ABOUT THE DATA SNACKS
Ensuring the responsible handling of research data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable) is a crucial aspect of the research process. In fact, the adoption of a sustainable data management strategy has become essential for the success of third-party funding proposals (e.g. requirements of the DFG).
With the Data Snacks, we invite you to a culinary data break served in four short sessions to share our love for research data. The menu revolves around different challenges in data management. Embark on a delightful journey with us, exploring FAIR data in digital humanities, uncovering the secrets of the Nagoya Protocol, indulging in data licences, and venturing into the realm of data publishing.
Please check the Website of the Data Science Center of the University of Bremen for more information.
WHAT’S THIS SESSION ABOUT?
This Data Snack is for you if you work with large volumes of text data and want to extract meaningful insights efficiently. The session is designed to provide practical guidance on applying text mining techniques using Python to analyze and interpret unstructured text.
In this session, Maryam will discuss the growing importance of automated text analysis in various fields, including social sciences, healthcare, marketing, and customer service. She will explore the key challenges of working with text data, such as preprocessing, feature extraction, and model selection, and provide hands-on tips for building an effective text mining pipeline.
Text mining involves numerous complexities, from handling noisy and unstructured data to selecting the right models for classification and interpretation. Maryam will also touch on recent advancements in deep learning for text analysis and how they can enhance traditional methods. Whether you are new to text mining or looking to refine your skills, this session will equip you with the essential knowledge to apply text analysis techniques effectively in your research or business applications.
ABOUT THE SPEAKER
Maryam Movahedifar holds a PhD in Statistics and has extensive experience in Interpretable Machine Learning. With a strong foundation in statistical methods and practical experience in applying these techniques to real-world problems, she is well-equipped to teach complex machine learning concepts. Her expertise includes making advanced models understandable and accessible.
WHERE AND WHEN?
The info event will take place from 1:00 to 1:30pm via Zoom. There will be a 15-20 minute presentation followed by an open forum for questions and discussion. We look forward to exciting discussions!
