Industrial Data Analytics: Models, Forecasts, and AI Tools

Unlock the power of data to drive smarter decisions in engineering and industry. This hands-on course helps engineers and technical professionals turn raw data into meaningful results. Using R, participants learn data preparation, visualization, and advanced analytics techniques, including classification, clustering, and pattern discovery. The course then transitions to Python for predictive modeling, anomaly detection, deep learning, and time series forecasting. Emphasizing real-world manufacturing, reliability, and quality challenges, participants build practical skills and confidence to improve performance, efficiency, and innovation.

Learning Outcomes:

At the completion of this course, participants will be able to:

  • Apply core and advanced analytics methods in R, including data manipulation, visualization, classification, clustering, and pattern mining to extract insights from data and support decision-making.
  • Develop and implement predictive models in Python for failure event modeling, anomaly detection, deep learning, and time series forecasting to address engineering and industrial challenges.
  • Integrate data-driven and AI-based approaches, such as statistical process control, supervised and unsupervised learning, and deep learning to enhance reliability, quality control, and operational efficiency in real-world applications

Who Should Attend:

  • Manufacturing, process, and quality engineers aiming to apply analytics to improve performance and reliability.
  • Operations and continuous improvement leaders seeking to integrate predictive and diagnostic models into decision-making.
  • Data analysts and technical managers interested in expanding from basic analytics to applied machine learning and forecasting using R and Python.
Date
Format
ID
Fee
 
 Jun 16-19, 2026
Live Online
ID: E107
Fee: $1,695View Discounts