Our company talks to utilities all over the world about the value of analytics and "the digital utility," and we share analytics use cases across: assets and operations; customers; portfolio; and corporate operations (see diagram below). In this third post of my four-part series, I'll highlight a customer analytics use case. Customer
Did you know that SAS has two on-site solar farms? At a combined 2.3 MW in capacity, SAS’ solar farms are located on 12 acres at world headquarters in Cary, NC. The photovoltaic (PV) solar arrays generate 3.8 million kilowatt-hours of clean, renewable energy each year, reducing carbon dioxide emissions
SAS enables you to evaluate a regression model at any location within the range of the data. However, sometimes you might be interested in how the predicted response is increasing or decreasing at specified locations. You can use finite differences to compute the slope (first derivative) of a regression model.
Atualmente, os dados são um dos ativos mais importantes das organizações. As organizações reúnem diferentes tipos de dados que são posteriormente processados e analisados para uma melhor compreensão da evolução das necessidades dos seus clientes. Os termos Business Analytics e Business Intelligence fazem parte da solução que ajuda as organizações a tomarem decisões baseadas
전 세계의 이목이 러시아에 향해 있습니다. 바로 2018년 국제 축구 대회 때문인데요. 오늘은 바로 이 국제 축구 대회에 대한 다양한 데이터를 시각화하고, 지난 대회 결과로부터 어떤 인사이트를 얻을 수 있는지 살펴보고자 합니다! 전 세계 축구팀들은 대회를 위해 얼마나 멀리 이동할까요? 데이터 시각화 리포트가 보이지 않으시면 클릭하세요. 위 보고서는 참가국들이 조별 리그 동안 얼마나 멀리
Künstliche Intelligenz wird meist mit Alexa und Siri oder Chatbots bei Banken und Callcentern assoziiert. Eher selten wird über das Potenzial der KI-Technologien in den Life Sciences und im Gesundheitswesen gesprochen – die Idee ist aber im Kommen. Das zeigt eine aktuelle Studie von Accenture: 74 Prozent der Führungskräfte in
前回は、SASの「Pipefitter」の基本的な使用方法を紹介しました。続く今回は、基本内容を踏まえ、ひとつの応用例を紹介します。 SAS Viyaのディープラーニング手法の一つであるCNNを「特徴抽出器」として、決定木、勾配ブースティングなどを「分類器」として使用することで、データ数が多くないと精度が出ないCNNの欠点を、データ数が少なくても精度が出る「従来の機械学習手法」で補強するという方法が、画像解析の分野でも応用されています。 以下は、SAS Viyaに搭載のディープラーニング（CNN）で、ImageNetのデータを学習させ、そのモデルに以下の複数のイルカとキリンの画像をテストデータとして当てはめたモデルのpooling層で出力した特徴空間に決定木をかけている例です。 In [17]: te_img.show(8,4) 以下はCNNの構造の定義です。 Build a simple CNN model In [18]: from dlpy import Model, Sequential from dlpy.layers import * from dlpy.applications import * In [19]: model1 = Sequential(sess, model_table='Simple_CNN') Input Layer In [20]: model1.add(InputLayer(3, 224, 224, offsets=tr_img.channel_means)) NOTE: Input
O mundo interconectado. Um mundo inteligente. Onde as empresas/marcas têm acesso a dados que permitem oferecer, aos clientes, soluções/produtos personalizados. Bem-vindos ao mundo (utópico?) da Internet das Coisas. Mas será que esta visão é viável? Como é que as empresas conseguirão atingir este cenário? Falámos com um dos especialistas na
Die Digitalisierung bringt enorme Potenziale für Unternehmen: individualisierte Kundenansprache, spezifische bedürfnisorientierte Angebote, bessere Steuerbarkeit der Kundeninteraktion – all dies dient letztlich der Umsatzsteigerung und der Befriedigung der Shareholder. Doch es sind nicht nur Unternehmen, die die Herausforderung der Digitalisierung von Prozessen und Produkten annehmen und ihren Profit daraus schlagen, sondern
In FiveThirtyEight's Significant Digits section, they recently mentioned that nine states could potentially have 50% or more females in their legislature after the upcoming election. My first thought - I need to see some graphs! They linked to a New York Times article that had some interesting graphs. There was
At SAS, we love data. Data is central to our corporate vision: to transform a world of data into a world of intelligence. We're also famous for enjoying M&Ms, but to us they are more than a sweet snack. They're also another source of data. My colleague Pete Privitera, with
In the second part of this series, Phil Simon argues that organizations can immediately take steps to handle likely legislation.
Looking to connect and engage with other like-minded people in a meaningful way? A #SASchat might just what you’re looking for! #SASChat is SAS’ version of a Twitter chat: a live, online event that works like any get-together. Participating in a #SASchat is a great way to: Engage with an
Which president of the United States is ranked the greatest by presidential historians? This article visualizes the results of the 2018 Presidential Greatness Survey, which was created and administered by B. Rottinghaus and J. Vaughn. They analyzed 166 responses from experts in political science who ranked the 44 US presidents
SASでは、Python向けパッケージ/ライブラリとして、機械学習のパイプラインの実装を簡素化する「Pipefitter」を提供しています。 SASの「Pipefitter」パッケージは、SAS Viyaまたは、SAS v9の反復可能なワークフローの一つの段階として、データ変換とモデルフィッティング向けパイプラインを開発するためのPython APIを提供します。 このパッケージを使用すると、SASでデータを操作して、次のような処理を実装できます： ・欠損値補完 ・デシジョンツリー、ニューラルネットワーク、およびその他の機械学習テクニックを使用したパラメータ推定値の適合 ・ハイパーパラメータチューニングを使用したモデル選択の高速化 ・スコアリングとモデル評価 「Pipefitter」のもう一つの重要な特徴は、SASが提供する他の2つのPythonパッケージの能力に基づいていることです。 SWAT: SAS Viyaプラットフォームのインメモリー分析エンジンであるSAS Cloud Analytic Services（CAS）を活用し様々なデータ操作や分析を可能にするPython向けパッケージ SASPy： SAS9.4の機能を活用し、分析、データ操作、および視覚化を行うためのPython向けパッケージ ロジスティック回帰でのパラメータ推定などのパイプライン処理は、SASPyを介してSAS 9で、SWATを介してCASで同じように実行されるように設計されています。 以下は、タイタニック号の乗船者データに基づくパイプラインの例です。 まず、KaggleのサイトからPandas DataFrameにデータをダウンロードします。 In [1]: import pandas as pd In [2]: train = pd.read_csv('http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv') In [3]: train.head() Out[3]: PassengerId Survived Pclass 0 1 0 3 1 2 1
The Geo Map Visualization has several built-in geographical units, including country and region names and codes, US state names and codes, and US zip codes. You can also define your own geographic units. This paper describes how to identify any geographic point of interest, or collection of points, on a map to create custom maps in SAS.
Bloomberg Government reported in the 2017 fiscal year that federal agencies could commit more than $1 billion on countermeasures for insider threats. Whether that number turned out to be accurate or not, it highlights the priority that the federal government is placing on protecting its information and data from being
As a resident of Northern California, I was interested in learning more about the causes of wildfires. My area has recently experienced large fires that caused many residents to evacuate their homes and some who have even lost their lives. Last October there were more than 170 fires that burned
SASの提供する機能、製品、コーディング上の利用の仕方、便利なSASマクロ等々は非常に幅広く、日々お客様と接している我々であっても全ての情報を持つことはできません。そのため、我々はWWのSAS利用ナレッジをこのSAS Global Forumにおける発表論文に頼ることが頻繁にあり、社内のナレッジシェアリングで参照することもしばしばです。f 今年4月に開催されたSAS Global Forum 2018の発表資料／論文を検索 2017以前の発表資料／論文を検索 なんと1976年からあります！ 是非、ご活用いただければと思います。
During these hot summers in the southern US, many people regard Willis Carrier (the man who invented modern air conditioning) as a saint. But along with the comfortable indoor temperatures comes the high electricity bill at the end of the month. And how much does the electricity cost? That's a difficult
We hear a lot about how various industries are using data visualization and analytics. But what about the education industry? The institutional research office (IR) at universities is the center for data, reports and analytics and provides decision makers with information about the university. The IR teams are working on
사물인터넷(IoT)이 보험사의 운영 방식은 물론 보험 가입자 개개인의 삶의 방식을 변화시키면서 보험 업계에 새로운 세계가 열렸습니다. 기술이 더욱 더 스마트해지고 연결되면서 예방 가능한 비즈니스 모델로 빠르게 전환되고 있는데요. 사물인터넷 시대에 적응하지 못하는 보험사는 새로운 진입자 그리고 새로운 생태계 및 시장 수요에 성공적으로 대응한 기업들에 밀려 토이저러스(Toys R Us)와 같은 신세를 피하기 힘들
Our company talks to utilities all over the world about the value of analytics. We help utility executives understand what the "digital utility company" looks like and share use cases to illustrate how these companies are using analytics across: assets and operations; customers; portfolio, and corporate operations (see diagram below).
This article describes how to obtain an initial guess for nonlinear regression models, especially nonlinear mixed models. The technique is to first fit a simpler fixed-effects model by replacing the random effects with their expected values. The parameter estimates for the fixed-effects model are often good initial guesses for the
I have lived in the Town of Cary for more than twenty years; two of my three children were born at the local WakeMed Cary Hospital. I’m a big fan of my city, or town as it prefers to be called – even though the population is over 160,000. That’s
One of my favorite Eddie Murphy movies when I was growing up was Coming to America. And speaking of coming to America, there's been a lot in the news recently about people doing just that. So I thought I'd use my SAS skills to help people wanting to come to
It seems only a few months back I posted an article on creating Pie Charts using a GTL based macro. Well, looking back, that was almost 6 years ago!! Recently, a colleague here at SAS needed to create Pie Charts in his report along with other plots created using SGPLOT
Joyce Norris-Montanari talks about how to implement data privacy policies, procedures and guidelines.
Retailers don’t need to be reminded about the importance of customer experience. It’s critical – and that’s been true since the dawn of retail. But the nature of that experience has been changing at a rapid clip in recent years, due in large part to well-documented shifts in technology and
When you fit nonlinear fixed-effect or mixed models, it is difficult to guess the model parameters that fit the data. Yet, most nonlinear regression procedures (such as PROC NLIN and PROC NLMIXED in SAS) require that you provide a good guess! If your guess is not good, the fitting algorithm,
