DataHour: Build and Operationalize your Machine Learning Model using Tableau Business Science

Online 25-03-2022 08:30 PM to 25-03-2022 09:30 PM
  • 905


  • Knowledge and Learning


DataHour Recording

About the Webinar:

Looking at the overall process of applying machine learning techniques to build an end-to-end solution for any business problems, there is huge distance between the data to the business users who are ultimate decision makers. Obviously when the immediacy is the most important factor this distance is problematic and will increase the time to value significantly.

Tableau Business Science lowers the barrier and reduces time to value by providing a guidance machine learning experience. It enables business users to develop and deploy their model with no-code and just click and embed this model into their analytic workflow seamlessly allowing for faster speed to insight and more confident decisions.

This webinar will focus on the AutoML solution and how it can enable business users to tune their data and business knowledge into actionable analytics. Amir will use Einstein Discovery integration with Tableau to demonstrate how business users can build a predictive model and bring it into Tableau and build different reports and get more insight. He will be using a real world business problem and the dataset would be available to the audience.

Prerequisites: Enthusiasm for learning Data Science and a trial version of Einstein Discovery as well as Tableau, which you can download for free from below:

1- Einstein Discovery Trial Request

2- Tableau Desktop 2021.1 or beyond

Note: Trials are valid for 14 days only so it is best if you download a few days before the webinar.

Who is this Webinar for?

  • Students & Freshers who want to build a career in Data Science
  • Working professionals who want to transition to a data science career
  • Data science professionals who want to accelerate their career growth


Amir Meimand

Data Science and Machine Learning Solution Engineer, Salesforce

Amir Meimand is a Principal Solution Engineering on the Salesforce strategic solution team focusing on Data Science and Machine Learning. Amir has 10+ years experience in building, deploying, and applying advanced analytics to solve enterprise business problems. 

Previously, he was the director of Data Science at Zilliant, a SaaS company providing machine learning solutions for price optimization and sales maximization, later acquired by Madison Dearborn. Amir’s current area of focus is scaling advanced analytics solutions by democratizing data science and machine learning. Amir holds a Ph.D. in Statistics and Operations Research from Pennsylvania State University, 2013.


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