Data Science in Practice is a series of 4 events that unites professionals from diverse domains and sectors who work with data science, providing a platform to share insights, discuss challenges, and explore innovative solutions.

The events emphasises the journey to a solution rather than the solution itself, as well as the mistakes along the way rather than the successes. Designed to foster collaboration and knowledge exchange, Data Science in Practice extends beyond the physical events. It encourages participants to engage in informal discussions and smaller group interactions between events, promoting continuous engagement and a robust network.

Are you missing

• Case studies and real-world applications of data science?
• A community of like-minded professionals where you can share and learn about the challenges of working with data science?
• Insights into the latest trends and technologies in data science?
• Access to leading professionals and thought-provoking discussions?

Whether you are working with data management, model deployment, or the entire MLOps process, Data Science in Practice is a space for you. Each event features leading professionals, interactive sessions, and ample networking opportunities.

Format
The first 1.5 hours is a structured format (presentations, panels, workshops)
The last 1.5 hours is an unstructured format (unconference, networking)

To accommodate accessibility to the events no matter if your work allows such networking within work hours or not, two events run within work hours from 13:00-16:00 and two events run after work hours from 16:00-19:00, providing equal options to participate despite sector.

Our goal is to have 20-40 participants per event.

While participation in each event is not mandatory, keeping in mind that building a strong network requires time and dedication. Regular attendance will help you forge meaningful connections and maximize the benefits of these networking opportunities.

Event 1 – Data Access, Usage, Quality, and Infrastructure

In our first session, we’ll cover what makes a strong foundation for any data science project, starting with the data. That includes access to the right data, ensuring quality, documentation, usage rights, and having the infrastructure to store, process, and version it effectively. A strong foundation also requires understanding the context behind the data, which is where close collaboration with domain experts comes in. It’s all about building data workflows that are technically solid and aligned with the real-world problems we’re trying to solve.

Agenda (subject to change)
13.00 Welcome & Introduction by Marie Helene Andersson, Managing Director of DDSA
13.15 Icebreaker

13.30 Presentation: Turning Physical into Digital: Data Workflows at PUFIN-ID by Ioanna Psylla, Digital Team Manager at Pufin-ID
This talk explores how PUFIN-ID captures and manages physical tag data crafted for product authentication systems. We’ll cover image collection, data processing, versioning with Data Version Control, and creating annotations with CVAT (Leading Image & Video Data Annotation Platform | CVAT). We’ll highlight key challenges in working with physical data, ranging from manufacturing constraints to image capture consistency, and share upcoming improvements to enhance quality and efficiency.

14.15 Discussion & Q&A
14.30 Break
14.45 Inspirational material
15.00 Unconference: Data collection, quality, usage rights, infrastructure
15.30 Facilitated Networking
16.00 End

Register here: https://forms.gle/1BidHYtuyBhTYk7h8