Advanced Social Data Science II

After completing the course, the student is expected to be able to:

Knowledge:

•  Understand key concepts in traditional bag-of-words approaches to text data and how they relate to embeddings- and transformer-based approaches.
• Explain the differences between and capabilities of neural network architectures such as RNN, LSTM and attentions-based models.
• Account for various learning strategies, algorithms as well as approaches: clustering and unsupervised learning, supervised learning, semi-supervised learning, and transfer learning.
• Account for the potential of different representations, encodings and transformations of text, structured and unstructured.

Skills:

• Apply and justify preprocessing methods for text data.
• Extract reliable informations from text data using supervised and unsupervised learning and techniques from natural language processing.
• Use scikit-learn and PyTorch to apply basic and advanced machine learning models.
• Apply state-of-the-art deep transfer learning to classify unstructured data.

Competences:

• Integrate theoretical and applied knowledge within the field of Social Data Science and formulate compelling research questions given an unstructured dataset.
• Construct data sets from unstructured text and media data that are validated and well documented.
• Independently carry out a problem-driven end-to-end analysis given an unstructured dataset of text, including exploratory analysis and discovery using unsupervised methods and supervised learning for measurement, and assessment of model-based biases.
• Critically evaluate the implications of results, considering model limitations and biases, and systematic noise introduced by data collection and sampling methods.