이전에 포스팅했던 시민 데이터 사이언티스트(Citizen Data Scientist, 이하 CDS)에 대한 블로그에서 CDS가 갖춰야 할 기본 역량이 Data Literacy(한글로는 데이터 문해력으로 번역/이하 데이터 문해력)라고 소개했었습니다. 참고로 ‘문해력’이란 단어는 글을 읽고 이해하는 능력을 의미하는데, 데이터를 다룰 때도 마찬가지로 데이터를 읽고 이해하는 능력이 중요하기 때문에 이러한 용어를 사용해도 큰 무리는 없으리라 판단됩니다. 이번
Tag: data literacy
The SAS Resiliency Rules report explores the global resiliency landscape. This report highlights country-specific findings about the ongoing market challenges, the difference between an importance in attaining resiliency versus perceived resiliency, and the principles companies need to follow to drive business resiliency. Our research defines resiliency as the ability of
With so much complexity and change in the marketplace, organizations worldwide are leveraging opportunities to make better predictions, identify solutions and take strategic, proactive steps forward – which means that they increasingly depend on big data. In their quest for organizational resilience, however, companies find that numbers aren’t necessarily the secret
More than 100 volunteers from the SAS Young Professionals Network (YPN) led sessions at Wake Forest Elementary school for the annual week-long Hour of Code initiative during Computer Science Education Week (CSEdWeek) to teach kids to code. Students were challenged to work in groups and navigate a Sphero robot through
社会でのデータ活用が進むにつれ、それを推進する人材の必要性が増しています。データ活用人材、アナリティクス人材、データサイエンティスト、呼び方や役割はさまざまですが、そのスキルの根底にあるのは、「データリテラシー」です。データリテラシーとは、世界で起こっているさまざまなことを理解するために、データと対話できることを指します。データの有用性を見極め、信頼性を問い、意味を見出し、その洞察を意思決定に役立て、洞察を他者に伝えることができる一連のスキルです。内閣府、文部科学省、経済産業省は、大学における「リテラシーレベル」の数理・データサイエンス・AI教育プログラムについて、認定制度をはじめようとしています。 SASは、学生向けにデータサイエンスを学べる SAS Skill Builder for Students を無料で提供しています。Skill Builder for Students の e-Learning のなかに、データサイエンスを学ぶ最初のコースとして、Data Literacy Essential があります。このコースでは、身近な例を取り上げ、段階を踏んでわかりやすくデータリテラシーについて学ぶことができます。 SASは、アナリティクスが個人や組織の意思決定のために活用されるものであることを意識し、製品やサービスを展開しています。この Data Literacy Essential のコースでも、意思決定の際にデータとどう向き合えばよいのか、その理解のためのファースト・ステップを提供します。よく統計学の初級コースで、「まず平均や分散を計算してみましょう」という教材がありますが、実は、それ以前に理解すべきことがあります。なぜデータを見る必要があるのか、どのようにデータを集めるのか、そのデータはどういう性質を持っているのか、という疑問と、それらを知ろうとする姿勢が必要です。 このコースは6つのモジュールで構成されます。 Why Data Literacy Matters ... WebやSNSなどで出会うさまざまなデータを例にデータリテラシーの重要性を学びます。 Data Literacy Practices ... 商品の購入を例にデータリテラシーの実践を学びます。 Identifying Reliable Data ... ある家族の新型コロナ感染予防の取り組みを例に信頼できるデータの収集について学びます。 Discovering the Meaning of Data ... 新型コロナの影響を受けたビジネスを例にデータから知見をどのように得られるのかを学びます。 Making Data-informed Decisions ...
Students have returned to school and another year of education is underway. For some of them, though, the learning didn't stop over the summer. This dose of fun STEM education didn't require a classroom – just an iPad, a Sphero robot and the SAS® CodeSnaps app. Students work together to
Are you looking to broaden your data analytics skills to land your dream job or propel your career? After looking at job posting statistics and the country's labor market, the data shows that now is the time to jump on board. As the demand for data skills is growing, the
The SAS Batting Lab was recently featured on NBC’s Today Show. If you missed it, you can watch the segment in the video above. For more about The Batting Lab, get a firsthand look at the experience of the batting cage and learn more about the data literacy value of
SAS research statistician Ji Shen reveals how to train a machine to be a batting coach.
"Companies across pharma and medtech need talented people to cover the range of data-related challenges." Paolo Morelli, Executive VP, Biometrics of Alira Health Paolo Morelli, Executive Vice President, Biometrics of Alira Health, tells us how he developed a relationship between the University of Bologna and industry-leading companies – and what
The SAS Batting Lab is a six-week program designed to help improve kids’ understanding of data while also helping them improve their baseball and softball swings. Using analytics in an interactive, AI-powered batting cage, kids can compare their swings to batting stars. During the program, the participants also became more
In the face of rapid digitalization and modernization, data scientists in Cameroon joined the SAS Hackathon seeking a way to preserve indigenous African languages.
Six scholars from North Carolina A&T State University in technology– or STEM-focused majors helped foster the next generation of data-literate students while also donating to those in need. SAS recently facilitated a donation drive with students from the Wake County Young Men's Leadership Academy (WYMLA) in Raleigh, North Carolina and scholars from
Higher education institutions are some of the most data-rich entities in the world. Postsecondary leaders need high-quality, consistent and accurate insights to make the best decisions for their institution, students and constituents. Data governance is a topic that may seem technical in nature and perhaps important to only the IT
Harvard Business Review may have dubbed being a data scientist the “sexiest job of the 21st century”, but that hasn’t necessarily made it easier to find analytics experts. There is still a huge skills gap in data science. Recently I met with Andreas Vermeulen who is Director of Technology and
When students hear the word data, they may envision measurements that scientists collect, or perhaps it has something to do with the social media platforms they love. Many students struggle to understand where data comes from and how it can help us make decisions and better understand our world! They may not make a connection with computer science either. Just as
When our knowledge was contained in books, learning to read was vital to understanding the world. Today, new information is increasingly generated and communicated in numbers, charts and graphs. That’s why data literacy is emerging as an essential skill for everyone who wants to understand our data-rich world and make
Data science expert Andy Pulkstenis describes how to skip common machine learning mistakes.
Our traditional assumptions about data are evolving, and so is our understanding of data literacy. Data is more than numbers, charts and graphs. And data literacy is not just for data scientists. “If you’re talking with people who aren’t already data fluent, you have to make them aware that data is
Editor's note: This blog post is part of a series of posts, originally published here by our partner News Literacy Project, exploring the role of data in understanding our world. Like infographics, social media and other forms of user-generated content pose unique challenges regarding data. Many news outlets and journalists have checks and balances
Editor's note: This blog post is part of a series of posts, originally published here by our partner News Literacy Project, exploring the role of data in understanding our world. Infographics are one of the most visual ways to tell stories with data. They are designed to catch the reader’s eye, and they use
Editor's note: This blog post is part of a series of posts, originally published here by our partner News Literacy Project, exploring the role of data in understanding our world. Every day people use data to better understand the world. This helps them make decisions and measure impacts. But how do we take raw
Editor's note: This blog post is part of a series of posts, originally published here by our partner News Literacy Project, exploring the role of data in understanding our world. As discussed in previous posts, statistics and visual representations of data can be misleading. But what happens when the data itself is misleading? And if data is
Editor's note: This blog post is part of a series of posts, originally published here by our partner News Literacy Project, exploring the role of data in understanding our world. “Numbers don’t lie” is a phrase we often hear to support the idea that something must be true if you can cite data or
Editor's note: This blog post is the first in a series of posts, originally published here by our partner News Literacy Project, exploring the role of data in understanding our world. Charts and graphs are useful tools for communicating complex information. They allow consumers to see — rather than read or calculate — differences
SAS has always believed in the power of education, but in today’s data-driven economy, it’s more important than ever to ensure our students are introduced to data science at an early age. We as a company are focusing our resources on creating student experiences in data literacy, computer science and
Being overwhelmed by the volume of news isn’t a new phenomenon. But today, our sense of being overwhelmed has increased and triggered feelings of fear, frustration and anxiety, given the ongoing developments and research tied to COVID-19. How do we sift through the volume of information facing us and truly understand whether the news we consume is factual or based on
Testing people for coronavirus is a public health measure that reduces the spread of coronavirus. Dr. Anthony Fauci, a US infectious disease expert, recently mentioned the concept of "pool testing." The verb "to pool" means "to combine from different sources." In a USA Today article, Dr. Deborah Birx, the coordinator
During this coronavirus pandemic, there are many COVID-related graphs and curves in the news and on social media. The public, politicians, and pundits scrutinize each day's graphs to determine which communities are winning the fight against coronavirus. Interspersed among these many graphs is the oft-repeated mantra, "Flatten the curve!" As
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Rick Wicklinによって執筆されました。元記事はこちらです(英語)。 2020年における新型コロナウイルスの世界的流行のようなエピデミック状況下では、各国の感染確認者の累計数を示すグラフがメディアによって頻繁に示されます。多くの場合、これらのグラフは縦軸に対数スケール(対数目盛)を使います。このタイプのグラフにおける直線は、新たなケースが指数関数的ペースで急増していることを示します。直線の勾配はケースがどれほど急速に倍加するかの程度を示し、急勾配の直線ほど倍加時間が短いことを示します。ここでの「倍加時間」とは、「関連状況が何も変わらないと仮定した場合に、累計の感染確認者数が倍増するまでに要する時間の長さ」のことです。 本稿では、直近のデータを用いて倍加時間を推計する一つの方法を紹介します。この手法は、線形回帰を用いて曲線の勾配(m)を推計し、その後、倍加時間を log(2) / m として推計します。 本稿で使用しているデータは、2020年3月3日~3月27日の間の、4つの国(イタリア、米国、カナダ、韓国)における新型コロナウイルス感染症(以下、COVID-19)の感染確認者の累計数です。読者の皆さんは、本稿で使用しているデータとSASプログラムをダウンロードすることができます。 累計感染者数の対数スケール・ビジュアライゼーション このデータセットには4つの変数が含まれています。 変数Region: 国を示します。 変数Day: 2020年3月3日からの経過日数を示します。 変数Cumul: COVID-19の感染確認者の累計数を示します。 変数Log10Cumul: 感染確認累計数の「10を底とする対数」(=常用対数)を示します。SASでは、LOG10関数を用いて常用対数を計算することができます。 これらのデータをビジュアル化する目的には、PROC SGPLOTを使用できます。下図のグラフは感染確認者の総数をプロットしていますが、総数の縦軸に常用対数を指定するために「type=LOG」と「logbase=10」というオプションを使用しています。 title "Cumulative Counts (log scale)"; proc sgplot data=Virus; where Cumul > 0; series x=Day y=Cumul / group=Region curvelabel; xaxis grid; yaxis type=LOG logbase=10 grid values=(100 500 1000