Data Analytics and Machine Learning in Production in Suzhou
苏州市 生产领域中的数据分析和机器学习

In 2023

min. 6 participants of each of this special course. We suggest one company to apply for one training.

Target Group

This training was developed for engineers who want to develop their data understanding and drive digitalization.

Objectives and Content

The ubiquitous use of sensors and connected devices leads to a tremendous increase in collected data. Data analytics in production is the use of big data analytics and machine learning methods to ensure quality, to increase performance, and to reduce cost. This training combines theoretical knowledge and hands-on practices to show the potential of data analytics in production. Participating in this training helps you to understand and drive integration of appropriate methods on your way to a smart factory.

Why this training is very important for your career and for your company?

  • Learn how to increase productivity by choosing the right tasks for lean production
  • learn how to increase the profit and growth on your shop floor using lean methods and tools
  • Get an overview of benefits from Data Analytics and Machine Learning in Production


  • Introduction of Data Analytics & Machine Learning
    • General Introduction
    • Standard CRISP-DM process model CRISP-DM
    • Examples for analysis & implementation
  • Machine Learning Algorithms
    • Supervised learning (e.g., Artificial Neural Networks)
    • Unsupervised learning (e.g., Support Vector Machines)
    • Reinforcement learning (e.g., Q-Learning)
  • Data analysis platform & tools
    • Knime
    • Python
    • TensorFlow and Keras
  • Introduction of Industry implementation examples
  • Guided practice on chosen use cases
Data Analytics and Machine Learning in Production in Suzhou






  • 数据分析 / 机器学习介绍
    • 基本概念介绍
    • 数据挖掘标准流程
    • 案例分析和演练
  • 机器学习算
    • 监督学习(例如人工神经网络)
    • 无监督学习(例如支持向量机)
    • 强化学习(例如 Q-学习)
  • 数据分析平台&工具应用
    • 案例
    • 案例
    • 案例
  • 工业应用案例介绍
  • 针对所选案例的指导性实践


If interested in, please send an email or call us for asking details and for quick enrollment process.