Advanced Analytics | Analytics

SAS KoreaSeptember 4, 2017 0

무료 플레이 게임 ‘월드 오브 탱크(World of Tanks)’ 분석으로 수십억 달러를 창출하다!

매일 전 세계 수백만 플레이어들이 가상의 전투에서 서로의 탱크를 무찌르기 위해 ‘월드 오브 탱크(World of Tanks)’에 접속합니다. 국내에서도 큰 인기를 끌고 있는 월드 오브 탱크는 벨라루스 게임 개발사인 워게이밍(Wargaming)의 온라인 MMO(대규모 다중 사용자) 슈팅 게임인데요. 등록된 온라인 사용자만 무려 1억1,000만명이 넘습니다. 워게이밍은 목표는 모든 레벨의 플레이어에게 멋진 경험을 제공하는 것으로, 부분 유료(free-to-play)

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Korean

Sports & Entertainment

Advanced Analytics

Rick WicklinAugust 30, 2017 0

The singular value decomposition and low-rank approximations

A previous article discussed the mathematical properties of the singular value decomposition (SVD) and showed how to use the SVD subroutine in SAS/IML software. This article uses the SVD to construct a low-rank approximation to an image. Applications include image compression and denoising an image. Construct a grayscale image The

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Advanced Analytics | Customer Intelligence | Machine Learning

Maarten OostenAugust 30, 2017 0

Bots, collusion and accountability in pricing

In the recent article, “Price-bots can collude against consumers,” the Economist discusses the consumer effects of prices set by price-bots. The article starts with an example of gasoline pricing strategies on Martha’s Vineyard. With a small number of gas stations on the island, the price-bots can cover all competitor prices frequently

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Energy & Utilities | Hospitality | Manufacturing | Retail

Advanced Analytics

Rick WicklinAugust 28, 2017 0

The singular value decomposition: A fundamental technique in multivariate data analysis

The singular value decomposition (SVD) could be called the "billion-dollar algorithm" since it provides the mathematical basis for many modern algorithms in data science, including text mining, recommender systems (think Netflix and Amazon), image processing, and classification problems. Although the SVD was mathematically discovered in the late 1800s, computers have

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Advanced Analytics | Machine Learning

Wayne ThompsonAugust 25, 2017 0

Machine learning best practices: Manage the temporal effect

This is the sixth post in my series of machine learning best practices. If you've come across the series for the first time, you can go back to the beginning or read the whole series. Aristotle was likely one of the first data scientists who studied empiricism by learning through

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English

Advanced Analytics | Analytics

Thomas Bodenmüller-DodekAugust 23, 2017 0

SAS Visual Analytics: Hallo Emilia – Goodbye Daniel

Die Geburtenrate in Deutschland befindet sich derzeit auf dem höchsten Niveau seit 33 Jahren. Eine erfreuliche Entwicklung, und zugleich stellt es Eltern vor die schwere Entscheidung, welchen Namen der Nachwuchs tragen soll. Zahlreiche Webseiten und Bücher bieten Hitlisten und Namensbeschreibungen an, um die Auswahl zu erleichtern. Oder sollte man das

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German

Advanced Analytics | Machine Learning

SAS KoreaAugust 22, 2017 0

최적의 ‘머신러닝 알고리즘’을 고르기 위한 치트키

“어떤 알고리즘을 사용해야 할까요?” 수많은 종류의 머신러닝 알고리즘을 맞닥뜨린 초급자 분들이 가장 많이 물어보는 전형적인 질문인데요. 사실 이 질문에 대한 답변은 하단 내용을 비롯한 수많은 요인에 따라 달라집니다. 데이터의 크기, 품질, 특성 가용 연산(계산) 시간 작업의 긴급성 데이터를 이용해 하고 싶은 것 그렇기에 숙련된 데이터 과학자(Data scientist)조차도 여러 알고리즘을 직접

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Korean

Advanced Analytics | Data Management

Mary Kathryn QueenAugust 16, 2017 0

Load Data into SAS Viya via REST API

In this blog post I am going to cover the example of importing data into SAS Viya using Cloud Analytic Services (CAS) actions via REST API. For example, you may want to import data into a CASLib via REST API.  This means you can perform an import of data outside

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English

Advanced Analytics | Analytics | Data Management | Machine Learning

Charlie ChaseAugust 16, 2017 0

At the end of the day, it’s all about analytics-driven forecasting

Analytics-driven forecasting means more than measuring trend and seasonality. It includes all categories of methods (e.g. exponential smoothing, dynamic regression, ARIMA, ARIMA(X), unobserved component models, and more), including artificial intelligence, but not necessarily deep learning algorithms. That said, deep learning algorithms like neural networks can also be used for demand forecasting,

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English

Manufacturing | Retail

Advanced Analytics | Data Visualization | Programming Tips

Robert AllisonAugust 10, 2017 0

So, 27 US states are farther north than Canada, eh?

With all the recent talk about some people wanting to move from the US to Canada, I got to wondering how cold, and how far north Canada is. And after a few Google searches, I was surprised to learn that 27 US states are actually farther north than the southernmost point

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English

Government | Travel

Advanced Analytics | Learn SAS

Kattamuri SarmaAugust 10, 2017 0

3 steps to prepare your data for accurate predictive models in SAS Enterprise Miner

In this post I describe the important tasks of data preparation, exploration and binning.These three steps enable you to know your data well and build accurate predictive models. First you need to clean your data. Cleaning includes eliminating variables which have uneven spread across the target variable. I give an

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English

Advanced Analytics | Machine Learning

Wayne ThompsonAugust 10, 2017 0

Machine learning best practices: Autotune models to avoid local minimum breakdowns

This is the fifth post in my series of machine learning best practices.  Hyperparameters are the algorithm options one "turns and tunes" when building a learning model.  Hyperparameters cannot be learned using that algorithm. So, these parameters need to be assigned before training of the model.  A lot of manual

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English

Advanced Analytics | Machine Learning

SAS KoreaAugust 9, 2017 0

데이터 과학자가 뽑은 "머신러닝 알고리즘 개발 베스트 프랙티스 1탄"

1980년대 후반에만 해도 머신러닝(machine learning)이나 데이터 과학자와 같은 개념은 없었습니다. 대신 통계, 분석, 데이터 마이닝, 데이터 모델링과 같은 단어가 사용됐는데요. 이후 글로벌 기업들은 30년 이상 머신러닝 모델을 연구해 왔으며, 페이스북의 이미지 인식 소프트웨어, 아마존의 음성 비서 알렉사, KT의 인공지능 서비스 기가 지니(GiGA Genie)까지 그 결과들이 연이어 쏟아지고 있죠! 이러한 결실

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Korean

Advanced Analytics

Rick WicklinAugust 9, 2017 0

Robust principal component analysis in SAS

Recently, I was asked whether SAS can perform a principal component analysis (PCA) that is robust to the presence of outliers in the data. A PCA requires a data matrix, an estimate for the center of the data, and an estimate for the variance/covariance of the variables. Classically, these estimates

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English

Advanced Analytics

Fiona McNeillAugust 4, 2017 0

Try it - you'll like it...

It's a common mantra many parents use to encourage their children to expand their food choices and try something new. Even as adults we’re often more comfortable easing into the unfamiliar, taking small bites, tiny samples, even dipping a toe in the water before diving in headfirst. Software is no

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English

Advanced Analytics | Machine Learning

Wayne ThompsonAugust 3, 2017 0

Machine learning best practices: Put your models to work

This is the fourth post in my series of 10 machine learning best practices. It’s common to build models on historical training data and then apply the model to new data to make decisions. This process is called model deployment or scoring. I often hear data scientists say, “It took

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English

Advanced Analytics | Machine Learning

Wayne ThompsonJuly 25, 2017 0

Machine learning best practices: combining lots of models

This is the third post in my series of machine learning techniques and best practices. If you missed the earlier posts, read the first one now, or review the whole machine learning best practices series.   Data scientists commonly use machine learning algorithms, such as gradient boosting and decision forests, that automatically build

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English

Advanced Analytics | Data Visualization | Learn SAS

Robert AllisonJuly 25, 2017 0

23 MSAs account for 1/2 of US GDP!

When it comes to economic activity, a handful of the largest metropolitan areas in the US account for lion's share. In 2013, the top 23 Metropolitan Statistical Areas (MSAs) accounted for 50% of the total US Gross Domestic Product (GDP). I recently came across a map created by Alexandr Trubetskoy

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English

Government

Advanced Analytics | Analytics | Risk Management

Hartmut KömmeJuly 20, 2017 0

Neue Prozess-Infrastruktur für die Renaissance des Datasteps

Wenn es darum geht, mit Daten zu arbeiten, dann ist der klassische SAS Datastep eines der wirksamsten Werkzeuge, das uns zur Verfügung steht. Es ist sehr einfach, mithilfe von Datenauswahl, Formeln und Bedingungen im Datastep sowie in spezifischen SAS Prozeduren zu Ergebnissen zu kommen. Hieraus sind in der Praxis umfangreiche

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German

Banking | Insurance

Advanced Analytics | Machine Learning

Wayne ThompsonJuly 19, 2017 0

Machine learning best practices: detecting rare events

This is the second post in my series of machine learning best practices. If you missed it, read the first post, Machine learning best practices: the basics. As we go along, all ten tips will be archived at this machine learning best practices page. Machine learning commonly requires the use of

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English

Advanced Analytics | Analytics

Larry OrimoloyeJuly 17, 2017 0

What are the characteristics of “true clusters?”

In the digital world where billions of customers are making trillions of visits on a multi-channel marketing environment, big data has drawn researchers’ attention all over the world. Customers leave behind a huge trail of data volumes in digital channels. It is becoming an extremely difficult task finding the right

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English

Advanced Analytics | Data Visualization

Robert AllisonJuly 17, 2017 0

Tracking STEM degrees - a deeper look!

My previous blog post focused on a graph, showing the % of women earning STEM degrees in various fields. While that graph was was designed to answer a very specific question, let's now look at the data from a broader perspective. Let's look at the total number of STEM degrees

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English

Education

Advanced Analytics | Data Visualization

Rick WicklinJuly 17, 2017 0

3 ways to visualize prediction regions for classification problems

An important problem in machine learning is the "classification problem." In this supervised learning problem, you build a statistical model that predicts a set of categorical outcomes (responses) based on a set of input features (explanatory variables). You do this by training the model on data for which the outcomes

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English

Advanced Analytics | Machine Learning

Wayne ThompsonJuly 12, 2017 0

Machine learning best practices: the basics

I started my training in machine learning at the University of Tennessee in the late 1980s. Of course, we didn’t call it machine learning then, and we didn’t call ourselves data scientists yet either. We used terms like statistics, analytics, data mining and data modeling. Regardless of what you call

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English

Advanced Analytics | Programming Tips

Rick WicklinJuly 12, 2017 0

The bias-corrected and accelerated (BCa) bootstrap interval

I recently showed how to compute a bootstrap percentile confidence interval in SAS. The percentile interval is a simple "first-order" interval that is formed from quantiles of the bootstrap distribution. However, it has two limitations. First, it does not use the estimate for the original data; it is based only

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English

Advanced Analytics

Hui LiJuly 6, 2017 0

How to use regularization to prevent model overfitting

When building models, data scientists and statisticians often talk about penalty, regularization and shrinkage. What do these terms mean and why are they important? According to Wikipedia, regularization "refers to a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. This information usually

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English

Advanced Analytics

Rob PrattJuly 3, 2017 0

Fun with Flags: Optimizing Arrangements of Stars with SAS

Tomorrow is Independence Day, a federal holiday in the United States. Flags are displayed everywhere, especially in Washington, D.C., where I live. So let's have a little Fun with Flags! The current U.S. flag has 50 stars, one per state, with five rows of six stars interleaved with four rows

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English

Advanced Analytics | Programming Tips

Makoto Unemi (畝見 真)July 3, 2017 0

SAS Viyaでフーリエ変換

みなさま、こんにちは。 さて突然ですが、フーリエ変換ってご存知ですか？ おそらく物理学や経済学で波形データを分析したことのある方には馴染みがあるでしょうが、フーリエ変換は波形データを扱う手法です。 フーリエ変換では周期的な波形を、sin波やcos波の重ね合わせで説明しようというものです。 たとえば以下のような波形データは、どの時間にどのくらいの強さの波が流れているかを表現しています。 これをフーリエ変換することで、周波数と振幅で表すことができるようになります。 ↓　フーリエ変換！　↓   従来のSAS製品では波形データでフーリエ変換をする機能を提供していなかったのですが、SAS ViyaのSAS Forcastingという製品を使うことで、フーリエ変換を実施することができるようになりました。 SAS Viyaでできるのは短時間フーリエ変換（Short time Fourier transform）です。 今回はSAS Viyaでフーリエ変換を実施してみたいと思います。プログラミング言語はPythonを使用します。 まずは前準備として、必要なライブラリをインポートし、CAS sessionを作成します。 CAS sessionはSAS Viyaでデータ分析を行うCASというエンジンへ認証し、接続するものです。 # CAS sessionの用意 import swat   host = "localhost" port = 5570 user = "user" password = "p@ssw0rd"   mysession = swat.CAS(host, port, user, password)   #

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Japanese

Advanced Analytics | Internet of Things | Machine Learning

Charlie ChaseJune 27, 2017 0

Forecasting at the edge for real-time demand execution

Let me start by posing a question: "Are you forecasting at the edge to anticipate what consumers want or need before they know it?"  Not just forecasting based on past demand behavior, but using real-time information as it is streaming in from connected devices on the Internet of Things (IoT).

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English

Manufacturing | Retail

Advanced Analytics | Learn SAS | Programming Tips

Rick WicklinJune 21, 2017 0

Jackknife estimates in SAS

One way to assess the precision of a statistic (a point estimate) is to compute the standard error, which is the standard deviation of the statistic's sampling distribution. A relatively large standard error indicates that the point estimate should be viewed with skepticism, either because the sample size is small

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English