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Curriculum

1. The Data Science Process

   In this session, participants will be exposed to the basic process of working with data.  This session will discuss the challenges and ideas related to those steps and lay a foundation for the remainder of the workshop.

2. Introduction to R

   The R programming language is a cornerstone of modern data science. In this hands-on session, participants will explore the basics of R and the tidyverse.  

    

3. Planning for Data Quality

   Data quality is a critical consideration. Practical tips to avoid data quality problems through better planning for data collection and structure will be covered.

4. Simulation and Bootstrapping

   In this session participants will examine tools that allow them to model and study random processes. These tools will be used to help participants understand machine learning in the data science process.

5. Introduction to Machine Learning Models

   Machine learning models are powerful tools for eliciting the story behind the data.  This session will give an overview of supervised and unsupervised machine learning and demonstrate methods of evaluating these algorithms.

6. Machine Learning for Classification

   This session will help continue the exploration of supervised machine learning models.  This session will introduce programming machine learning algorithms for classification. Students will explore the fundamentals of the decision trees and k-nearest neighbors algorithms and discuss evaluating these algorithms.

7. Ensemble Methods in Machine Learning

   Ensemble methods combine multiple machine learning models to refine and improve models.  This session will expose students to these ensembles and techniques such as random forests.

8. AI and Neural Networks

   Neural networks have broad applications including much of what we think of as Artificial Intelligence (AI). In this session, participants will see the basics of training and using neural networks.

9. Simulation with Process Playground

   This session will introduce participants to the powerful discrete event simulation software.

    

10. Unsupervised Learning

    Machine learning is adept at finding hidden groups and relationships in data. This session allows participants to explore algorithms for unsupervised learning such as hierarchical clustering, and k-means clustering.

11. Using Process Playground

    This session will give participants more hands-on experience putting Process Playground to use in simulation.

12. Applying Machine Learning in Process Improvement

    This final session will allow students to look back at the machine learning tools and apply them to a process improvement scenario.