Aster Community Digest Issue No.5: Teradata Goes Big on Analytics of Things (AoT)

Document created by Dolletta Mitchell Administrator on Nov 11, 2016Last modified by Dolletta Mitchell Administrator on Nov 11, 2016
Version 3Show Document
  • View in full screen mode
Teradata
 Aster Community DigestIssue 5 – October 2015 
  
  
  
  
  
  
  
 
Teradata Aster Machine Learning
Tired of struggling with your data? What if your machine could learn how to handle data on its own without being told exactly what to do? It’s called machine learning. This lively 3-min video explains the difference between supervised and unsupervised machine learning. Watch video 
 
  
 Whether you’re doing Social Network Analysis (SNA) or other analytics work, graph analytics can provide solid insights and help you make decisions about a complex problem. Graph measures can also be applied to graphs from social network data such as twitter feeds, emails, Facebook connection data etc., to find key nodes of interest. Read blog

 
  
 For 25 years, operational models have been built on traditional data warehouses – a lot of them attributed to embedded rules in the code. Rules are extremely easy to understand and were developed in the past by domain experts. There comes a point where it’s difficult to measure how well your rules work or how many exceptions you have. Read blog

 
  
 Review an example of an analysis process using the new Teradata AsterR package. Gain insights into what Teradata AsterR is and how it works. Plus link to an excellent AsterR description. Read blog
 
 
  
  
  
  
  
  
 
 

Attachments

    Outcomes