Feature Row: h2 Call-to-Action Here

Row 1: h2 Heading in a Full Width Content Section (1-Column)


Attend the Workshop

Seats Are Limited - Sign Up Now

Big Data for Retailers Workshop 


Details: 

When:  Tuesday, January 30th, 2018    |     8:30 AM - 2:00 PM CST
Where:  Microsoft Technology Center: 7000 North State HWY 161, Irving, TX 75039
 

About this Event:  

Big data is changing the way consumer facing businesses operate, helping them with everything from inventory optimization, improving the customer experience, and even helping to detect fraud. Most data driven companies are leveraging best practices of cloud and automated data platforms to transform their business quickly and cost effectively. Join us on Tuesday, January 30th to gain hands-on experience leveraging a Big Data platform on Azure to tackle a real-world retail challenge using big data. During this workshop, we will be diving deep into one particular big data opportunity: how to determine the usefulness of customer product reviews.

Workshop Challenge

Companies, particularly Retailers, have rich product reviews, but often fail to take full advantage of the internal and customer-facing insights that can be derived from them. Many retailers are finding it challenging to create an automated way to rate the helpfulness of a review and uncover valuable insights buried amongst thousands of reviews. During this workshop, attendees will get hands-on experience leveraging Qubole’s big data platform on Azure and a machine learning model utilizing Natural Language Processing (NLP) to identify the most useful customer reviews in real-time. Customer reviews can then be surfaced more quickly to other consumers. Retailers can also leverage similar techniques to derive additional business value from customer-produced reviews, such as:
  • Fake review fraud detection
  • Identifying positive product characteristics
  • Identify influencers
  • Uncover new feature attributes for a product to inform merchandising

Key Takeaways: 

Learn how leveraging a cloud-based architecture and services can increase the velocity of deploying advanced analytics for engaging customers:
  • Experience the full analytics lifecycle
  • Ingesting a data set
  • Preparing and transforming a data set for modeling
  • Training a machine learning model on a dataset
  • Gaining insight from the data
  • Leverage the model for real-time value

Agenda

8:30 - 9:00 AM Arrive and Breakfast Provided
9:00 - 10:30 AM Workshop Begins: Intros and Workshop Overview
10:30 - 12:00 PM Getting Data Set Up, Training Model, Getting Results
12:00 PM Lunch Provided
12:15 - 2:00 PM Write reviews - run real-time models, pick winner of best review, recap,
code and put material on GitHub for future reference






 

 



Row 4: 2-Column Break Out Sections

Thumbnail

The 'headline' class removes top margin on heading tags

This is a nested "row" inside of the left column. It's 5/6 width (col-sm-9) and can be adjusted the same as the larger columns (instructions above). You can turn this entire row off by toggling the "Left Column: Nested Row 2" variable to "Hide".

Thumbnail

The 'headline' class removes top margin on heading tags

This is a nested "row" inside of the left column. It's 5/6 width (col-sm-9) and can be adjusted the same as the larger columns (instructions above). You can turn this entire row off by toggling the "Left Column: Nested Row 2" variable to "Hide".

The 'headline' class removes top margin on heading tags

Add a message above your form here

This is an optional area for full-width content. Alternately, to move the "Brands That Rely on Qubole" section down, you can copy all the HTML in that section and paste it here.