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Industrial Data Analytics: Models, Forecasts, and AI Tools

This advanced data analytics course empowers engineers and technical professionals to transform complex data into actionable insights. Participants will gain hands-on experience with Python, applying techniques such as classification, clustering, frequent pattern mining, anomaly detection, failure event modeling, deep learning and forecasting to predict system performance and optimize processes. Emphasizing real-world industrial applications, the course bridges analytics with practical outcomes in reliability, quality, and data-driven decision-making, enabling participants to deliver measurable impact in their organizations.

Learning Outcomes:

At the completion of this course, participants will be able to:
  • Apply core and advanced analytics methods in Python, including data manipulation, visualization, classification, clustering, and pattern mining to extract insights from data and support decisionmaking.
  • 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.
  • This course will use Python. Students are not required to know Python in advance, though prior familiarity is preferred. However, some programming experience is required.
Date
Format
ID
Fee
 
 Jun 16-19, 2026
Live Online
ID: E107
Fee: $1,695View Discounts