Too many data professionals are letting AI do their thinking. The result is broken pipelines, misaligned projects, and careers that stall at the first real challenge. Learning to use AI as a data analyst effectively, without surrendering the strategic judgement that makes the output valuable, is the capability that separates analysts who advance from those who plateau.

In the first conversation of this series, we look at where AI genuinely accelerates analytics work, where it quietly leads you down the wrong methodological path and why adaptability is the most important attribute any new analytics graduate can develop.

Host Eduardo Ariño de la Rubia is joined by Seneca Miller, a Data Analytics Scientist at eMedia Monitor in Vienna. She is a 2025 graduate of CEU’s Master of Science in Business Analytics programme and winner of the cohort’s Best Capstone Award.

THINGS WE SPOKE ABOUT

- Transitioning from marketing into analytics as a working professional

- Being a company’s first data hire with full project ownership

- How AI accelerated growth while creating knowledge blind spots

- When to step back from AI and rely on your own judgement

- What the MSBA should teach more and less of in the future

GUEST DETAILS

Seneca Miller is a Data Analytics Scientist at eMedia Monitor, a media monitoring company based in Vienna. A 2025 graduate of CEU’s Master of Science in Business Analytics programme, she won the cohort’s Best Capstone Award and joined eMedia Monitor as their first dedicated analytics hire. Reporting directly to the CEO, she designs and delivers data projects from the ground up, from NLP pipelines to topic classification models.

QUOTES

- “I can look at my project and say this from start to finish was what I did.” - Seneca Miller

- “People will outsource their thinking to AI. And that’s where people get in trouble.” - Seneca Miller

- “You might have linear growth, but this transforms everything to exponential growth.” - Seneca Miller

- “If I didn’t have this course, I would just have a bunch of puzzle pieces still sitting there that I’d still be trying to put together myself.” - Seneca Miller

- “It’s not the technical aspect of the work anymore that if you can write code, if you can put together a pipeline, it’s do you understand the business needs the business context, what that business wants, because every business incentivizes different things.” - Seneca Miller

KEYWORDS

#DataAnalytics #AnalyticsCareer #AITools #DataScience #MsbaProgram

Today our conversation explores why AI did not break academic assessment but made it impossible to verify, introduces the idea of signal inflation in technical fields and argues that sovereignty over decision-making is the one skill students must never outsource to a machine. We also reflect on how the MSBA curriculum at CEU will need to evolve and what a good relationship with AI looks like in research.

Our host Eduardo Ariño de la Rubia is joined by Ulrich Wohak, a postdoctoral researcher in the Department of Economics at Central European University, where he teaches coding and data visualisation in the MSBA programme. He holds two MSc degrees from the Barcelona School of Economics and Vienna University of Economics and Business and previously worked as a competition economist for the British government.

THINGS WE SPOKE ABOUT

- From chef to competition economist: Ulrich's unlikely path into academia

- Why AI empowers students but undermines academic assessment

- Ulrich's AI stack and how he uses it in research

- Maintaining sovereignty over decision-making in an AI world

- Signal inflation, curriculum evolution, and the future of the MSBA

GUEST DETAILS

Ulrich Wohak is a postdoctoral researcher in the Department of Economics at Central European University, where he also teaches coding and data visualisation in the MSBA programme. He holds two MSc degrees from the Barcelona School of Economics and Vienna University of Economics and Business, and previously worked as a competition economist for the British government. His current research, conducted alongside CEU colleagues, focuses on how AI is reshaping the labour market.

QUOTES

- "AI is such a cool product for students to engage more deeply with the material and to understand things maybe from a different angle. You know, it's a great learning tool, but it's also ripe for exploitation." - Ulrich Wohak

- "I think one should be very aware of what one outsources to AI. And I think there are some things that one should not give up sovereignty over." - Ulrich Wohak

- "I think students expect to learn very big ideas that deliver big results. But often I think what we're teaching is very small ideas that deliver big results." - Ulrich Wohak

- "It's a signal inflation, if you want, rather than a signal erosion." - Ulrich Wohak

- "I want to encourage them to use AI to empower themselves, not to outsource their thinking capacity." - Ulrich Wohak

KEYWORDS

#TeachingAnalytics #DataScience #AIEducation #AcademicIntegrity #HigherEducation

Think, Don't Just Prompt

Over-reliance on AI is quietly eroding the critical thinking and communication skills that data analytics careers are built on. Analysts who will thrive in the age of AI will be defined not by how well they can prompt a chatbot, but by how well they can think for themselves.

Our guest shares how the problem-solving skills he built during the MSBA, transferred directly to learning a new coding language on the job. He tells us why he uses an internal AI tool daily yet reaches for it sparingly and what he sees as the one capability the programme should emphasise more: face-to-face presentation and communication in front of real people.

Host Eduardo Ariño de la Rubia is joined in conversation with Ian Brandenburg, a 2024 graduate of the CEU Master of Science in Business Analytics programme. He is currently working as a data analyst and consultant in Vienna, Austria. With a background in business administration and early management experience gained during the COVID-19 pandemic, he brings a practical, people-first perspective to the world of data.

THINGS WE SPOKE ABOUT

- Transitioning from business management into data analytics

- How MSBA problem-solving skills transferred to a new coding language at work

- How AI is reshaping day-to-day work for data analysts

- Knowing when not to use AI: context, high stakes, and judgment calls

- What future MSBA graduates need to succeed in the age of AI

GUEST DETAILS

Ian Brandenburg is a 2024 graduate of the CEU Master of Science in Business Analytics programme, currently working as a data analyst and consultant in Vienna, Austria. With experience spanning business administration, general restaurant management, and classical piano, Ian brings a distinctly human perspective to the field of data analytics and is particularly focused on the role of critical thinking and communication as AI reshapes the profession.

QUOTES

- "There's a reason why we learn Python or learn coding in our program. It's really the problem-solving skills that we're going for, not to memorize the syntax." - Ian Brandenburg

- "We as data analysts, we are trying to convince our stakeholders that the solution that we have in mind is the effective solution that will take us to the next level." - Ian Brandenburg

- "I'm asking myself am I going to be able to give the AI enough context to give me a solution that is well rounded and sustainable?" - Ian Brandenburg

- "Critical thinking is incredibly important. That is not a skill that should go down. In fact, as along with communication should go up with Chat GPT coming in." - Ian Brandenburg

- "Prompt engineering isn't going to mean success. And I think that that's something that's really important for people to understand about where we are with AI." - Ian Brandenburg

KEYWORDS

#CriticalThinking #AnalyticsCareers #BusinessAnalytics #DataAnalyst #AiInTheWorkplace

Building the right data infrastructure before you find product-market fit is a trap. So is delegating the thinking to AI before you understand the system it is building for you. The question AI in data engineering education must answer now is not how to teach more tools. It is how to teach students to know what is sufficient, and when to push back on AI-generated results.

In this episode of our series, our guest explores how ten years of teaching at CEU has shifted his focus from courseware volume to hands-on depth, how AI has simultaneously delighted and concerned him in the classroom and what universities are getting dangerously wrong about how they try to regulate student AI use.

Our host Eduardo Ariño de la Rubia is joined by Zoltan Toth, a Professor of Practice in the MSBA programme at Central European University. A data engineer and educator with twenty years of experience, he is a bestselling Udemy instructor with more than 60,000 courses sold and a board member of the Vienna Data Science Group.

THINGS WE SPOKE ABOUT

- From web developer to CEU faculty: Zoltan's twenty-year journey into data engineering

- Why hands-on practice outperforms courseware volume in data education

- The AI hackathon moment that was both impressive and alarming

- Why debugging skills are now worth more than implementation skills

- What universities are getting wrong about regulating student AI use

GUEST DETAILS

Zoltan Toth is a Professor of Practice in the MSBA programme at Central European University, where he teaches cloud computing, modern data platforms, and AI engineering. He is a bestselling Udemy instructor with more than 60,000 courses sold, a former solutions architect and instructor for Databricks, and a board member of the Vienna Data Science Group, which organises meetups for data professionals across Austria.

QUOTES

- "I think that there is a danger there. So if you integrate some code, you still want to understand it." - Zoltan Toth

- "What we should meditate on is what is the kind of work that we delegate to AI, and what is the kind of work where we need to accept the cognitive pain of going through thinking and solving and just sweating it through." - Zoltan Toth

- "What we need instead is to give students a great mental model about how everything is built, so that they can take a look at the problems from an architectural point of view." - Zoltan Toth

- "We should embrace that we need to suffer through a few problems if we want to understand those problems." - Zoltan Toth

- "I think we are underestimating how much AI our students use, and we are overestimating our power, how much we can regulate how much AI students should use." - Zoltan Toth

KEYWORDS

#DataEngineering #AIEducation #TeachingAnalytics #DebuggingSkills #HigherEducation

Entering the analytics profession and discovering that 80 per cent of the job is cleaning dirty data is a reality check that no course prepares you for. Some say that analysts who thrive in an AI-augmented data science career, will be defined not by their ability to prompt a model, but by their capacity to ask the right questions and critically evaluate the answers.

Today we learn how the team at RBI is building knowledge graphs to ground LLMs in real organisational context, why SQL coding skills are declining in value while critical analysis is rising and what should change about the MSBA curriculum, including a stronger focus on ethics, data lineage, and AI-augmented workflows.

We are joined by Naida Dzigal, a 2023 graduate of the CEU Master of Science in Business Analytics programme and IT expert at Raiffeisen Bank International. With a PhD in technical physics and prior experience as a nuclear specialist at the International Atomic Energy Agency, Naida brings a rare combination of scientific rigour, multilingual diplomacy, and real-world analytics leadership to this conversation.

THINGS WE SPOKE ABOUT

- From nuclear physics to banking analytics via the MSBA

- Dirty data, frustration tolerance and the analytics reality check

- Using AI for 80 per cent of the working day at RBI

- Building knowledge graphs and context layers to ground LLMs

- Redesigning the MSBA curriculum for an AI-augmented analytics world

GUEST DETAILS

Naida Dzigal is an IT expert at Raiffeisen Bank International (RBI), where she is part of a strategic data transformation team reshaping how data is managed across the bank's entire network, influencing data processes that affect billions of euros annually. A physicist by training, she holds a PhD in technical physics from Technical University Vienna and previously served as a nuclear specialist at the International Atomic Energy Agency. She completed her Master of Science in Business Analytics at CEU in 2023 and speaks five languages, bringing scientific rigour and cross-cultural professional experience to the field of data and AI.

QUOTES

- "I was just so surprised that I was getting paid for essentially cleaning up data and spending maybe 20% of my time actually doing real analytical work." - Naida Dzigal

- "The biggest experts always have the highest frustration tolerance." - Naida Dzigal

- "I think where we fail most of the time is in our critical analysis of the answer." - Naida Dzigal

- "LLMs are very powerful. AI in general is very powerful, but it lacks context." - Naida Dzigal

- "The students that will succeed are the students who are able to ask the right questions." - Naida Dzigal

KEYWORDS

#AiDataScience #AnalyticsCareer #KnowledgeGraphs #DataTransformation #BusinessAnalytics

Teaching data analytics in the age of AI demands a fundamental rethink of what students need to learn, how they are assessed, and what analytical competence actually means when AI can write the code, run the models, and produce the results.

This episode explores the rapid collapse of traditional assessment, shares why taste may be the most valuable skill a data analyst can develop, and explains why working in teams of humans and AI agents is the direction the entire profession is heading. Our guest also reflects on how his Data Analysis with AI course at CEU has had to be redesigned with each passing semester and what universities must do differently if they are to stay relevant.

Our guest is Gabor Bekes, Professor of Economics and Programme Head of the MSBA at Central European University, Budapest. An applied economist whose research spans international trade, open source software collaboration, and industrial policy, Gabor is co-author of Data Analysis for Business, Economics, and Policy, published by Cambridge University Press, and has been teaching data analysis for over 15 years.

THINGS WE SPOKE ABOUT

- Why asking good questions matters more than choosing the right method

- The Data Analysis with AI course and what students discover

- How rapidly improving AI models overhauled the curriculum

- Taste as the emerging signal of analytical competence

- Zero-tech exams, dark factories, and the future of curriculum design

GUEST DETAILS

Gabor Bekes is Professor of Economics and Programme Head of the MSBA at Central European University, Budapest. An applied economist whose research spans international trade, open source software collaboration, and industrial policy, he is co-author of Data Analysis for Business, Economics, and Policy (Cambridge University Press) and has taught data analysis for over 15 years.