An Interview with Assoc. Prof. Viera Labudová on guiding students to victory in the Curiosity Cup 2024, Data Analysis Domain.   Could you briefly describe your role in the team and during the competition? The project submitted by students from the Faculty of Economic Informatics at the University of Economics in Bratislava, Slovakia, to the Curiosity Cup was originally part of the "Analysis of Categorical Data" course, a second-year subject in the Data Science in Economics program. In this course, students select their research topics—often addressing issues pertinent to the younger generation—to conduct surveys and utilize the collected data to showcase their proficiency in categorical data analysis and their skills with the SAS Enterprise Guide. The course emphasizes teamwork, applying Belbin's theory of team roles to form balanced groups. Throughout the project, students receive guidance on questionnaire design, appropriate statistical methods, and other relevant aspects. My involvement included: Consulting on topic selection and providing general guidance on questionnaire design, scaling, and choice of statistical methods. After the students presented their project, I suggested they participate in the competition and offered moral support for their continued efforts. Consulting on selecting project elements that would be of interest in an international context. Guiding the creation of the final submission for the competition's first round and discussing the video format. How did you present this type of competition/project to the students? Is it now part of your course? I learned about this competition during a visit from Katarína Krausová, SAS Academic Program Manager, who presented the competition's conditions and benefits to our Data Science in Economics students. I have long incorporated team-based projects into my teaching, with each team producing a comprehensive project. The primary difference is that our course projects are more extensive and detailed. In the future, I will certainly encourage teams with high-quality and interesting outputs to participate in such competitions.   Did your team encounter any challenges during the competition, and how did you overcome them together? The main challenge was balancing the students' work on this project with their activities related to completing their final dissertations.   How has participating in the Curiosity Cup impacted your students' learning and development? The students who participated in the competition are now successful graduates of our faculty. I consider this achievement a significant culmination of their work. It certainly serves—and will continue to serve—as motivation for other students.   How do you plan to use what you learned in this competition in your future teaching? If I once again have students who not only possess quality knowledge but also demonstrate enthusiasm, creativity, and assertiveness, I will certainly support them in presenting their work in such formats. For me, experience in timing the different stages of project work is essential.   More information on the Curiosity Cup, including the latest winners, can be found HERE.   ... View more

Gerhard Svolba and I have been colleagues for over 20 years. He started at SAS in 1999, after working as a researcher and SAS user at the University of Vienna in the Department for Medical Statistics. Gerhard is a Data Scientist (and Analytic Solution Architect) at SAS, and also a part-time lecturer in data science at several universities. I caught up with him to talk about his career in data science, and his experience of teaching, including a guest appearance at his son’s primary school many years ago. Gerhard, tell me about your teaching work. Where do you teach, and in what subjects? I teach at three universities for applied sciences, in Styer (University of Applied Sciences Upper Austria), Burgenland (University of Applied Sciences Burgenland)  and Krems (University of Applied Sciences Krems). I teach sales and marketing students at Krems and Styer, and process engineering students at Burgenland. Statistics is not their main subject, so I try to show them relevant analytical business case studies, and provide as much context as possible. I also teach at two classic universities, Johannes Kepler University in Linz and every second/third year at Medical University of Vienna. I teach the medical students in Vienna on methodology, and at Linz I teach statistics students a mixture of statistical programming and business cases. How do you find balancing your work at SAS with your teaching? I left the university to do more practical work. I love doing research and teaching, but I also had so many great ideas that I thought would be relevant for customers, and so I joined SAS. After a while, I realised that I wanted to document my work, and so I started writing books. Teaching and writing both give me a chance to share my knowledge with other people. I love seeing people get excited about using data science to solve business questions. We both know how fast data science moves. Are you planning on writing any new books to cover new areas? This may change, but I currently have no plans to write any books in the next three years. I don’t really have time with all my teaching. However, I am reusing a lot of the content from my books in articles and YouTube webinars, which has given me the chance to update it a bit (there are some examples here and here). That said, I think my books are still relevant. I’m not saying nothing has changed, because we are definitely talking less about data mining and statistics and more about machine learning and artificial intelligence. If I was writing my books now, I’d use slightly different language, but the content remains useful. What are your current projects at SAS? Recently, I have been worked with the International Institute for System Analysis (IISA), a worldwide research company, on a computer vision project to classify satellite pictures of the Amazon rainforest. The idea is to use images to see where there is a high probability of manual deforestation. We are using artificial intelligence methods like computer vision and training and identifying sections on the picture. Another interesting recent project was the COVID-19 portal for the German Ministry of Health to bring together data from different sources, and make them analysable at different levels. My job was to build a capacity simulation to answer questions about the number of people who might need admitting to hospital, how many would end up in intensive care, and how long they might stay, and to forecast the number of beds needed. I’m also doing some work with natural language processing area to support public organisations and authorities and automate certain processes around building services for citizens. Based on your experience teaching the next generation, how do you see the future of the data science and AI? Looking at my experience over the last 25 years, we are already able to solve certain analytical problems which were not possible in the past. However, when I teach my marketing students, I see the importance of teaching people about very basic concepts. It’s vital that people understand that even fairly simple analysis can provide new insights. You also need to be able to explain this knowledge to others. The second issue that matters is that as a data scientist, you don’t spend all your time building models. You also need to clean data, or talk to others about where to find the right data. Analytics is multidimensional, from data to business. Gerhard, thank you. ... View more

Ricardo Galante is a Senior Analytics Customer Advisor for SAS Iberia, based in Lisbon, Portugal. He is also a mentor for the SAS Hackathon, a statistician and a PhD researcher, and an invited professor at three universities in Lisbon, the University of Lisbon, the European University, and the Portuguese Institute of Marketing Administration (IPAM). I caught up with Ricardo to talk about teaching, data science and the SAS Hackathon. Ricardo, you wear so many hats: customer advisor, hackathon mentor, researcher in machine learning and artificial intelligence and data science professor. Which is your main hat? Interesting question! It would be impossible for me to define myself as ‘just’ a customer advisor or professor or researcher, or even say that I am more one of those things than another. They’re so closely related. My ‘day job’ is a customer advisor at SAS, but while I am doing that, I am also researching new approaches on machine learning and data science, and when I am teaching students, I am also improving my skills as a customer advisor. What they all share is that I am learning all the time. You are a Brazilian, but currently living in Lisbon. Was your move for personal or professional reasons? I started my SAS career in 2007 at SAS Brasil in São Paulo. I worked as an instructor in the Education area, and taught courses on analytics, before I started working on the customer advisory team. I loved working in São Paulo, but I couldn’t turn down the opportunity to join the Iberian customer advisory team at SAS Portugal when it was offered in 2015. The move has worked out well for me and my family and we have no regrets about it. Tell us about your teaching work. I think you’ve recently been involved in a new SAS Lab at the University of Lisbon? I currently teach at three universities. At IPAM and the European University, I teach Applied Data Science in Marketing, and at the University of Lisbon, I teach Data Science with a more statistical approach. The new SAS Lab at the University of Lisbon opened a few months ago. This aims to link the academic world, the corporate world and SAS. It is a great environment for these interactions, with different courses aimed at academia and companies. It will also be an opportunity for students, professors and professionals to exchange experience and knowledge. Have you noticed any changes among young data scientists since you started teaching 10 years ago? There has been a lot of change, and a real evolution of young data scientists in recent years. Data science as a field has existed for 30 years, but it has certainly gained prominence in recent years because of the emergence and popularization of large databases and the development of areas such as artificial intelligence and machine learning. There is no question that this knowledge is highly sought after in the job market. Students are well aware of this. I think there has been a cultural change in young data scientists: they now want to know much more about data science in context, not as an isolated science. You are one of the SAS faces for the forthcoming hackathon. Tell us more about the event. I am one of the mentors for the SAS Global Hackathon with a particular focus on the retail area. The SAS Hackathon is for developers, students, start-ups, SAS customers and technology partners who want to solve a real problem and present the solution using SAS and Open Source technology. In this Hackathon, participants collaborate online for a month, improving their data science skills. The deadlines are: January – February: Technology enablement, resources and support on SAS Communities in SAS Hacker’s Hub. The deadline for registration is February 28. February 9: Kick-off session on LinkedIn and YouTube. March 15 – April 15: The month of the Hackathon. April – May: Jury voting. June: Industry track, technology and regional awards. September: The overall winner will be announced at SAS Explore: An Analytics Experience for Technologists. You can find out more at https://www.sas.com/sas/events/hackathon.html. I have no doubt that participating in this Hackathon will be a unique experience. Because when teams from different regions come together, with diverse backgrounds and skills working in analytics, data science and artificial intelligence, amazing things can happen. And being a part of it is unforgettable. Is there anything else that you are particularly looking forward to in 2023? I think that 2023 will be a challenging year, because of the global political and geoeconomic situation. However, I also hope that it will be a fantastic year with many interesting challenges, learning and work. Thank you, Ricardo. ... View more

Iain Brown is the head of data science at SAS UK and Ireland. He is also an adjunct professor, one of Data IQ’s top 100 most influential people in data and was recently chosen by ONALYTICA as one of the 50 influencers to follow on the topic of artificial intelligence. He has over 32,000 followers on LinkedIn and 110,000 followers on Twitter as of December 2022. I caught up with him to talk about data science, and the challenges and pleasures of teaching it. Iain, how did you become a data scientist? Funnily it was never really part of the plan to become a data scientist. I have always had a love for mathematics and a curiosity to explain how and why things are. This led me to pursue applied statistics at university, which led to a postgraduate degree in Operational Research, which is simply applied statistics for business problems, and then onto a PhD. However, at that point no one was talking about data science: that was a term that hadn’t yet been invented. I wanted to work in business and find ways in which I could use data analytics to understand how things worked and could work better, and I ended up falling into data science purely out of luck! Besides being head of data science in SAS UK and Ireland, you teach marketing data science at the University of Southampton. Why did you decide to teach? I completed my own PhD at the University of Southampton, so I know the university well. Going into the world of work, I was always keen to give something back. One of my fellow PhD students is now a professor at the University of Southampton, and about six years ago, she told me that they were looking for someone to teach an applied marketing analytics course, so I jumped at the opportunity. I was able to redevelop the course, drawing on my knowledge and experience over the last 15 years of applying these techniques in the real world. I’ve turned it into a very practical use case-led course. I think it’s incredibly important to build strong relationships with universities, because we need to nurture and develop the next generation of curious minds to solve tomorrow’s problems. How do you describe data science to your students when you first start teaching them? A lot of students are not aware of the potential of data science and what a career in this field would entail. There are plenty of courses on offer, but they don’t always join the dots between mainstream subjects like math’s and stats, science and engineering and what data scientists do. Coming from a data science background and working in that space, I always start by explaining the value data scientists provide, both to organisations and more generally through how it effects our day to day lives. I try to bring data science to life by showing students its practical applications, how data is transformed into intelligence and how businesses, public sector bodies or healthcare providers can use those insights to make decisions that affect people’s lives. From your experience, how has the use of data science changed in business? I’ve been in my current role just over 11 years now. If I go back to when I started, it was an uphill struggle to explain to businesses the benefit of using data based decisioning over more traditional gut feel. There wasn’t a common acceptance as to why you would use data to make decisions instead of drawing on personal experience and knowledge. Fast forward to today and most organisations appreciate the value of data and if anything, they want to do more with it. But even now I would there are huge opportunities to do more with data with 70%-80% still underutilized in unstructured form (e.g., free form text, conversations, or images). Most of my work today is exploring new use cases to get more out of data. The focus has changed from why should we do it to what else can we do with the data? What is the data science project you are the proudest of so far in your career and why? One of the projects I personally most enjoyed was working with a large online digital retailer, helping them to understanding the patterns and behaviours of customers and to make accurate real-time personalised recommendations to their customers. Working on that was incredibly rewarding and added a lot of value to the business. However, I’m prouder of the work my team has worked on for Data4Good projects, be it using computer vision to transform kidney treatment for the NHS or using AI to save mothers and babies through early diagnosis of pre-eclampsia. Being involved in an organisation that puts societal benefits at the forefront of our work is fantastic. It goes beyond the business value that organisations can achieve, and towards the human-centric value that all of us can benefit from when the right people, the right skills, the right data and the right capabilities are put together to address key societal challenges. Under your LinkedIn profile https://www.linkedin.com/in/iainljbrown/ we can find many interesting insights, articles…if students asked me to give them an advice what to read/follow in the topic of Data Science and Data Analytics I would recommend your LinkedIn for sure but what are your recommendations for other sources of knowledge? I’m a big fan of podcasts, medium blogs and YouTube series. I’ve found these are great ways to ingest information and to catch up with it on the go. I can certainly recommend the following; Lex Fridman; Data Science Central; and StatQuest with Josh Starmer. Thank you, Iain. ... View more

The number of academics using and teaching SAS has grown steadily over time. More than 3,000 universities now use SAS in their teaching and research activities. Also over time, it has become clear that this group is keen to come together as a community, to share ideas and experience, and learn from each other. This community has been fairly informal until now. However, we have recently put in place new support mechanisms to help it grow and develop, and it also has a name: the SAS Powered Educators Community.   Networking for success One of the issues that we keep finding among SAS users and customers is a keen desire to learn from peers. SAS provides the tools and the initial expertise on analytics and how to use those tools. However, we don’t necessarily have the deep domain expertise in our customers’ fields. SAS communities offer the opportunity for SAS users with particular shared interests to come together and learn from each other about the experience of using SAS tools in their world. They also give us a chance to learn from our customers about what they need, and therefore enable us to help them to solve problems more effectively. The SAS-Powered Educators Community is focused on universities and the academic community. Its initial focus was Central and Eastern Europe. However, in this hybrid world, there is no reason why academics and universities from elsewhere should not be involved. The idea is to allow academics to come together both in-person and virtually, to learn how different universities use and teach with SAS, and to share ideas.   We run quarterly meetings, each lasting 90 minutes. These are hybrid events: part in-person and part virtual. We will be present at the host university, along with the hosts themselves, and quite likely others too. However, we expect the majority of attendees to be remote, from across Europe, the Middle East and Africa, and even beyond if they wish. The session will also be recorded, and the recording—or, more likely, an edited and condensed version that is easier to manage—will be available afterwards for those who were unable to attend. Each event to feature a particular university, and a particular topic that the group has identified as important. There will be a short presentation, followed by a moderated discussion and question and answer session. These events will be led by the interests of the group, and by what the wider community wants to discuss. Focus will be on the big issues facing universities in teaching and using SAS. We want to be able to showcase those who are successfully tackling these problems, and help others to learn from their experience.   Kicking off the series in Poland The first event will be from the Warsaw University of Technology in Poland. It will be hosted by Ewa Hryciuk, SAS’s Academic Programme Manager for the area, who will moderate the discussion. There will be two speakers, Agnieszka Kucharska, from the Faculty of Management, and Bartosz Jabłoński, from the Faculty of Mathematics and Information Science. They will talk about using SAS Viya in teaching. They will start by discussing the university’s migration from SAS9 to SAS Viya, and the main differences between the two. They will also touch on why you might want to migrate, and when is the best time to do so. They will also talk about SAS Viya for Learners, and the advantages and disadvantages of using SAS Viya for teaching. This should be a very interesting discussion. Agnieszka’s research is on business intelligence and the use of Big Data in the public sector. She is very business-oriented in her approach. Bartosz, by contrast, is more technical. He is a mathematician by background, and has extensive experience of using SAS tools. It will be fascinating to hear their different views from those perspectives and faculties, and how both are using SAS Viya in their teaching. Next quarter, the first in 2023, we will move to Romania, and Politehnica University of Bucharest. Beyond that, it will depend on the group, and who is prepared to share their experience and host an event. We’d love to hear from you if you’d like to get involved. We will be sending out invitations to people who we think would be interested, but please do get in touch if you’d like to attend.    The first edition - December 9 th , 2022 from Poland, a recording from the meet-up is available here: https://youtu.be/TjV1QbU96iA The second edition - March 31st, 2023 from Romania, a recording from the meeting is available here: SAS Powered Educators (4140044) (on24.com). ... View more