SAS Viya Copilot for Code Assistance in SAS Data and AI Studio
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In this post, we’ll take a look at how SAS Viya Copilot for Code Assistance can help with some of those everyday tasks: understanding code, improving it, and making changes more confidently—all without leaving the SAS Data and AI Studio environment.
Date: 11 th November 2026
Time: 12.30-15:00 CET
Type: Virtual (only for SAS AML & Fraud customers, partners or prospects) Locations: Microsoft Teams,
Register: Denmark / Finland / Norway / Sweden / UK&I
The join link will be sent to you before the meeting.
Agenda
Will be updated …
Welcome
By Frans Holm, SAS
Sceduling of AML jobs in Airflow
By Jesper Ågård, Bankdata
Investigation templates
By Daniel Lund, SAS
Break
Alert & Scenario migration
By Henrik Thuesen, Bankdata
News in AML
By Leevi Söderling, SAS
Closing
By Frans Holm & Daniel Lund, SAS
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Team Name Critical Thinking Crew Track Energy & Telecom Use Case Analyze why unused smartphones remain dormant in households across markets, identify where and why their circulation is interrupted, and evaluate which approaches—continued use, resale/reuse, collection, recycling, or other forms of utilization—may be appropriate in different contexts. Technology SAS Viya for data integration, analytics, and visualization Region Tokyo, Japan (AP Region) Team lead ITOCHU Techno-Solutions Corporation: Takeshi Nakada Team members ITOCHU Techno-Solutions Corporation: Michito Taga Keisuke Fukunaga Social media handles - Is your team interested in participating in an interview? N Optional: Expand on your technology expertise Our team plans to use SAS Viya to integrate multiple public datasets, explore cross-market differences, identify key factors and bottlenecks, and visualize analytical findings. We may also explore AI-assisted approaches to explain analytical results and support the development of market-specific action options.
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For people working with both SAS and Python, how do you usually divide tasks between the two? For example, would you use SAS for statistical analysis and data preparation and Python for visualization or machine learning, or keep most of the workflow within SAS? For someone learning data science, which combination of SAS, SQL, Python and statistics would you recommend focusing on first?
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