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
When I was growing up the term “data science” didn’t even exist, let alone dedicated “data scientist” roles. My friends and colleagues might argue that is because I am yet to grow up (!), but do not let this ruin my lead in to the fact that data science as
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
Don’t just think like a data scientist. Be one! You know analytical talent is in high demand. Differentiate yourself by earning a newly launched certification in big data and data science from SAS. The SAS Academy for Data Science can help you sharpen your skills and validate your expertise – for
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
With so much information available about high-performance analytics, business intelligence and visual analytics, it can be difficult to know exactly where to begin, especially if you don’t have a team of statisticians standing by. I'm frequently asked by customers who hope to take advantage of analytics how to get started. How do
As a data scientist, I have the rare privilege of possessing the job title that Tom Davenport and others have dubbed the sexiest job in the 21st Century. As this popular job title catches on, I’ve even noticed a trend where customers make direct requests for help specifically from “the data
“Correlation does not imply causation” is a saying commonly heard in science and statistics emphasizing that a correlation between two variables does not necessarily imply that one variable causes the other. One example of this is the relationship between rain and umbrellas. People buy more umbrellas when it rains. This
My previous post pondered the term disestimation, coined by Charles Seife in his book Proofiness: How You’re Being Fooled by the Numbers to warn us about understating or ignoring the uncertainties surrounding a number, mistaking it for a fact instead of the error-prone estimate that it really is. Sometimes this fact appears to
My previous post explained how confirmation bias can prevent you from behaving like the natural data scientist you like to imagine you are by driving your decision making toward data that confirms your existing beliefs. This post tells the story of another cognitive bias that works against data science. Consider the following scenario: Company-wide
