Analytics e-Meet

How to Scale your Data Analytics Strategies

Approach. Methods. Solutions

Learn the evaluation criteria metrics, levels of analytics, data science methods & solutions, scale-up strategy for successful implementation of ‘Data Science & Analytics’ initiative.

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 on strategy, approach, methods, and solutions for manufacturing data analytics. The senior management professional would get the insights into Data Analytics evaluation metrics and methodical approach to implement Data Science and Analytics solution within their organization. The session will showcase levels of analytics, prevalent data analytics prerequisites and methods, advantages & limitations, and scale-up strategy for implementation.

Meet our experts and get insights into:

  • Layer 1: How to create Quality Data for Analytics
  • Layer 2: How to Select Right Analytics & Validation Methods
  • Layer 3: How to Deploy Analytics Solution / Strategy
  • Case studies – Levels of Analytics
  • Introduction to Process Data Analytics Solution
  • Scale-up Methodology

Who should attend:

  • CIO’s
  • Chief Digital Officers (CDO’s)
  • IT Heads
  • Production / Operations Head
  • Process Heads
  • Maintenance Heads
  • Plant heads
  • Technology leaders
  • Data Science leaders

Register Now

Date: 20/10/2020
Time: 11:00 am IST

Templatizing Advanced ML-based Process Data Analytics

11/11/20203:00pm IST1 hour
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.

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