![Small group of coworkers, happy to be data-driven to new insights](https://blogs.sas.com/content/datamanagement/files/2019/10/464721725.jpg)
Data-driven businesses use technology as an insight platform to empower nontechnical users.
Data-driven businesses use technology as an insight platform to empower nontechnical users.
“Analytics Can Save Higher Education. Really.” is a call to action for the higher education community to leverage data and analytics for better decision making at colleges and universities. It stresses the importance of using data and analytics to improve student outcomes, campus operations and much more. Oklahoma State University
Risk and finance teams know more about each other’s worlds than at any time since risk emerged from finance.
Eliud Kipchoge recently ran a marathon in under 2 hours. It was a special marathon where they had set up the best possible conditions to help him achieve this goal (such as swapping in pace-setting runners to block the wind for him), so it won't count as the world record
Are you looking for a Data Science easy button? The Data Science Pilot Action Set comes pretty close.
Quantitative risk and finance modeling is no different. Data scientists use a mix of old and new technologies and algorithms.
In response to a recent article about how to compute the cosine similarity of observations, a reader asked whether it is practical (or even possible) to perform these types of computations on data sets that have many thousands of observations. The problem is that the cosine similarity matrix is an
Which measures financial services can take to keep their customers complaints at a minimum.
Analytics-based approaches in healthcare offer a new way of thinking about fraud. They are able to predict potential events.
Computing rates and proportions is a common task in data analysis. When you are computing several proportions, it is helpful to visualize how the rates vary among subgroups of the population. Examples of proportions that depend on subgroups include: Mortality rates for various types of cancers Incarceration rates by race
The “last mile” is well known in telecommunications and supply chains. Now we face it in the analytical world as well. Read why!
SASChat - Innovation@scale on 14 November. A vivid twitter discussion on how innovation projects become revenue generating services.
You've probably heard of the stupid 'challenges' where people usually end up hurting themselves (cinnamon challenge, Carolina reaper pepper challenge, etc). I thought it might be more helpful to society to have a challenge that could actually help people, rather than hurting them! Therefore I came up with the Halloween
SAS study (i.p. w. Harvard Business Review, Accenture, Intel) to learn more about how companies are enhancing their business.
Two pieces of advice stood out for me in Tom Siebel's book: re-educate your leadership team and pick your partners carefully.
The EFFECT statement is supported by more than a dozen SAS/STAT regression procedures. Among other things, it enables you to generate spline effects that you can use to fit nonlinear relationships in data. Recently there was a discussion on the SAS Support Communities about how to interpret the parameter estimates
なぜ“sasctl”が必要なのか? オープンソースとの統合性はSAS Viyaの一つの重要な製品理念であり、そのための機能拡張を継続的に行っています。その一環として”sasctl”という新しいパッケージがリリースされました。SAS Viyaでは従来から、PythonからViyaの機能を使用するために”SWAT”パッケージを提供しており、SAS Viyaのインメモリー分析エンジン(CAS)をPythonからシームレスに活用し、データ準備やモデリングをハイパフォーマンスで実行することができるようになっていました。しかし、データ準備やモデル開発は、アナリティクス・ライフサイクル(AI&アナリティクスの実用化に不可欠なプロセス)の一部のパートにすぎません。そこで、開発されたモデルをリポジトリに登録・管理して、最終的に業務に実装するためのPython向けパッケージとして”sasctl”が生まれたのです。 sasctlの概要 sasctlで提供される機能は、大まかに、3つのカテゴリーに分けられます。 また、この3つのカテゴリーは、お互いに依存する関係を持っています。 1.セッション sasctlを使用する前に、まずSAS Viyaのサーバーに接続する必要があります。(この接続は、ViyaマイクロサービスのRESTエンドポイントに対して行われることに注意してください) SAS Viyaのサーバーへの接続は、セッションのオブジェクトを生成することにより行われます。 >>> from sasctl import Session >>> sess = Session(host, username, password) この時点で、sasctlはViya環境を呼び出して認証し、この後のすべての要求に自動的に使用される認証トークンを受け取りました。 ここからは、このセッションを使用してViyaと通信します。 2.タスク タスクは一般的に使用される機能を意味し、可能な限りユーザーフレンドリーになるように設計されています。各タスクは、機能を実現するために、内部的にViya REST APIを複数回呼び出しています。例えば、register_modelタスクではREST APIを呼び出し、下記の処理を実行しています: リポジトリの検索 プロジェクトの検索 プロジェクトの作成 モデルの作成 モデルのインポート ファイルのアップロード その目的としては、ユーザーがPythonを使って、アナリティクス・ライフサイクルで求められるタスクを実行する際に、sasctlの単一のタスクを実行するだけで済むようにすることです。 >>> from sasctl.tasks import register_model >>> register_model(model, 'My Model', project='My Project') 今後も継続的に新しいタスクを追加していきますが、現在のsasctlには下の2つのタスクを含まれています:
The connection between SAS Viya and the Austrian mountains Friends from Tyrol recently visited me in Burgenland at Lake Neusiedl. Tyrol is an area famous for really high mountains. Tourists from all over the world come to Tyrol to experience the fantastic views and hikes. So my friends laughed when
Validating and testing our supervised machine learning models is essential to ensuring that they generalize well. SAS Viya makes it easy to train, validate, and test our machine learning models.
Vivemos um tempo onde, quer nos sectores produtivos da nossa economia, quer em toda a área de prestação de serviços, se tem vindo a perspectivar a introdução de novas tecnologias de automatização de tarefas. Os avanços científicos e a maturidade de conhecimento que foram sendo alcançados num conjunto vasto de
I recently wrote about how to use PROC TTEST in SAS/STAT software to compute the geometric mean and related statistics. This prompted a SAS programmer to ask a related question. Suppose you have dozens (or hundreds) of variables and you want to compute the geometric mean of each. What is
In a recent video blog, I discuss forecast accuracy as a parameter for measuring the ability to forecast and plan demand. I further argue for the use of causal data as a key input to understanding historical demand and forecasting/planning future demand. Forecast accuracy is often claimed NOT to be
The SAS Championship golf tournament is happening this week, here in Cary, North Carolina! If you're following along and watching the scores, you might wonder how they're doing compared to past years, and what kind of scores it generally takes to win. Follow along as I plot the data from
Proper data prep means faster, better analytics. Guest blogger Jenine Milum shares tips.
What can data tell us about the easiest hole at your favorite golf course? Or which hole contributes the most to mastering the course? A golf instructor once told me golf is not my sport, and that my swing is hopeless, but that didn’t stop me from analyzing golf data!
Estimations suggests that 65% of children entering primary school now will be doing jobs that do not yet exist.
In a previous article, I mentioned that the VLINE statement in PROC SGPLOT is an easy way to graph the mean response at a set of discrete time points. I mentioned that you can choose three options for the length of the "error bars": the standard deviation of the data,
Thanks to recent advances in Artificial Intelligence (AI) and deep learning, image recognition has become a reality.
In Greece we conduct a SAS Hackathon. This will focus on wildfires - come and join!
It is always great to read an old paper or blog post and think, "This task is so much easier in SAS 9.4!" I had that thought recently when I stumbled on a 2007 paper by Wei Cheng titled "Graphical Representation of Mean Measurement over Time." A substantial portion of