Training builds a common foundation and vision for analytics across business units, helps identify resource or process gaps, and empowers an organization’s team to take ownership of the process.

Elder Research is a leader in advanced analytic training. Senior members of the team explain complex concepts efficiently to non-specialists as evidenced by the success of our well-regarded training seminars on Data Mining and Pattern Discovery, delivered to dozens of companies, universities, labs, and government agencies over the last decade.

Elder Research offers a variety of analytics training services, which provide an overview of the data science process and data mining tools and techniques. Additionally, custom on-site courses can be designed for your organization using data examples from your industry.

Talk to an expert about analytic training

Developing a Culture for Analytic Success

An Executive Overview for Deriving Value from Data

This interactive instructor-led analytics course provides practical wisdom on the organizational and technical keys for achieving high return on investment in analytics.

One of the strongest indicators of future analytics success is to develop a work culture that supports and nurtures analytics. This webinar describes the best ways to build an effective analytics culture and institutionalize analytics-based decision making. The speakers will describe how to get started, choose the right project, build a team, select the right tools, obtain stakeholder buy-in, and achieve other vital organizational goals.

The different levels of analytics are reviewed and key terminology are defined, enabling clear understanding and effective communication of technical goals within an organization.

Real world business examples from multiple industries illustrate how leading organizations employ analytic solutions to transform data into insight, make more informed decisions, and improve business value.

Analytics Concepts Course

Tools for Discovering Patterns in Data: Extracting Value from Tables, Text, and Links

This instructor-led analytics training surveys computer-intensive methods for inductive classification and estimation drawn from statistics, machine learning, and data mining. The course will describe the key inner workings of leading algorithms, compare their merits, and briefly demonstrate their relative effectiveness on practical applications.

Get more information on this in-person or online course

Analytics Practitioner Course

Data Mining: Principles and Best Practices

Data mining is an advanced science that can be difficult to do correctly. The instructor-led Analytics Practitioner course introduces participants to the power and potential of data mining and shows how to experience, focuses on how to properly build reliable predictive models and interpret the results with confidence. Examples are drawn from several industries, including credit scoring, fraud detection, biology, investments, and cross-selling. The course is intended for participants with a strong interest in solving analytical business problems and who have a technical background, especially familiarity with computer programming and statistics.

In this course, participants will learn how to:

  • Identify projects with a high probability of success
  • Translate a business problem into a closely related set of technical tasks
  • Transform raw data to create higher-order features that reveal information
  • Build models using powerful nonlinear techniques, such as decision trees and neural networks
  • Use multiple re-sampling techniques to avoid overfit and predict how well models will perform in actual use
  • Interpret and validate modeling results
  • Become aware of the most common analytic mistakes and how to avoid them.

Making Text Mining Work

Practical Methods and Solutions

Text Mining is the science of leveraging textual data for data mining. Text, a type of unstructured data, is challenging due to the richness and complexity of language, but holds enormous potential due to the sheer volume and depth of available textual data. Text mining and text analytics can be valuable tools, if you know where to look for the solution.

This course describes the leading text mining algorithms, demonstrates their performance with business case studies, compares their merits, and how to pick the approach best suited for a project. Methods covered include search indexes, text classification, information extraction, document similarity and more.

What you will learn:

  • The key to successfully leveraging text mining methods and understanding the limits of those techniques.
  • How to set positive but realizable expectations for the return on investment from a text mining project.
  • How to choose the proper text mining solution and combine technologies to maximize the value of the vast store of unstructured data.
  • Examples of the top analytics mistakes and how to avoid them.

Tools Training Course

Elder Research has developed analytical models in numerous tools and has experts on staff for many of the common analytic software packages available. A sample of the tools with which Elder Research has demonstrated expertise includes:

  • IBM SPSS Modeler
  • SAS Enterprise Guide
  • SAS Enterprise Miner
  • JMP Pro
  • R
  • Dell/Statsoft Statistica. 

Talk to an expert about analytic training

Training Clients


  • Affecto – Norway and Finland
  • AFS
  • American Management Systems
  • AT&T Bell Labs
  • BBVA Bancomer
  • Becton-Dickinson
  • Bell Atlantic
  • Capital One
  • Citibank (England)
  • Continental Airlines
  • Daimler-Benz - Germany
  • Daman Health
  • Dow AgroSciences
  • Edward Jones
  • Electronic Warfare Associates
  • Fannie Mae
  • Federal Data Corporation
  • FM Global
  • Glaxo-Smith-Kline
  • Glaxo-Wellcome (now part of GSK)
  • Great American Insurance Group
  • HSBC
  • Insurance Services Office
  • Johnson & Johnson
  • Kabbage, Inc
  • Khol’s
  • Lindner Funds
  • Martin Marietta
  • MetLife
  • Nationwide
  • Peoples Bank
  • Peregrine (now part of HP)
  • Pharmacia & Upjohn
  • Pitney Bowes
  • Proctor & Gamble
  • PSCU Financial Services
  • Rio Grande Medical Technologies
  • Salford Systems
  • SAS – US, Mexico, UAE
  • Smith-Kline Beecham (now part of GSK)
  • Southwest Research Institute (SWRI)
  • SunTrust
  • The Nature Conservatory
  • Thomas Industrial Network
  • UNI Strategic – Malaysia
  • World’s Foremost Bank (part of Cabela’s)


  • American Public University System
  • Baylor Medical School Urology
  • Fairfield University School of Business
  • George Mason Applied Engineering & Statistics
  • Harvard - Business School, School of Public Medicine
  • MIT Sloan Business School
  • Rice University - Computational and Applied Mathematics, Statistics, Continuing Education
  • University of Alabama at Huntsville Computer Science
  • University of Tennessee Knoxville Statistics
  • University of Virginia - McIntire School of Commerce, Darden Business School, Systems and Information Engineering, Statistics
  • Virginia Polytechnic Institute Aerospace and Oceans Engineering
  • Wheaton College

Government Agencies

  • Defense Finance and Accounting Agency (DFAS)
  • Department of Labor (DOL)
  • Internal Revenue Service (IRS)
  • National Academy of Sciences
  • National Science Foundation (NSF)
  • National Security Agency (NSA)
  • Social Security Administration (SSA)
  • Naval Weapons Center

Professional Associations

  • American Marketing Association (AMA)
  • American Statistical Association (ASA)
  • Applied Statistics Society
  • Biopharmaceutical Society
  • Gordian Institute
  • IEEE Conference on Data Mining (ICDM)
  • IEEE Systems, Man & Cybernetics Society
  • INFORMS Roundtable
  • Institute for Professional Education
  • Institute of Electrical and Electronics Engineers (IEEE)
  • Interface Foundation of North America
  • International Communications Forecasting
  • Knowledge Discovery & Data Mining (KDD) – US, France
  • Midwest Biopharmaceutical Statistics Society
  • Operations Research Society
  • Pharmaceutical Management Science Association (PMSA)
  • Predict Conference – Ireland
  • Predictive Analytics World (PAW) – US, UK, Canada
  • Richmond Society of Financial Analysts
  • Society for Industrial and Applied Mathematics (SIAM)
  • Systems Engineering Society – US, Chile
  • Virginia Piedmont Technology Council

Testimonials from Training Attendees

"I appreciated the two day course and enjoyed it more (and absorbed more) the second time around. I really appreciate [Dr. Elder’s] ability to explain complex concepts using stories and analogies. It is a rare gift and he obviously has refined it.  I also liked all of the pictures, diagrams and visualizations.” 

Casimir Saternos
Software Architect
Synchronoss Technologies, Inc.

“Exceeded expectations. Very good for learning more detail in a snapshot and broadening specific understanding. Very applicable for people out in the field.”

“A solid overview of the map of the territory of data science, allowing you to drill in on pieces that will help you the most.’

"[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."

“A very interesting overview of data science highlighting best practices, pitfalls to avoid and John’s experiences with both.”

“A great overview of the key data mining skills and trends, and value provided in all industries.”

“A great wide intro into important data science topics. Ensembling, sampling techniques and metric evaluations were the most useful topics covered.”

“A modern approach to delivering insights from data and assessing your confidence in those insights.”

“Entertaining and informative. An Overview of the methods and applications of data mining with emphasis on best practices.”