience as they share lessons learned and best practices for managing analytics initiatives at international conferences and corporate events.  

Please complete the form if you would like to discuss requirements for your speaking engagement. 

The Gap: Is It Leadership? 

An Executive Overview for Harnessing Analytics Insight 

Data Analytics is a hot topic, and deservedly so. It powers exponential growth in modern behemoths like Google and Facebook but also drives positive transformation in ancient businesses and agencies – helping to cut costs, uncover fraud, discover new markets, etc. Still, many analytic initiatives are never implemented, though they are complete technical successes; they are proven to work but never given the chance. What is going wrong?  

 AI Needs HI to Succeed 

Artificial Intelligence (AI) is suddenly shaping our everyday world. We share the road with autonomous vehicles, can be medically diagnosed by a computer, or can unlock our phone with a glance. At its core, AI is human knowledge captured in a computer algorithm which, in turn, is based on historical information provided by a human. It’s impossible to escape the human role in AI’s equation! This presentation will provide practical examples where Human Intelligence (HI) has focused and maximized the value of AI.  

John is represented by Chartwell Speakers Bureau. To book John as a speaker for your event, please email ellis@chartwellspeakers.com

The Data Science Revolution in Industry 

The hype around “the thinking sciences” — Artificial Intelligence, Machine Learning, and Data Science — is enormous, so it’s tempting to be skeptical of the gains claimed. The capabilities of Data Science, where models are inductively built from real history, have been growing steadily. Dr. Elder reveals as examples five of Elder Research’s most interesting fielded solutions from the last two decades, from the diverse worlds of investment timing, credit scoring, drug discovery, medical diagnosis, and gas exploration. 

The Greatest Science  

Data Science, if judged as a separate science, exceeds its sisters in truth, breadth, and utility. Date Science finds truth better than any other science; the crisis in replicability of results in the sciences today is largely due to bad data analysis, performed by amateurs. As for breadth, a data scientist can contribute mightily to a new field with only minor cooperation from a domain expert, whereas the reverse is not so easy.  And for utility, data science can fit empirical behavior to provide a useful model where good theory doesn’t yet exist. But only if we do it right!  The most vital data scientist skill is recognizing analytic hazards.   

Top 3 Things I’ve Learned in 3 Decades of Data Science 

The three most important analytic innovations I’ve seen in three decades of extracting useful information from data have to do with:  Ensemble models, Target Shuffling, and Cognitive Biases. Ensembles are sets of competing models that can combine to be more accurate than the best of their components. Target Shuffling is a resampling method that corrects for “p-hacking” or the “vast search effect” where spurious correlations are often uncovered by modern methods’ ability to try millions of hypotheses. Third, the increasing understanding of our own Cognitive Biases, and how deeply flawed our reasoning can be, helps reveal how vital projects can be doomed unless we seek out — and heed — constructive critique from outside.   

Luck, Skill, or Torture? How Target Shuffling Can Tell 

When you mine past data and find a pattern or quantitative model, to what degree is it real, or chance? Ancient Statistics geniuses devised formulas to answer this for special-case scenarios. Yet, those only apply to handmade analyses where a few hypotheses are considered. But modern predictive analytic algorithms are hypothesis-generating machines, capable of testing millions of “ideas.” The best result stumbled over in their vast searches have a great chance of being spurious, leading to failing models molded to noise. The good news is an antidote exists! Dr. Elder reveals the simple technique of “Target Shuffling” and illustrates how it has helped in real-world projects – in the stock market, medical research, gas exploration, and baseball.  

What To Optimize? The Heart of Every Analytics Problem 

Every analytics challenge reduces, at its technical core, to optimizing a metric. Product recommendation engines push items to maximize a customer’s purchases; fraud detection algorithms flag transactions to minimize losses; and so forth. As modeling and classification (optimization) algorithms improve over time, one could imagine obtaining a solution merely by defining the guiding metric. But are our tools that good? More importantly, are we aiming them in the right direction? I think, too often, the answer is no. I’ll argue for clear thinking about what exactly it is we ask our computer assistant to do for us and recount some illustrative war stories. (Analytic heresy guaranteed.) 

Top 5 Technical Tricks to Try When Trapped 

There’s no better source for tricks of the analytics trade than Dr. John Elder, the established industry leader renowned as an acclaimed training workshop instructor and author — and well-known for his “Top 10 Data Mining Mistakes” and advanced methods like Target Shuffling. In this webcast, Dr. Elder, who is the Founder of Elder Research, North America’s largest pure play consultancy in predictive analytics, will cover his Top Five methods for boosting your practice beyond barriers and gaining stronger results.  

Doing Space-age Analytics With our Hunter-Gatherer Brains 

Predictive Analytics is so powerful and useful everywhere. Its modest risk and phenomenal return should lead should lead to widespread use, yet most projects fail to be implemented.  What is going on?    

Success actually requires solving three serious challenges: 1) Convincing experts that their ways can be improved, 2) Discovering new breakthroughs, and 3) Getting front-line users to completely change the way they work.   

John argues that it’s helpful to have a mental model of the human brain as not optimized for success in our modern life of safety and abundance, but for survival within a small tribal society. And that with this model we can better anticipate – and escape – the traps that we idealistic techno-nerds tend to blunder into as we try to bring life-changing fire into the tribal circle. 

Can Machines Think? 

Data Science has been able to tremendously improve decision accuracy and productivity through capturing patterns from the past to predict the future.  It can be said to learn general principals from experience.  But isn’t this humankind’s greatest distinctive?  As machines master complex tasks once thought to be beyond automation, can they be said to think?  (Eventually, will they take over?  Merge with us?  Be awesome sidekicks, or eliminate us?)  And, meanwhile (setting aside for a moment frets about extinction), what meaningful work will be left for us to do? Come hear musings – dark and light – on the (near) future of life with our creations. 

How to Tell if Your Market Timing System Will Work 

A New Measure of Model Quality 

The most widely used measure of investment performance is the Sharpe ratio, which is simply the “excess” return of an investment divided by its volatility.  It defines an efficient frontier of optimal alternatives at different risk levels.  Yet, the Sharpe ratio really reveals the quality of your returns and not the quality of your strategy.    

In this talk, Dr. Elder will describe a measure capable of evaluating the information quality of a market timing system.  It takes into account not only return and volatility, but also the trend of what is being traded and the system’s exposure (% in vs. out).  Most importantly, the new metric is a better predictor of which timing systems will succeed in the only place that matters:  tomorrow.  

Workshop: The Best and the Worst of predictive Analytics 

Predictive Modeling Methods and Common Data Mining Mistakes 

Predictive analytics has proven capable of enormous returns across industries – but, with so many core methods for predictive modeling, there are some tough questions that need answering: 

  • How do you pick the right one to deliver the greatest impact for your business, as applied over your data? 
  • What are the best practices along the way? 
  • And how do you avoid the most treacherous pitfalls? 

This one-day training session surveys standard and advanced methods for predictive modeling. Dr. Elder will describe the key inner workings of leading algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to pick the method and tool best suited to each predictive analytics case.  

If you’d like to become a practitioner of predictive analytics – or if you already are one and would like to hone and broaden your skills across methods and best practices, this workshop is for you! 

What you will learn: 

  • The tremendous value of learning from data 
  • How to create valuable predictive models for your business 
  • Best Practices by seeing their flip side: Worst Practices