Find out how analytics, from data mining to cognitive computing, is changing the way we do business
If you have an interesting topic for a 20 minutes lightning talk, please feel free to contact us.
Find out how analytics, from data mining to cognitive computing, is changing the way we do business
If you have an interesting topic for a 20 minutes lightning talk, please feel free to contact us.
Another year, another traditional Christmas song or carol turned into a fun technology-related version! This is the sixth year and my ninth song. I hope you enjoy your 2019 holiday song, based on this famous tune. The Data Science and AI Song Computer vision processing on an open stack The
This is a second article about analyzing longitudinal data, which features measurements that are repeatedly taken on subjects at several points in time. The previous article discusses a response-profile analysis, which uses an ANOVA method to determine differences between the means of an experimental group and a placebo group. The
SAS Viya is a cloud-enabled, in-memory analytics engine which allows for rapid analytics insights. Viya utilizes the SAS Cloud Analytics Services (CAS) to perform various actions and tasks. Best of all, CAS is accessible from various interfaces including R. In this blog, I will go through a few blocks one of my notebooks, which moves through an analytics workflow using R and SAS.
A Transformação Digital deixou para trás muitas lacunas processuais e documentais, e trouxe novos desafios à comunidade científica e ao mundo dos Dados de organizações de Tecnologia e de quem dela faz uso para governação de Informação. Neste artigo o paradigma sai do campo do hardware, do storage e do
Longitudinal data are used in many health-related studies in which individuals are measured at multiple points in time to monitor changes in a response variable, such as weight, cholesterol, or blood pressure. There are many excellent articles and books that describe the advantages of a mixed model for analyzing longitudinal
In just over six months, football fans across Europe face a logistical maze: how to follow their favourite teams from stadium to stadium as games are played all over the continent. We described the challenge and optimisation approach we took in a separate piece. In this gallery, we walk
Football fans around the world have something exciting to look forward to, with the European Championship scheduled to take place in June and July 2020. Twenty teams out of 24 have already qualified for the tournament, and after last Saturday's draw, the teams and fans are now getting ready to
With time series data analysis, we can apply moving average methods to predict data points without seasonality. This includes Simple Average (SA), Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Moving Average (EMA), etc. For series with a trend but without seasonality, we can use linear, non-linear and autoregressive
This article discusses how to restrict a multivariate function to a linear subspace. This is a useful technique in many situations, including visualizing an objective function that is constrained by linear equalities. For example, the graph to the right is from a previous article about how to evaluate quadratic polynomials.
A business glossary improves data quality – one of the top five ways it makes analytics better.
Los datos, y sobre todo su significado y usabilidad, se han ido transformando con el tiempo. Anteriormente hablar de datos era pensar en unos y ceros, en tablas estáticas o incluso en materiales que no se aprovechaban. Hoy, pensar en datos es pensar en la fuente principal de las historias,
What is an efficient way to evaluate a multivariate quadratic polynomial in p variables? The answer is to use matrix computations! A multivariate quadratic polynomial can be written as the sum of a purely quadratic term (degree 2), a purely linear term (degree 1), and a constant term (degree 0).
“Ocean acidification is sometimes referred to as global warming's equally evil twin.” ~ Elizabeth Kolbert This is the second post in my two-part series about climate change. You can read part 1 of this series here. When engaging in data exploration for insights, it’s good practice to start with a
The dsAutoMl action is all that and a bag of chips! In this blog, we took over all aspects of the data science workflow using just one action.
Colleges and universities have access to enormous stores of data and analytics has the power to help higher education tackle some of its biggest challenges. Larry Burns, Assistant Director of Institutional Research and Information Management (IRIM), Oklahoma State University (OSU) knows a great deal about the power of analytics to
In a linear regression model, the predicted values are on the same scale as the response variable. You can plot the observed and predicted responses to visualize how well the model agrees with the data, However, for generalized linear models, there is a potential source of confusion. Recall that a
앞으로 10년 뒤, 2030년에는 어떤 브랜드가 살아남아 성장을 지속할 수 있을까요? SAS와 글로벌 시장조사기관 퓨처럼 리서치(Futurum Research)는 SAS 애널리틱스 익스피리언스 2019에서 ‘2030년 고객 경험의 미래(Experience 2030: The Future of Customer Experience)’ 설문조사 보고서를 발표했습니다. 다니엘 뉴먼(Daniel Newman) 퓨처럼 리서치 수석분석가 겸 창립 파트너는 더 많은 권한을 갖게 된(empowered) 소비자가 새롭게
지난 10월 21일부터 23일까지 이탈리아 밀라노에서 열린 'SAS 애널리틱스 익스피리언스 2019(SAS Analytics Experience 2019)'에서는 SAS의 머신러닝, 컴퓨터 비전, 자연어처리 등 AI 기술을 기반으로 기업들이 어떻게 실제(real) 가치를 실현할 수 있는지 보여주는 다양한 사례들이 소개되었습니다. 특히 행사 둘째 날에는 짐 굿나잇 SAS CEO, 올리버 샤벤버거 SAS 수석부회장 겸 최고운영책임자(COO) & 최고기술책임자(CTO)의
*Post basado en la presentación de Isabel Cristina Zuluaga de Tuya en SAS Forum Colombia Cada año en Colombia, cerca de 1,5 millones de personas inician su vida crediticia y obtienen su primer puntaje de riesgo (según cifras de la Superintendencia Financiera de Colombia). Los recién llegados al sistema buscan
지난 10월 21일부터 23일까지 이탈리아 혁신 기술의 중심지 밀라노에서는 유럽 최대 규모의 분석 컨퍼런스 ‘SAS 애널리틱스 익스피리언스 2019(SAS Analytics Experience 2019)’가 개최됐습니다. 3일간 밀라노 컨벤션 센터(Mico Milano Convention Centre)에서 진행된 올해 컨퍼런스에는 1,800명이 넘는 데이터 사이언티스트와 비즈니스 리더들이 모여 다양한 논의가 진행되었으며, 참석자들에게는 56개의 breakout 세션, 48개의 데모 부스, 그
Getting value from analytics is becoming top of mind for businesses. Organizations have invested millions of dollars in data, people and technology and are looking for a return on their investment. That requires operationalizing analytics so that it can be used for strategic decision making -- often referred to as
Ladies and gentlemen, I give you Value-Based Payments (VBP), health care’s new magic, “silver bullet” that will solve all our fraud problems. Last month, the US Department of Health and Human Services (HHS) issued a press release entitled, “HHS Proposes Stark Law and Anti-Kickback Statute Reforms to Support Value-Based and
“The future is already here — it's just not very evenly distributed.” ~ William Gibson, author The same can be said for climate change – global warming is here, in a big way, but its effects are still an arm's length away for many of us. How is climate change
Biplots are two-dimensional plots that help to visualize relationships in high dimensional data. A previous article discusses how to interpret biplots for continuous variables. The biplot projects observations and variables onto the span of the first two principal components. The observations are plotted as markers; the variables are plotted as
A major UK insurance company used text analytics to categorise complaints.
US military veterans are mission-focused, team-oriented and natural leaders that benefit any organization that hires them. Many of today's veterans organizations use data and analytics to help transition military members and their spouses find rewarding civilian careers. SAS supports those efforts and we're also proud to offer many programs to
I recently saw in several social media posts that sales of vinyl records are forecast to be higher than sales of CDs this year (2019) for the first time since 1986. Two questions came to mind - 1) Is this true? and 2) Is this a big deal? Let's analyze
Are you looking for a Data Science easy button? The dataSciencePilot action set comes pretty close.
Principal component analysis (PCA) is an important tool for understanding relationships in continuous multivariate data. When the first two principal components (PCs) explain a significant portion of the variance in the data, you can visualize the data by projecting the observations onto the span of the first two PCs. In