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.
The Gap: Mining Your Own Business
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?
We explore the heretical thought that leadership is getting in the way - that leaders often inadvertently nurture organizational inertia that diminishes, or completely eliminates, the chance for success with analytics. Learn instead how to harness its counter-intuitive insights, as illustrated by tales from the front lines of this emerging field.
Intended Audience: C-level Managers, Analytics Champion, Analytics Project Managers
Duration: 2 Hours
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 describes the key inner workings of leading algorithms, compare their merits, and briefly demonstrate their relative effectiveness on practical applications. The course emphasizes practical advice and focuses on the essential techniques of resampling, visualization, and ensembles. Actual scientific and business examples illustrate proven techniques employed by expert analysts.
Intended Audience: Analytics Champion, Analytics Project Managers, Decision Makers
Duration: 2 Day
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.
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.
Intended Audience: Data Scientists, Data Analysts, Analytics Managers, Business Intelligence Analysts
Duration: 2-3 Days
Data Wrangling in R
R is a an open-source programming language that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques. However, to take advantage of the wide statistical and visualization capabilities within R, the data itself must be in the right format. This data wrangling course provides a comprehensive overview of the tools and techniques needed to effectively transform data using the R language.
An overview of data science best practices for data ingestion, preparation, and wrangling for analysis provides a strong foundation for the course content. Students will query relevant databases using R, be introduced to Base R, and explore examples of data wrangling code using specific R packages. R programming concepts focus on exercises using real data.
What you will learn:
- An introduction to data science concepts and data preparation for analysis
- An introduction to R, how R is different, basic data types, and R/RStudio installation
- Overview of base R concepts and specific data wrangling packages in R
- Connecting to databases, executing database queries in R
- Common problems experienced in data wrangling
- Demo examples using canonical datasets
- An introduction to programming in R with applied data wrangling programming
Intended Audience: Data Scientists, Data Analysts, Business Intelligence Analysts
Duration: 4 Days (can be customized based on requirements)
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.
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.”
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.”