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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:

  • Apply core and advanced analytics in R, including data manipulation, visualization, classification, clustering, and pattern mining to support decision-making.
  • Build predictive models in Python for failure event modeling, anomaly detection, deep learning, and time series forecasting for engineering applications.
  • Use data-driven and AI-based methods to improve reliability, quality control, and operational efficiency in real-world industrial settings.

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