DATA ANALYTICS
& IOT

How to connect machines and sensors to create relevant data, collect and structure data, extract impactful insights for performance analysis and fact-based (predictive) decision making.

Training Programs


Data Analytics Basics 

The participants will get an overview about Analytics, starting from data preparation going all the way to developing dashboards.

Training content:

  • Types of data analytics
  • Overview of descriptive analytics
  • Visualizations and Dashboards
  • Digital Shopfloor Management
  • Data Value Stream

 

Introduction to Data Structures 

The participants will get an overview about using data and its prerequisites.

Training content:

  • Basic steps of data preparation
  • Data Sources and Data Types
  • Good and bad data quality
  • Data Matching
  • Types of data storage
  • Structured and unstructured data
  • Data Semantics

 

Connectivity & Sensors

The participants will get an overview about retrofitting machines and the potential sensors and connectivity provide.

Training Content:

  • Communication technologies
  • Types of Sensors
  • Concept of retrofit and retrofitting a machine
  • IoT Protocols
  • Use Cases with sensors

 

RPA & Process Mining 

The participants will get an overview about Digital support technologies such as Robotic Process Automation (RPA) and Process Mining.

Training content:

  • Robotics Process Automation
  • RPA Use Cases and Implementation
  • RPA Assessment
  • Process Mining
  • Process Mining Use Cases
  • Smart Automation Use Cases


IoT Platforms & Architecture 

The participants will get an overview about IoT platforms and their capabilities in supporting businesses.

Training content:

  • IoT Overview & Applications
  • IoT Ecosystem
  • Industry 4.0 Architecture
  • Edge & Cloud computing
  • Implementation approach & methodologies


Advanced Analytics & AI

The participants will get an overview about Advanced Analytics and will solve a practical real-world example with the help of machine learning.

Training Content:

  • Types and use cases of artificial intelligence (AI)
  • Mathematical and statistical foundations of AI
  • Machine Learning (ML), deep learning & neural networks
  • Types of advanced analytics
  • Autonomous solutions

 

Data & Analytics Expert