The job profile "Data Analysts and Scientists" is listed as number 1 of the Top 10 emerging jobs with a growing job demand for the year 2025. Source: Future of Jobs Report of the World Economic Forum.
Our students and alumni are pursuing very individual career paths.
For example, they work as data scientists, data engineers, machine learning engineers, data analysts, BI specialists, etc.
They are expected to assume multiple roles in an organization and need different skill sets, such as:
Deriving workable hypotheses
- Understanding business requirements and translating them into technical roadmaps
- Figuring out what data are needed and what story one can tell based on the results
- Designing practical approaches of data collection, model training and selection, as well as result communication
Organizing iterative and collaborative modelling
- Breaking down complicated data science operations with smaller goals: Proof-of-concepts based on small samples, plans for model training and selection, minimal viable products (MVP), productionalization of scalable applications
- Measuring the required resources and prerequisites for a task: Navigation through complicated data systems, integration of external data, possibilities and constraints of model choices
- Distributing tasks smartly to other colleagues, code reviews, and enabling
Output communication (Data visualization, storytelling)
- How to be concise, how to deep-dive in the evidence? How to design intuitive user experiences for even non-technical clients?
- How to increase the data literacy of your colleagues and clients?
- How to communicate the results?
Knowing and designing solution architecture
- Crafting orchestration for data and machine learning / statistical model life cycle: Extracting, Transforming, and Loading (ETL), as well as model training, tuning, selection, and serving
- Gaining proficiencies in advanced data engineering and machine learning engineering tools / frameworks
- Collaborating with other developers (backend, frontend, infrastructure, security etc.) with good practices in DevOps