Workshop – The Deadly Dozen: The Top 12 Analytics Mistakes and the Techniques to Defeat Them
Thursday, June 7, 2018 in Las Vegas
Full day: 8:30am – 4:30pm
Room: Emperors I
Intended Audience: Interested in advanced machine learning techniques.
Knowledge Level: For this intermediate-level workshop, it is helpful for attendees to already be familiar with the basics of machine learning methods.
Companion Workshop: This workshop is the perfect complement for Dr. Elder’s other one-day PAW workshop, “The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques,” although both workshops stand alone and may be taken in either order.
Workshop Description
This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.
This workshop covers:
- Antidotes: the best practices that overcome the most common flawed practices
- Intuitive explanations of resampling methods that ensure your models work on new data
- Practical tips for both the hard and the soft skills that will see your project through to implementation
In this workshop, renowned practitioner and hugely popular instructor Dr. John Elder will survey the most advanced analytics tools in the practitioner’s toolkit, with particular emphasis on resampling tools – such as cross-validation and target shuffling (a method to avert p-hacking devised by Dr. Elder) – which reveal the true accuracy of your models.
Workshop topics also include visualization, feature engineering, global optimization, criteria of merit design, ensembles, and “soft” factors that affect success, such as human cognitive biases. Attendees will also leave with an understanding of the inner workings of the most popular algorithms – including regression, decision trees, nearest neighbors, neural networks, bagging, boosting, and random forests.
Throughout the workshop day, Dr. Elder will share his (often humorous) stories from real-world applications, illuminating the technical material covered.
If you’d like to become a more expert practitioner of predictive analytics, this workshop is for you.
What you will learn:
- The 12 subtle pitfalls to watch out for on any new project
- The latest ways to increase the value of predictive models and machine learning for your business
- How to succeed when your biggest threat is not technology, but people (e.g., resistance to change)
Why Attend?
View Dr. Elder describing his course, “The Deadly Dozen,” in this brief video:
Schedule
- Workshop starts at 8:30am
- Morning Coffee Break at 10:30am – 11:00am
- Lunch provided at 12:30pm – 1:15pm
- Afternoon Coffee Break at 3:00pm – 3:30pm
- End of the Workshop: 4:30pm
Coffee breaks and lunch are included.
Free books:
Attendees receive a free copy of John Elder’s ebook Handbook of Statistical Analysis and
Data Mining Applications, a PDF of all training slides from this workshop, and an official certificate of completion at the conclusion of the workshop.
Special offer: Register for both this workshop as well as Dr. Elder’s other one-day PAW workshop, “The Best of Predictive Analytics: Core Machine Learning and Data Science Techniques” (complementary but not required), and also receive his co-authored book Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions (in Kindle form).
Instructor
Dr. John Elder, Founder and Chair, Elder Research
John Elder leads America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Washington DC, Maryland, and North Carolina. Dr. Elder co-authored books on data mining, ensembles, and text mining — two of which won book-of-the-year awards. John was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. Dr. Elder is an (occasional) Adjunct Professor of Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.
