The List Table can be more than just a black-and-white ledger style visual. There are many other enhancements such as alternating row colors, abbreviated values, display rules, etc.
Search Results: Visual Analytics (1744)
Note from Udo Sglavo: In our peace of mind blog series, we documented areas of analytics that are either evolving or not necessarily in the standard toolset of data scientists. We looked at causal modeling, network analytics, and econometrics, to name a few. With this blog post, we would like
Q: SAS 是不是都需要寫程式? 不會寫程式怎麼辦? A: NO! SAS Enterprise Guide(EG) 與 SAS Enterprise Miner(EM),無須寫程式快速進行資料整理、資料分析與資料探勘。 有許多老師同學喜歡使用SAS EG 進行教學研究: 1. 如果有地方要修改不用一直按上一步,想改哪裡就改哪裡 2. 可以一次把所有圖表直接輸出,不用一個個複製貼上 3. 每一步分析都流程化的呈現,方便修改與瞭解整個分析思路 Q: 可以在哪裡取得SAS呢? A: 若貴校為SAS全校授權學校,可以直接至資訊處/電算中心取得軟體。 若欲採購或是不確定學校是否為授權學校,敬請寫信至 i-jie.tsai@sas.com Q: SAS 要如何安裝呢? A: 請參考SAS 安裝支援頁面 【事前準備】 –請務必依文件 (1.1~1.4) 先確認電腦環境 –確認電腦為32位元或64位元 (選擇不同安裝檔案) –電腦名稱與使用者登入名稱務必為英文 【安裝SAS】 –若學校提供光碟/5-6個安裝檔案/iso檔→請從文件2.1 建立SAS Software Depot 開始 –若學校提供1個安裝檔案→請從文件2.3
From mental health to biodiversity, SAS is committed to using data for social innovation, committing our resources, analytics expertise and software to tackle global issues. It's a vital undertaking, and we can't do it alone -- strategic partnerships are key to these efforts. Here's a quick overview of our top
Yazarlar: Kağan Şen & Tunay Güneş Teknolojinin gün geçtikçe ilerlemesi ile birlikte, uyduların kullanımı ileri teknoloji gerektiren alanlardan daha günlük alanlara doğru ilerlemeye başlamıştır. İlk başlarda haberleşme ve astronomi uyduları bu alanda daha çok kullanılırken, günümüzde meteoroloji uyduları, keşif (casus) uydular, seyir (navigasyon) uyduları, gözlem uyduları oldukça yaygın kullanılmaya başlanmıştır.
지난 글에서는 기존 데이터 분석의 한계와 현업 사용자가 데이터 분석을 해야 하는 이유, 그리고 시티즌 데이터 사이언티스트가 되기 위한 조건을 알아봤습니다. 그렇다면 프로그래밍 기술이나 전문적인 분석 기술에는 능숙하지 않은 현업 사용자가 어떻게 데이터를 분석할 수 있을까요? 전문가 영역이었던 데이터 분석이 일반 현업 사용자로 확대되는 여러 움직임은 오래전부터 있었습니다. 그 가운데
지난 텍스트 분석 시리즈 2편에서는 보험사의 데이터를 이용하여 예측 모델을 개발하고, 모델의 성능을 개선하여 고객 행동에 대한 예측도를 높이는 방법을 살펴봤습니다. 이번에는 영화 리뷰 데이터를 사용하여 분류 규칙을 개발하는 과정을 SAS Visual Text Analytics를 중심으로 알아보겠습니다. SAS Visual Text Analytics(이하, VTA)는 대용량의 비정형 데이터로부터 쉽게 인사이트를 추출할 수 있도록 설계된
In recent years, we have seen some astronomical contracts given to professional athletes. Major League Baseball (MLB) has certainly had its share. One of its first notable “megadeals” was when Alex Rodriguez, the Seattle Mariners’ power-hitting shortstop, left the team to join the Texas Rangers in 2001. The Rangers committed
01. はじめに 最近多くの人々がクラウド環境をベースにしたデータストレージサービスを利用しています。 ここで皆さん、突然ですが、データを管理するためにローカル(またはオンプレミス)環境を構築していた過去を振り返ってみてください。 以前は、データを保存するために、関連ソフトウェアやハードウェアを購入・設置・インストールし、様々な環境設定を行います。3か月後、データの量が増えてきてデータベースの容量が足りなくなります。そしてまた多くの費用と時間を使って、必要なソフトウェア・ハードウェアを再び購入、同じく様々な環境設定をします。 上記に記載したような様子は現在のビジネス世界ではほとんど見当たりません。今日必要なのは、ただメールアドレスとクレジットカードのみです。最近では様々なデータストレージサービスが生まれてきたからです。このようなサービスはクラウド環境で動いていて、一定期間料金を支払えば利用できる「subscription」(サブスクリプション)ベースであり、前払い方式ではなく、使用した分だけ課金される「pay as you go」(ペイアズユーゴー)方式が特徴です。SASでも様々なデータストレージサービスに対応していますが、今日はその情報について詳しくお伝えします。 02. SAS/ACCESSのご紹介 「SAS/ ACCESS」とは、SASと他のベンダーのデータストレージサービスを連携するインターフェースです。下記のような特徴があり、様々なデータストレージサービスとの連携を支援しています。 シームレスで透過的なデータアクセス (Seamless, transparent data access) 柔軟なクエリ言語のサポート (Flexible query language support) パフォーマンスチューニングオプション (Performance tuning options) 性能最適化機能 (Optimization features for better performance) より詳しい情報はこちらをご参照ください。 様々なデータストレージベンダーの中で、今回は「SAS/ACCESS INTERFACE TO SNOWFLAKE」を使って「Snowflake」というサービスに連携してみたいと思います。* Snowflakeの設定はこちらを見て事前に行いました。 3. SAS/ACCESSデモ 3-1. LIBNAME statementで連携 SASのLIBNAME statementで簡単にSnowflakeとの連携を行うことができます。連携することでSnowflakeのデータをDATA StepやSASプロシージャで参照することが可能になります。LIBNAME Statementのサンプルコードは下記のボックスをご参考ください。 LIBNAME
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should
When news about a new Coronavirus outbreak in China first hit the news, Falko and his colleague Anand Chitale wanted to know more. “We knew we could use SAS to analyze the data and discover new insights,” he said. By now, you’ve heard about our work helping customers combat the coronavirus pandemic.
비정형 텍스트 데이터는 인류가 생성하는 가장 큰 데이터입니다. 더 나은 비즈니스 결정을 내리고, 제품 전략을 알리고, 고객 경험 개선에 도움이 되는 유용한 정보가 바로 이 데이터에 포함되어 있습니다. 비정형 텍스트 데이터의 잠재력을 최대한 활용해야 하는 이유입니다. 본 시리즈에서는 텍스트 데이터에서 인사이트를 얻는 주요 방법과 이를 위한 SAS 솔루션을 살펴봅니다. 전
Machine learning and visualisation help - 250 students enrolled in the program across all years. Now the program started its third year ...
If you're a SAS Enterprise Guide user who is looking to move to SAS Studio, there is a lot to like about your new coding environment.
Where do your data scientists sit? Perhaps they occupy a typically gloomy, computer-filled basement. Or maybe they have a glassy building all to themselves. Either way, you’ll not always see business decision makers walking the same corridors. After all, analytics is best left to the experts, isn’t it? Yet back
Public and private schools are struggling to figure out how to bring face-to-face instruction to students during this pandemic. Health risks to students and teachers, parents struggling with child-care options and/or support for virtual learning, and schools’ capacities and budget limitations make this problem a severe logistical challenge. Schools need
Every presidential candidate has a list of states they’re expected to win, but there are always states that are too close to call because they have similar numbers of registered voters for each of the two dominant political parties: Democrat and Republican. It’s in these “swing” states that candidates invest
Everyone knows that SAS has been helping programmers and coders build complex machine learning models and solve complex business problems for many years, but did you know that you can also now build machines learning models without a single line of code using SAS Viya? SAS has been helping programmers
This is the third installment of a series focused on discussing the secure integration of custom applications into your SAS Viya platform. I began the series with an example of how a recent customer achieved value-add on top of their existing investments by building a custom app for their employees
[Nabaruna Karmakar was coauthor of this post] A study was conducted at the University of Denver on The Economic Impacts of the Austin, Texas "No Kill" Resolution. The study found great value in creating an animal welfare-focused community. It highlighted the benefits of economic growth due to an increased need in
Remember back to your early school days, singing with all your classmates “If you’re happy and you know it clap your hands!” and then we’d all clap our hands. Being happy back then was so simple. Today, it’s hard to get away from all the negative headlines of 2020! It’s
During the 2020 Coronavirus pandemic, you've probably formed a great appreciation for good, informative graphics. Good graphics can help you get a handle on thousands of individual data values, see the geographical distribution, or look for trends. In February, I wrote a blog post about creating a coronavirus dashboard with
The current state of policy enforcement during an infectious disease pandemic is mostly reactive. Public health officials track changes in active cases, identify hot-spots and enforce containment policies primarily based on geographic proximity. By combining telecommunications data -- which we turn into mobility information -- with public health data of
질병 확산을 억제하고 경제적 영향을 최소화하기 위해서는 인구가 어떻게 이동하는지 분석하고, 지역 내 접촉자를 추적하여 적절한 의사결정을 해야 합니다. 이번 SGF 시리즈에서는 인구 이동 분석과 접촉자 추적 등 SAS 분석 기술이 어떻게 팬데믹 극복을 위한 의사결정을 지원하는지에 대한 SAS 짐굿나잇 회장과 스티브 베넷 글로벌 정부기관 프랙티스 부문 이사의 세션을 소개합니다.
Teknik mimari çeşitli bir konudur- ancak bazı sorular tekrar tekrar gündeme gelir. Altyapıyı, açık kaynak politikasını, IoT veri yönetimini, paydaş katılımını kapsayan geniş yelpazenin tamamı, kurumsal mimarların oynadığı rolün giderek daha önemli bir hale geldiğine işaret ediyor. Bu yazımda son 12 ay boyunca en sık sorulan on soru ve müşterilerime
인류가 전례 없는 전투를 치르고 있는 가운데, 우리나라는 코로나19 모범 대응 국가로 큰 주목을 받고 있습니다. 여기서 한발 더 나아가 그 어느 국가보다 앞서 포스트 코로나19 를 준비하고 있습니다. 하지만 코로나19는 끊임없이 존재감을 드러내며 정부와 지자체, 공공기관의 의사결정자들을 당혹스럽게 하고 있습니다. 이 위기를 효과적으로 헤쳐나가고 또 다른 위기를 막기 위해서는
The COVID-19 pandemic challenged agriculture and supply chains, but the overarching resilience of agriculture around the world speaks to the industry's efficiency, built-in redundancy and indispensability. In the US, flourishing interactions between government, industry and academic stakeholders underscore how ag represents unity and consilience. And there may be no better
[Jessica Curtis and Adam Hillman, both Forecasting Advisors at SAS, were co-authors of this post] The world has been dramatically impacted by the recent COVID-19 pandemic. Many of us are juggling a completely new lifestyle that was forced upon us overnight. As consumers find their way to a new normal,
この記事はSAS Institute Japanが翻訳および編集したもので、もともとはMichael Gillilandによって執筆されました。元記事はこちらです(英語)。 カオス状況下での予測/フォーキャスティング Institute of Business Forecasting(IBF)は、「世界的パンデミックというカオス状況下での予測と計画」に関して80分間のバーチャル・タウンホールを開催しました。現在、それを録画したオンデマンド・ビデオが公開されており、一見の価値が大いにあります。そこには、以下のような経験豊富な識者陣による堅実かつ実践的なガイダンスが満載です。 エリック・ウィルソン(Eric Wilson)氏: IBFのソートリーダーシップ担当ディレクター(司会者) ダスティン・ディール(Dustin Deal)氏: 北米ビジネス・オペレーショズ担当ディレクター、Lenovo社 パトリック・バウアー(Patrick Bower)氏: グローバル・サプライチェーン・プランニング&カスタマー・サービス担当シニア・ディレクター、Combe社 アンドリュー・シュナイダー(Andrew Schneider)氏: サプライチェーン担当グローバル需要マネージャー、Medtronic社 ジョン・ヘルリーゲル(John Hellriegel)氏: IBFのシニアアドバイザーおよびファシリテーター 以下に、私が各パネリストから得た重要な知見をまとめます。 ジョン・ヘルリーゲル氏: 今現在、マクロ予測は相当困難であり、ミクロ予測(製品レベルに至るまで)は更に困難である。 平時状況を超えるレベルで多数の介入要因(例:政府による刺激策、原油価格の下落など)が存在しており、それら全てが不確実性と複雑性を増大させている。 高い予測精度が期待できないことから、需要計画担当者は企業における「不確実性の理解」と「適切な意思決定の実現」を支援することにフォーカスするべきである。 最も役立つのは、明確な前提条件に基づくシンプルなモデルである可能性が高い(例えば、個々の品目を調整しようと多大な労力を費やすのではなく、「3ヶ月間、各カテゴリーで25%の削減を実施する」など)。 ジャスティン・ディール氏: 中国では生産が回復しつつあるが、物流の遅延は依然として存在する。 マクロ/ミクロの両レベルでデータを収集するべき。これには、チャネルの在庫とセルスルー(実販売数)も含まれる。 チャネル在庫が低水準な場所や、即座の補充が必要な場所を把握するべき。 プランニング(例:S&OP)をもっと頻繁に実行するべき。 アンドリュー・シュナイダー氏: 今現在は、典型的な需要計画を行うのではなく、代わりに、「需要衛生サービス」(データ・クレンジング、仕入数/実売数の比較・把握など)にフォーカスするべき。 物事が平時状況に回復するまでの間は、需要の統御(コントロール)および形成(シェイピング)にフォーカスするべき。 変動係数を活用して、どの製品がCOVID-19(新型コロナウイルス感染症)の大規模な感染拡大のインパクトを最も受けるのかを特定するべき。そして、そのインパクトに従って製品をセグメント化し、リスクベースのABC分析を考慮する。 「データの観察・収集という “受動的” な取り組み」と「欠品状況から “入手可能な代替製品” への需要推進という “能動的” な取り組み」とを区別するべき。 需要シグナルの品質を評価するべき。POS(販売時点情報管理)システムを導入済みであれば申し分ないが、未導入の場合でも、顧客の真のニーズの解明に努めるべき(注文数/注文減少数/注文残数などの状況を踏まえた上で)。 組織内のデータだけでなく、外部の追加的なデータソースの活用も試みるべき。そこから何が分かるか? 需要の確率分布を考慮するべき。ただし、過剰な取り組みは禁物。「平時状況に回復した後、組織がトラブルに直面するような事態」を招いてはならない。 今現在は、精度についてはそれほど心配する必要はない。代わりに、様々なアプローチの予測付加価値(FVA)を検討するべき。
At the end of March, the German government sponsored a hackathon called #WirVsVirus. The aim was to bring Germany’s collective coding expertise to bear on some of the many problems surrounding COVID-19. In total, more than 27,000 coders joined the challenge, working from home, and programming for 48 hours from