SAS Viya Copilot for Code Assistance in SAS Data and AI Studio
Recent Library Articles
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.
SAS Forward: Real Stories, Live Demos and AI Innovation
Ask the Expert
Join us November 4-5 for SAS Forward, a virtual event exploring modern analytics, AI and intelligent decisioning. Discover real-world success stories, explore emerging trends, and see the latest SAS innovations in action through expert-led sessions and live demonstrations.
As segment maps grow, the logic can sometimes become difficult to interpret at a glance. The person who built it understands the design, but another user might need extra context before using or updating it.
Text and image annotations help turn that technical canvas into a map that communicates its purpose. They do not change who qualifies for a segment or how the segment map runs. Instead, they add information that helps people understand the design, follow the intended flow, and collaborate more confidently.
... View more
anyone can tell why summ on rett0_difs is off from rett0_dif6?!
rett0_difs seems just three * rett0_dif6
data temp;
do tick=1 to 1000;
do i=1 to 1000;
rett0=ranuni(i);
output;
end;
end;
run; quit;
data temp;
set temp;
by tick;
if dif6(rett0)>0 then rett0_dif6=1 ; else if dif6(rett0)=0 then rett0_dif6=0; else rett0_dif6=-1;
if dif12(rett0)>0 then rett0_dif12=1 ; else if dif12(rett0)=0 then rett0_dif12=0; else rett0_dif12=-1;
if dif24(rett0)>0 then rett0_dif24=1 ; else if dif24(rett0)=0 then rett0_dif24=0; else rett0_dif24=-1;
run; quit;
proc sql;
create table temp_ts as
select tick,
avg(rett0_dif6) as rett0_dif6,
std(rett0_dif6) as rett0_dif6_stv,
avg(rett0_difs) as rett0_difs,
std(rett0_difs) as rett0_difs_stv
from temp
group by 1 order by 1;
quit;
... View more
Thinking about joining the Curiosity Cup 2027? Take this as your sign 😊. Explore the finalist projects from 2026 and discover which of the three categories is yours.
Every year, the Curiosity Cup, a global student competition, shows what happens when students combine curiosity, analytics, and the guidance of their educators.
The 2026 edition brought something new: three competition categories that align more closely with real industry challenges: Business & Economy, Health, Science & Engineering, and Culture, Lifestyle & Entertainment. And students embraced them fully.
Most teams chose topics in Health & Life Sciences, the same like last year, followed closely by Business & Economy.
Whether they were forecasting electricity prices, detecting bots, modeling biodiversity, predicting nurse fatigue, analyzing financial markets, or studying consumer behavior, one thing became clear: Analytics is relevant everywhere.
From 138 teams across 20 countries, only 13 teams earned a place among the global finalists.
Their projects are proof of what students can achieve when they work with data and they are the perfect inspiration for anyone considering joining the next Curiosity Cup.
Below is the full list of finalists, winners, and runner‑ups, including their category and a link to their complete project. If you’re thinking about participating this year, explore these papers. They show exactly what’s possible.
Team Statistix & Chill - Winner: What does social media really do to student well-being and academic performance? (Culture, Lifestyle & Entertainment)
Team UCCU - Winner: Can we predict market turbulence? (Business & Economy)
Team STATYX - Winner: Forecasting urban biodiversity (Health, Science & Engineering)
Team ColdChain Predictors - Runner up: Using AI and Analytics to improve refrigerator price prediction (Culture, Lifestyle & Entertainment)
Team Nasi Lemak Tambah Nasi - Runner up: Predicting nurse fatigue (Health, Science & Engineering)
Team Machine Still Learning - Runner Up: Predicting customer churn with machine learning (Business & Economy)
Team Benchmark Team - Finalist: From auction demand to benchmark credibility (Business & Economy)
Team BotBusters - Finalist: Human or Bot? Can we really tell the difference? (Culture, Lifestyle & Entertainment)
Team ORSOL Forecasting - Finalist: Forecasting electricity prices to support the future of EV charging (Health, Science & Engineering)
Team Resonance Analytics - Finalist: Can financial literacy protect consumers from impulse buying? (Business & Economy)
Team Strategy Stormers - Finalist: Using data science to improve rural infrastructure in India (Health, Science & Engineering)
Team The Data Fusionist - Finalist: Can data help us predict which communities need better healthcare? (Health, Science & Engineering)
Team The Tea Test - Finalist: Using data science to strengthen global healthcare resilience (Health, Science & Engineering)
Learning That Lasts Beyond the Competition
As Jürgen Kaselowsky, Global Academic Programs Director at SAS, perfectly summarized: “Even if you are not a winner, all participants will learn so much! Working in a team, meeting a deadline, keeping formalities, and receiving feedback from real-life data scientists.”
All participants receive digital badges, and winners also earn great prizes.
If you’re a student passionate about data, analytics, and real-world impact, the Curiosity Cup 2027 might be your chance to shine.
if you are an educator with questions about how to make the Curiosity Cup work for you and your students, we've got answers.
Registration opens December 1, 2026 and closes January 31, 2027.
Join the interest list here to stay in the loop.
... View more
Many ways to sk... I'm gonna just say many ways to accomplish a task. Here's an illustration and guideline on the best source data ingestion strategy to choose for SAS Retrieval Agent Manager.
... View more
Team Name NSW PredictView Track IoT, Industry, Manufacturing Use Case Our team has experience building IoT systems that collect local environmental data, such as temperature and humidity, directly from sensors.We use this data to forecast influenza trends in Tokyo one to four weeks ahead. We analyze the relationship between weekly influenza indicators and environmental factors such as temperature and humidity to visualize early outbreak signals. Instead of directly forecasting vaccine demand, we combine the predicted influenza trend with current vaccine stockpile and supply status to detect periods of rising shortage risk at an early stage. Technology SAS Analytics for IoT Region AP Team lead Kenji Ono Team members We have two members Social media handles - Is your team interested in participating in an interview? N Optional: Expand on your technology expertise -
... View more