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
Analyzing tweets is challenging because of their succinctness (max 280 characters). However, that task is facilitated by the powerful features of SAS Visual Text Analytics (VTA), which includes embedded machine learning algorithms.
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
It’s always worth trying to improve our world and if it can be done thanks to analytics - I can’t imagine a better use case.
In grade school, students learn how to round numbers to the nearest integer. In later years, students learn variations, such as rounding up and rounding down by using the greatest integer function and least integer function, respectively. My sister, who is an engineer, learned a rounding method that rounds half-integers
With all this sitting at a desk writing code, I have to do something to keep in shape. And for me, that something is paddling boats ... as fast as I can - and occasionally trying to race them. This past weekend I entered the race at Hunting Island, SC.
What precisely, then, is needed in risk management? Users want a user-friendly interface, so that they can find what they need.
Are you looking for a Data Science easy button? The dataSciencePilot action set comes pretty close.
Innovation comes in all shapes and sizes. Business value though, lies in translating ideas into production.
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
The public sector is finally changing. Artificial intelligence is gradually finding its way into its administrative structures.
According to the Price Waterhouse Cooper 2018 Global Economic Crime and Fraud Survey, the reported rate of economic crime is on the rise, up to 49% in 2018. That makes the use case I want to share particularly relevant, no matter what industry or sector you're in. This use case
Understanding multivariate statistics requires mastery of high-dimensional geometry and concepts in linear algebra such as matrix factorizations, basis vectors, and linear subspaces. Graphs can help to summarize what a multivariate analysis is telling us about the data. This article looks at four graphs that are often part of a principal
#Amazon forced retailers to find new ways to attract their customers. How can artificial intelligence help #retailers to adress their group?
When I started working in data and analytics 30 years ago, information security wasn’t high on the agenda for organizations. That's changed with the rise of the Internet, and now that cloud is becoming more and more prevalent in organizations, information security is no longer just the domain of specialists
Sabe o que está a causar defeitos nos produtos da sua fábrica? Sabe avaliar os prejuízos provocados por esses defeitos e que factores os influenciam? Sabe quais os parâmetros da máquina que têm maior influência na ocorrência desses mesmos defeitos? Estas são algumas das questões e dúvidas que muitas fábricas
I was recently reading David Mintz's excellent SESUG 2018 conference paper on Five Crazy Good Visualizations and How to Plot Them, and saw a map that caught my eye. David showed how to create a similar map, but with completely different data - I decided to try creating a map
SAS Japanでは昨年末より”Data for Good”を目指す学生コミュニティ「SAS Japan Student Data for Good community」を運営しています。このコミュニティは、Data For Goodを題材にデータサイエンスの一連の流れを体験する場として設立されました。今回紹介する勉強会も、その活動の一環です。詳しくは「Data for Goodを通じて"本物の"データサイエンティストになろう！」の記事をご覧ください。 三回目の勉強会ではヒートアイランド現象をテーマに、課題設定の部分を学びました。 ヒートアイランド対策、”どこ”から？ ヒートアイランド現象とは、都市部の気温が周りに比べて高くなる現象です。その要因には、都市化による土地利用の変化や人間活動で排出される熱などがあります。対策事例として人口排熱削減のために次世代自動車の普及をしたり、保水性舗装の普及や屋上緑化を推進して地表面被服の改善を目指したりというものが行われています。 勉強会で取り上げたヒートアイランド対策事例の一つに、リッチモンドのヒートマッピングがあります。ヒートアイランド現象は都市部と郊外を比較して都市部の方が暑いという考え方が一般的です。しかし、植生域より人口被覆域の方が地表面からの大気加熱を大きくすることや、明るい色の舗装より暗い色の舗装の方が熱を吸収して暑くなることから、都市部の中でも暑さに対する強度は場所によって異なります。そこで、リッチモンドでは「都市の中でも特に暑さの影響を受けやすい場所を見分ける」ことで、対策を優先して行うべき場所の判断をサポートするためのプロジェクトを開始しました。そのアプローチとして、 リッチモンドをブロックで分けた各地点の気温・場所・時間のデータを収集する 観測データ＋土地利用マップ＋住民の収入データ→各地点のヒートアイランドに対する脆弱性レベルを定量化・可視化 に取り組んでいます。このプロジェクトは2017年にリッチモンドで開始し、今では様々な都市に活動の輪を広げています。詳しい内容はこちらの記事（英語）をご覧ください。 解くべき課題を設定する これらの知識を踏まえて、次は「課題設定」を行いました。自分たちでヒートアイランド現象という問題に対して、解くべき課題は何か・解決するために誰のどのような意思決定が必要か・どのようなデータが必要か、についてディスカッションをしました。 議論を進めていく中で、さまざまな意見が飛び交いました。その中には、テーマとして設定していたヒートアイランド現象を解決するというよりも、ヒートアイランド現象が”障壁”となって起きるであろう「熱中症を未然に防ぐ」というものを課題に設定するという意見がありました。その解決策として、リッチモンドの事例を応用した「ある人がいる地点の体感気温＋その人の体温のデータをリアルタイムで収集し、熱中症のおそれがある場合に通知するアプリケーションの作成」などの案が出てきました。 ディスカッションをすることで、自分では思いつかない新鮮な発想に触れることができたり、テーマに広がりを持たせることが出来たりすることを感じました。アナリティクスの結果を活用するアクションを考えるための「課題設定」を実際に体験できたディスカッションになりました。 コミュニティメンバー募集中！ SAS Japan Student Data for Good communityでは引き続き学生の参加者を募集しております。社会貢献を目指す活動を通してデータサイエンティストの役割である「課題の設定」から「データを用いた解決法の提示」までの一連の流れを経験できます。 興味をお持ちでしたら下記の事項をご記入の上JPNStudentD4G@sas.comまでご連絡ください。 大学名 / 高校名 名前 メールアドレス
I have a clear view on the potential of AI: the true value of AI lies in helping banks to know and understand their customers.
It’s imperative that banks embrace technology - driven changes, break down barriers to change and disrupt themselves digitally.
Every year at Halloween, I post an article that shows a SAS trick that is a real treat. This article shows how to use the INTNX function to find dates that are related to a specified date. The INTNX function is a sweet treat, indeed. I previously wrote an article
Was haben SAS Viya und die österreichischen Berge gemeinsam? Eine ganze Menge! Neulich besuchten mich Freunde aus Tirol am Neusiedler See, also im Burgenland. Bekanntermaßen ist Tirol ein Bundesland im Hochgebirge. Entsprechend amüsiert waren meine Freunde, als ich ihnen den Rosenberg und den Ungerberg in der Nachbarschaft zeigte. „Bei uns
I suffer from arthritis. You can tell just by watching me walk: Depending on the day, I have a slight limp, which varies in severity based on a number of factors such as the time of day and recent physical activity. Years of treatment for my condition have shown me
If you're looking for advice on developing an analytics strategy, there's no shortage of resources, including this from SAS: Building your data and analytics strategy. If, on the other hand, you're looking for advice on how to apply analytics to strategic planning, your search has likely to come up wanting.
How to address insurers' challenges by providing AI and machine learning to streamline a customer centric pricing process.
Edge computing allows businesses to handle thousands of data points and interactions rapidly and immediately, in real time.
A common task in SAS programming is to specify a list of variables that satisfy some pattern. You can specify lists for the KEEP= or DROP= data set options, and you can use lists of variables on many SAS statements such as the VAR and MODEL statements. Although SAS has
With NHANES data from the CDC, we can use SAS to research the impact of endocrine-disrupting chemicals. Special sampling techniques are needed when using these data.
