„Die wichtigsten Dinge schreibt man am besten gleich in die Einleitung! Eventuell lesen einige ja gar nicht bis zum Hauptteil weiter“. Einen ähnlichen Gedanken hatte ich bei meinem aktuellen Buch Applying Data Science – Business Case Studies Using SAS auch. Da sind bereits in der Einleitung die Mehrwerte aufgezählt, die
The Economist magazine takes pride in its ability to provoke deeper thinking. One of its campaigns was to ask people to taste weird-looking foods, such as smoothies made from “ugly” produce that had been rejected by supermarkets. The idea was to stimulate participants to reassess what was important to them.
Diese Frage bekomme ich von Nicht-Data-Scientists immer häufiger gestellt. Und es ranken sich viele Meinungen und Mythen um diese Expertengruppe. Genau aus diesem Grund habe ich mich mit Simon Greiner, einem angehenden Data Scientist und erfahrenen IT-Berater, unterhalten. Ein Mythos über Data Scientists: sie lesen keine Bücher mehr. Stimmt nicht!
Wien und die Donau: Zahlreiche Lieder, Geschichten und Filme dokumentieren die innige Beziehung zwischen der österreichischen Hauptstadt und „ihrem“ Fluss. Das war aber nicht immer so: Über Jahrhunderte stellte das Gewässer eine große Bedrohung für die Stadt dar – und es erforderte beträchtliche Ingenieurskunst, um die Donauauen in ein echtes
I can still remember the emergence in the 1990s of Java as a universal and largely open-sourced programming language. Some observers believed it would signal the eventual demise of proprietary languages such as C, Fortran and Cobol. Current surveys, however, and there are many, confirm that plenty of people continue
What is Data Preparation and why does it matter? Data preparation is all the tasks involved in collecting, processing, and cleansing data for use in analytics and business intelligence. It therefore includes accessing, loading, structuring, purging, unifying (joining), adjusting data types, checking fields to see that only valid values are
Data scientists need good skills in communication, data mining, data wrangling and more. Joyce Norris-Montanari explains.
La duda es uno de los nombres de la inteligencia. - Borges Para data scientist/analistas de datos/viejos mineros/lobos de mar. En los comienzos de nuestra carrera de data scientists, el poder de cómputo era muy inferior al que disponemos hoy. Sin embargo, muchas empresas ya veían el enorme potencial de
In my first two posts of this blog series, we heard why two students chose to pursue a STEM field and what appealed to them about data science. We also heard how they put their knowledge to work on a real-world data science project. Today, we'll hear their advice to future
Phil Simon chimes in with some tips on how to set these folks loose.
Mein Name ist Daniel und ich bin in der vierten Klasse. Diese Woche wurden die Eltern in meine Schule eingeladen, um uns etwas über ihre Arbeit zu erzählen. Und mein Vater war mit dabei. Am Anfang war ich etwas unsicher, ob ich mich darüber freuen sollte. Denn wenn sein Vortrag
“There are known knowns … There are known unknowns … There are also unknown unknowns” This statement by Donald Rumsfeld is famous from the world of politics. But the phrase “unknown unknowns” was actually coined by a NASA data scientist to describe the huge possibilities presented to us by big data
People who work with data have had a number of job titles in my time, including data admin, data specialist, ETL developer and data integrator. The rise of the term data scientist as the de facto job market term for an analyst, however, left us data people a bit high
The information obtained from all your accessible data is a great source of power for your organization to exercise. Information is power! By applying artificial intelligence and machine learning models, you can infer details that might be very sensitive about a person or community, which may or may not be
British Prime Minister Theresa May made it clear that her ambition is for the UK to become the global beacon for innovation in artificial intelligence when she spoke at the Annual General Meeting of the World Economic Forum earlier this year. Great news, then, that two UK master’s in data
In Q1 2018, the Serbian economy grew by 4.5 percent, well ahead of forecasts. The data science community also grew, rapidly attracting talent and energy, but still not fast enough to meet demand! Things Solver is a Serbia-based data analytics and engineering company working with customers worldwide, and is one
We hear a lot about data science nowadays, but do you ever wonder how it’s being used to help solve real-world problems? In my first post of this blog series, we heard why two students chose to pursue a STEM field and what appealed to them about data science. Today, we'll hear
Are you curious? Do you have a passion for science, technology, engineering and math (STEM)? Do you enjoy robotics or statistics? Do you like to solve hard problems? If you answered yes to any of the above, you might have what it takes to be a data scientist. Recently, I
The data science community has embraced hackathons in a big way. My colleagues are active participants, some are organisers and many are habitual prize winners. We have explored how hackathons support innovation and build teams. But mostly, these have looked at how to inspire adults. As a parent, I was
At the German SAS Forum, Sascha Schubert and I ran some hands-on sessions or workshops on data science and analytics. You might say that this was nothing new, but I noticed some changes from previous forums that I think may point towards wider changes in the analytical landscape. Here, then,
Analytics platforms have a lot to live up to. The scope may be fairly straightforward, but expectations can be high, and there is a wide range of users and customers, all of whom have slightly different needs. This post explores what IT decision makers can expect from an analytics platform.
Data-Science-Plattformen müssen vielen Anforderungen gerecht werden. Ihre Aufgaben sind relativ genau definiert – und die Erwartungen, die Unternehmen mitbringen, oft sehr hoch. Darüber hinaus sind die Anwendergruppen alles andere als homogen. Dieser Blog erläutert, was insbesondere IT-Entscheider erwarten. Was ist eine Data-Science-Plattform? Die Antwort ist einfach: Sie dient dazu, Erkenntnisse
Beim diesjährigen SAS Forum Deutschland in Bonn boten Sascha Schubert und ich einige Hands-on-Sessions zu Data Science und Analytics an. Nichts Neues, denken Sie wahrscheinlich. Aber mir sind einige Veränderungen zu vorherigen Events aufgefallen, die meiner Ansicht nach auf einen größeren Umbruch in der analytischen Landschaft verweisen. Hier also meine
For part 7 of this series, I had the pleasure of interviewing mathematician, former colleague, and data science “rock star” Longhow Lam. Since there’s no need for an office for his one-person company, we decided to meet for lunch in a very hot (31 degrees) and sunny Amstelveen city
Several weeks ago, I wrote about practical advice from a Chief Data Scientist in my blog “From Aristotle to Pi: Practical advice from a chief data scientist.” Now I want to offer my advice as a newbie trying to navigate through machine learning concepts and how to code them. Over
In a previous posting, SAS Customer Intelligence 360 was highlighted in the context of delivering relevant product, service, and content recommendations using automated machine learning within digital experiences. Shifting gears, SAS recognizes there are different user segments for our platform. This post will focus on building custom analytical recommendation models
Data science is hot. Being a data scientist has been described as one of the world’s sexiest careers – though that may say more about the people coining the description than data science – and data scientists are both in high demand and increasingly well-paid. It is, in fact, highly
What can you learn from a chief data scientist who's worked in analytics for for 25 years and has been involved in the development of many key SAS solutions, including SAS Enterprise Miner? As a veteran of the analytics industry, Wayne Thompson has witnessed the evolution of machine learning and
Prof. Dr. oec. HSG Holger K. von Jouanne-Diedrich has been Professor of Business Informatics and Customer Relationship Management at the Aschaffenburg University of Applied Sciences in Bavaria since 2013. He holds a doctorate from the Institute of Information Systems at the University of St. Gallen, Switzerland, and studied business administration
There is a saying that you learn something new every day. The idea behind it is that every situation offers the potential to learn something. In other words, you are always learning from those around you, and from what happens in your everyday life and work. This sounds wonderful. Ongoing
