Learn How to Leverage Python modules for Advanced Analytics, How to Codify ML Algorithms for Descriptive Analytics, and How to deploy Python/Jupyter Notebook with Analytics Solutions

Implementing Manufacturing Data Analytics solution need a layered and guided approach to realize ‘Return on Analytics’ (ROA) before investing big dollars. This analytics e-meet is focused how to approach advanced analytics by using Python/Jupyter notebook, methods and best practices to work on process (time-series) data and advanced visualization. The session will showcase prevalent prerequisites and use cases such as statistical inference on process variability, correlation /deterministic analysis, significance testing and model building, etc. The data science / analyst team would get the insights into Advanced Data Analytics templatization.

Meet our experts and get insights into:

• Approach: Process Data Mapping and Representation – Prerequisites
• Methods: How to templatize Root Cause Analysis, Predictive Analytics, Performance Analysis, Model Building and Fingerprint of a Process, Visual Analytics
• Solutions: Advanced Visualization, Deployment of Python model, Integration with Analytic Solutions

Who should attend:

• Process R&D Heads
• Production / Operations Head
• Maintenance Heads
• Plant heads
• Technology leaders
• Data Science leaders
• Data Analysts

Register to view the recorded session of this e-Meet or  email us at analytics@tridiagonal.com and we will email you the link.

Register Now