PDC 401: Introduction to Big Data and Data Analytics – NEW!

Mwangi Ndonga, CIH, CSP, CHMM Lead Instructor
Ovintiv
Denver, CO 
United States of America
 
Evgeny Andronov Instructor
Whiting Oil & Gas
Denver, CO 
 
Lohrasp Seify, M.Sc., MCSE Instructor
Blackline Safety
Calgary, Alberta 
Canada
 
Kyle Krueger Instructor
Industrial Scientific
Highlands Ranch, CO 
United States of America
 
Sun, 5/31: 8:00 AM  - 5:00 PM 
Professional Development Course 
Georgia World Congress Center - Building B 
Room: B206 
CM Credit Hours:

Description

This course will introduce IH/EHS professionals to the basic concepts of big data, data analytics, internet of things (IoT), and machine learning. Topics will address: a) new responsibilities for the industrial hygienist with Big Data - defined as data that has variety, velocity, and volume; b) where industrial hygiene ends and data science begins; c) how to interact with other disciplines (i.e., IT professionals, data scientists); d) the growing use of mobile data capture tools and applications; e) standardization during data capture; f) data maturity of IH programs and organizations; g) data visualization and virtualization; h) data architecture; and i) common machine learning algorithms. Industrial hygiene and EHS case studies will be used to convey the course information. Attendees will have the opportunity to participate in a machine learning exercise.

*Registration includes boxed luncheon.

**Please note: power strips will not be provided at each attendee's seat; however, charging stations for laptops will be provided in each course room.

Course Outline

• Overview of Big Data, Machine Learning, and Artificial Intelligence
• Ethics Related to Machine Learning Algorithms and Artificial Intelligence
• Practical Ways to Scope Projects
• Communicate with Information Technology Professionals and Data Scientists
• Business Optimization Opportunities for Industrial Hygienists
• Case Studies Using Data Science Concepts: 1) Asset Integrity Management 2) Real-time Sensor/Devices 

Learning Outcomes

Upon completion, the participant will be able to:

• Describe the foundations of data science.
• Define big data, machine learning, and artificial intelligence.
• Demonstrate how data analytics enhances the anticipation, recognition, evaluation, control, and verification of hazards.
• Distinguish between supervised and unsupervised machine learning.
• Cite occupational/environmental health case studies applying data science tools.
• Discuss two simple algorithms: K-means and Naïve-Bayes.
• Apply an algorithm by authoring a short script using industrial hygiene data. 

Prerequisites

Attendees should have a comfort, but not necessarily an expertise with statistics. 

Value Added

Attendees will identify the stages of maturity within their organization, apply data science tools/concepts to advance industrial hygiene within their practice, and be prepared for more advanced courses in data science.  

Business Case/IH Value Statement

The role of the industrial hygienist will continue to change. Some of that change will be the need to perform at a high level with limited resources. This course will provide data solutions to IHs so that they can manage more people and processes without diminishing quality. 

Course Level

Introductory

Learning Aids

Laptop

Learning Level(s)

Novice

Topics

Big Data: Data Management & Interpretation
Exposure Assessment Strategies
Sensor Technologies

Transfer of Knowledge

Hands-on demonstrations and practicum