Data products offer enormous opportunities for SMEs to open up new business areas or further develop existing fields. But how does an idea become a successful product - and how can it be turned into a sustainable business?

Format: Workshop with presentations, practical examples and interactive exercises

Target group: Small and medium-sized enterprises (SMEs) that want to utilise the potential of data products in the B2B sector to establish a new business model or advance their existing one. The course is aimed at beginners and interested parties without in-depth prior knowledge of data science

Techno-Z, seminar room C, Jakob Haringer Straße 5, 5020 Salzburg

The agenda includes theoretical lectures, practical workshops (such as "RL for business applications"), guest lectures from renowned institutions and networking opportunities.

Organiser: IDA Lab, Smart Analytics & Reinforcement Learning team in co-operation with AI Austria, Fraunhofer Austria, JOANNEUM RESEARCH ROBOTICS, ExtensityAI and DIH West

Registration is already possible!

RoomE.002 U1.002 (HS Agnes Muthspiel)

Find out how small and medium-sized companies can generate real added value from their data using specific use cases. Ask your individual questions to our experts and discover practical solutions.

No previous knowledge necessary!

Target group: Small and medium-sized enterprises

Location: Online

The link will be sent out a few days before the event!

Are you fascinated by the rapid advances in the fields of artificial intelligence and data science? Entrepreneurs who would like to utilise these developments in their own company in a targeted and successful manner and want to delve deep into the subject matter together with the experts at IDA Lab Salzburg are very welcome to join us.

Target group: Small and medium-sized enterprises (SMEs)

Meeting room IDA Lab: Techno-Z, Jakob Haringer Straße 6, 2nd floor right, 209, 5020 Salzburg

The time and format of the meeting (online or on site) are agreed individually.

Introduction to the basics of regression analysis with the aim of creating forecasts. The acquired knowledge of the regression types 

• Linear regression 
• Multiple linear regression 
• Non-parametric regression 
• Logistic regression 

are deepened using selected case studies. A bridge is built to the methods of machine learning. The statistical analyses are carried out using the programming language R; previous knowledge of R is helpful but not required. 

Please bring your own laptop!