I bet many of you didn’t even know the term machine learning five years ago. But Gartner did. The Gartner Magic Quadrant for Data Science and Machine Learning Platforms, 2018 was just released, and SAS has been in the leader’s quadrant for five years straight. According to Gartner, “This Magic
Kürzlich habe ich mich sehr amüsiert über eine Reportage im Fernsehen: Man fragte Jugendliche, was sie mal beruflich machen wollen, und die Antworten waren „YouTube-Star“, „DJ“ und immer wieder auch „Influencer“. Letzteres ruft gerade bei der Ü30-Generation Verwunderung hervor, da man irgendwie an Grippe denkt – die Aussprache ist gleich,
Im Jahr 1963 wurde zu Silvester erstmals der Sketch „Dinner for One“ mit dem britischen Komiker Freddie Frinton im deutschen Fernsehen ausgestrahlt. Im Laufe der Jahre wurde diese Sendung zur lieb gewonnenen Tradition und erlebt immer wieder Phasen, in denen sie zum Kult avanciert. Die zentrale Frage lautet ja bekanntermaßen:
Meetup ist ein Online-Social-Networking-Service mit einem kleinen Unterschied zu Facebook, Instagram, LinkedIn & Co. Das Format ist dazu gedacht, Face-to-Face-Treffen zu interessanten Themen zu organisieren. Die Offline-Welt ist also ein elementarer Bestandteil. Selbstorganisiert und thematisch vielfältig Gruppen gibt es zu fast jedem Thema: Bücherclubs, Socialising, R-Nutzer, Unternehmer oder künstliche Intelligenz
W ostatnim czasie zalała nas bezprecedensowa fala popularności tematyki Data Science/Machine Learning. Mnóstwo szumu w mediach społecznościowych, tłumy na meetup-ach i konferencjach, popularność profilowanych studiów podyplomowych – to zaledwie kilka jej przejawów.
Wo können Sie Ihr Können mit dem von anderen Experten vergleichen? Und in der Data Science Community über den Tellerrand blicken? Oder einfach ungezwungen programmieren - wie zu Studienzeiten - weil es Spaß macht? Zum Beispiel bei einem Hackathon. Mit Fabian Buchert, selbst Data Scientist, sprach ich über seine Erfahrungen beim
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
Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
Experience design is not just like a standard advertising campaign or an online app, but rather a strategy to keep customers engaged with a brand through impactful interactions. It means that every product and service is designed to offer a delightful experience; the packaging, mobile app, web and print ads
My last post described my top general business analytics books, those that would appeal to business leaders and analysts alike. This post is a bit more specific, and covers books that will help you to learn for yourself. It is therefore mainly aimed at analysts — but I still hope
One aspect of high-quality information is consistency. We often think about consistency in terms of consistent values. A large portion of the effort expended on “data quality dimensions” essentially focuses on data value consistency. For example, when we describe accuracy, what we often mean is consistency with a defined source
One of the most frequent questions I’m asked by my students is which business analytics books to read to support their professional self-development. It is always hard to pick out the best books, especially because I like to mix classics and domain-specific references. I particularly like those that influence business
Data science may be a difficult term to define, but data scientists are definitely in great demand! Wayne Thompson, Senior Product Manager at SAS, defines data science as a broad field that entails applying domain knowledge and machine learning to extract insights from complex and often dark data. To further
There has been much discussion about the relationship between data science and artificial intelligence. It can become a complicated dance when applied data science is partnered with emerging artificial intelligence technologies. Who takes the lead? How do we keep the beat? Can we make sure neither party steps on the
We all find change easier when it starts with something we’re familiar with. That’s why I think sports analytics examples are popular – most of us are sports fans, so we get it more easily. It’s also why automotive examples that illustrate the potential reach of the Internet of Things
What's more, CXOs who believe that they can substitute data scientists for real data integration are as foolish as the duffer who consistently uses the wrong club.
Analytics, statistics, operations research, data science and machine learning - with which term do you prefer associate? Are you from the House of Capulet or Montague, or do you even care? Shakespeare's Juliet derides excess identification with names in the famous play, Romeo and Juliet. "What's in a name? That which we call
Machen wir doch gemeinsam eine kleine Zeitreise in die Zukunft. Es ist Freitag, der 29. April 2016, der Tag nach der größten Konferenz für Business Intelligence im deutschsprachigen Raum, dem SAS Forum Deutschland. Und wir lauschen dem Bericht zweier Teilnehmer des SAS Forum Deutschland, die sich über die Highlights des
"I've seen the future of data science, and it is filled with estrogen!" This was the opening remark at a recent talk I heard. If only I'd seen that vision of the future when I was in college. You see, I’ve always loved math (and still do). My first calculus
In der Vergangenheit hat sich die Agilität von BI-, Big Data- und Analytics- Anwendungen (Datenarchitekturen) als Erfolgsfaktor für Unternehmen aus unterschiedlichsten Branchen erwiesen. Gerade die Integration neuer Datenquellen in bestehende DWH-Architekturen und die daraus resultierenden Anpassungen resultieren in langwierigen Entwicklungsprozessen.
Data science is hot. You've undoubtedly heard a lot about the field and the role of the data scientist, now it's time to learn more. So, here are the top three reasons to become a data scientist. 1 – For the enjoyment of creating and building new “things” No, data scientists do not
You could argue that it’s misguided for someone like me to say data science doesn't have to be difficult. After all, I’ve been in the industry for many years and should have a few tricks up my sleeve for dealing with data. But with the latest data visualisation technology –
Ok, so the title is a little provocative, but some people are dubious that data science training is even possible, because they believe data science entails skills one can learn only on the job and not in a classroom. I am not in that camp, although I do believe that data
Along with the data scientist hype, analytics and the people who make them work have found themselves in the spotlight. The trend has also put an emphasis on the "science" aspects of analysis, such as a data focus, statistical rigor, controlled experiments and the like. Now, I’m not at all against adding more
I enjoy watching TV crime series like Law and Order, Crime Series Investigation (CSI), CriminalMinds, Numb3rs, Person of Interest, as well as real-life mystery stories on shows like 20/20 and others. Obviously, the popularity of these types of shows means I'm not the only one who enjoys this type of entertainment. Here at SAS,
“The most successful life sciences companies will be the ones that can convince their customers – patients, health care professionals, government authorities and health plans – that new treatments are the most effective and provide true value compared with alternatives.” Jamie Powers, DrPH, Principal Consultant and Practice Lead, SAS Health
We recently met up with Paul Bennett, a member of the GB Rowing Team and current World Champion, and Laurie Miles, Head of Analytics for SAS UK & Ireland, who has been analyzing the team's data. They chatted about data, the life and mind of an elite sportsman, and uncovered some
Well OK, so there is an "i" in science, but being a data scientist is certainly not a lonesome job. Engagement with other team members is essential with data analytics work, so you never really work in isolation. Without the rest of the team, we would fail to ask all
I read an interesting article recently that suggested analyst and data scientist job positions may be on the way out. The author argued that analytics are being incorporated more and more heavily into operational systems, making “analytic capabilities” more readily accessible to business users without the involvement of a data scientist. Being a data
It’s an incredibly exciting time for data science. Just ask Jake Porway, former New York Times Data Scientist and now CEO of DataKind, who opened his April 28 SAS Global Forum keynote by asking busy conference goers to pause and reflect on the revolutionary times we are living in. “Cell phones
