Join us for an exciting, one-day learning event on predictive analytics using Microsoft Azure.
WHO SHOULD ATTEND
Data Scientists, Developers & Programmers, Architects, and Analysts
WHO SHOULD ATTEND
Data Scientists, Developers & Programmers, Architects, and Analysts
WHY SHOULD I ATTEND?
Azure Machine Learning is an exciting new Microsoft product that allows rapidly add the power of Machine Learning to their efforts. If your business deals with data, Azure ML can add value.
LOCATIONS AND DATES
March 25, 2015, 9AM-6PM
AGENDA
The workshop will include information and hands-on lab sessions covering Predictive Analytics scenarios with Big and real-time data and Machine Learning.
Time | Activity |
9:00 AM | Breakfast |
9:30 AM | Introduction |
10:00 AM | Presentations: What is Data Science? What is Predictive Analytics? |
12:00 PM | Lunch and Demo of Azure ML working with Power BI |
2:00 PM | Building a Marketing Mix Model in Azure ML |
3:30 PM | Setting up Excel to Send Data to Azure ML, and Get Predictions Back Via AML’s API |
5:00 PM | Using R in Azure ML |
PRE-REQUISITES
Please bring your laptop to the workshop for participation in the hands-on labs. Please make sure you have a valid Azure Subscription – trial subscription is fine.
COST
There is no fee for attending this event. Participants are responsible for covering their own travel expenses.
Please bring your laptop to the workshop for participation in the hands-on labs. Please make sure you have a valid Azure Subscription – trial subscription is fine.
COST
There is no fee for attending this event. Participants are responsible for covering their own travel expenses.
ABOUT THE INSTRUCTOR:

ABOUT NEAL ANALYTICS:
Neal Analytics is a solution integration firm specializing in Big Data, Data Warehousing, Business Intelligence, and Predictive Analytics services. Our specialty is using Azure Machine Learning and advanced predictive analytics to achieve a positive business outcome in the consumer goods, manufacturing, retail, marketing, and energy industries. Typically, we find that in order to get to prescriptive analytics, firms need to build their way up through data ingress management with big data systems, successful data warehousing, and traditional business