Home / Solutions / Services / Training / Workshop: Tools and Techniques used in Data Science and Artificial Intelligence

Workshop: Tools and Techniques used in Data Science and Artificial Intelligence

June 13-14, 2024 | Arlington, VA

Learn to Solve Critical Analytics Problems

Drawing on 30 years of experience, Dr. John Elder will explain techniques employed by experts to solve challenging problems.

This course describes powerful analytic methods for classification and estimation drawn from Statistics and Data Mining.

Register Now

What Will You Learn?

Find the useful information hidden in your data!  This course will cover:

• Algorithmic methods for inductive classification and estimation

• Key inner workings of top algorithms

• Classical linear and nonparametric statistical techniques

• Essential techniques of Resampling, Visualization, and Ensembles

• How to successfully implement Data Mining solutions through practical lessons in real-world challenges

Details:

Arlington, VA
(location to be provided)

June 13-14, 2024

8:30am-5:00pm (with 8:00am check-in on Thursday). Snacks provided during breaks.

Course:

Live instruction. Dozens of Real-World examples. Dynamic Q&A.

$1,299 per participant.
Discounts for multi-students and government employees.

What Do Our Students Say?

  • "[Dr. Elder] provided examples shedding light on complex concepts. He gave the big picture all along the way."
  • "Gave real practical insights from a practitioner's point of view."
  • "Finally someone told me how things are done, not just how great data mining is."
  • "Most valuable were the insights into the essence of various methods, their relative strengths and weaknesses, and the important open research areas."
  • "Very interesting, knowledgeable, and entertaining approach."

Course Outline

Data Science: An Overview

 

  • Inducing Models from Data: Benefits & Dangers
  • Example Projects from Science and Business
  • Characteristics of successful projects

Classical Statistical Techniques

 

  • Regression, and Regularization (Ridge, LASSO)
  • Principal Components
  • Nearest Neighbors

Modern Methods

 

  • Decision Trees
  • Neural Networks
  • Random Forests

Key General Tools

 

  • Visualization tricks
  • Bootstrapping/Resampling (Essential!)
  • Optimization
  • Overfit Control

Data Trouble-Shooting

 

  • Case Diagnostics (Outlying, Influential, Leverage, & Missing points)
  • Feature Creation and Selection

Crisis in Science

 

  • Multiple-comparison trap
  • Target Shuffling solution

Comparing and Combining Adaptive Algorithms

 

  • Matching an algorithm to your application
  • Combining models to improve accuracy
  • Bundling, Bagging, Boosting
  • Why Ensembles work

Top 10 Data Mining Mistakes

 

  • Lack data
  • Focus on Training
  • Rely on 1 technique
  • Ask the wrong question
  • Listen (only) to the data
  • Future leakage
  • Discount pesky cases
  • Extrapolate
  • Answer every inquiry
  • Sample without care
  • Believe the best model

Cognitive Biases

 

  • How to get your model into production

Meet Your Instructor

John F. Elder IV, Ph.D.
Founder

John Elder founded Elder Research, America’s most experienced Data Science consultancy in 1995. Elder Research has offices in Virginia, DC, Maryland, and North Carolina.

Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker.

John is an occasional Adjunct Professor of Systems Engineering at UVA, and was named by President Bush to serve 5 years on a panel to guide technology for national security.

Ready to start learning?

Register for the Workshop