Champagne Coding: Time Series Analytics for Equipment Condition Monitoring


Details
Join Alexandra Gunderson and Mia Ryan on February 23 for a hands-on workshop where we will discuss the lifecycle from sensor to cloud & how time series analytics can be applied to condition monitoring of heat pumps!
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DESCRIPTION
There’s a lot of buzz about predictive maintenance, but what does it mean? How is it done? How can we transform raw sensor data into meaningful insights?
In this Champagne Coding, we will discuss the basics of collecting and storing sensor data, the unique complexities of performing analytics on time series data, and delve into a real world data set where you will get to put your new skills to the test.
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AGENDA
17:00 - 17:30 - Welcome & mingling, with dinner
17:30 - 18:00 - Introduction and presentation
18:00 - 20:00 - Hands-on workshop using real data collected from the field and also Databricks for processing
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PREREQUISITES
A GitHub account and basic knowledge in Python (e.g. know how to write a for loop) and machine learning (e.g. understand what training means) are recommended but not required.
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LOCATION & DIETARY CONSTRAINTS
The event will be held at Parkveien 53B at Unifai's offices. From 5-5:30, we will serve finger food and drinks. From 5:30 - 8, we will run the workshop (with champagne!). If you have a food allergy, please send a message and we will try to accommodate!
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ABOUT THE GROUP
WiDS Oslo is an independent event organised by Alexandra Gunderson, Heidi Dahl, Mia Ryan, and Sheri Shamlou to coincide with the annual Global Women in Data Science (WiDS) Conference held at Stanford University and an estimated 150+ locations worldwide. All genders are invited to attend WiDS regional events, which features outstanding women doing outstanding work.
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SPONSORS
This event would not be possible without our partner, Microsoft (https://www.microsoft.com/nb-no), and we are very grateful for their support of women and diversity in data science!
COVID-19 safety measures

Sponsors
Champagne Coding: Time Series Analytics for Equipment Condition Monitoring