Lectures: 26 Feb - 1 Mar, C427 9 AM – 12:30 PM
Supervised learning: 1:30 PM – 2:30 PM
Office Hours: 2:45-3:45 PM
Schedule:
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9:00-9:15: Welcome and Overview - Introduction + logistics
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9:15-10:00 Lecture: Introduction to Social Data Science and the case of search data and Google Flu trends. [Slides]
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10:00-10:30 Tutorial: Introduction to Data Processing with Pandas. [Notebook 1.1]
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10:30:11:00 Break
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11:00-12:30 Tutorial: Using Google Trends data in Python. [Notebook 1.2]
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12:30-13:30 Lunch
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13:30-14:30 Supervised Learning: Testing the relationship between future orientation and GDP with Google Trends and World Bank API. [Exercise 1]
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09:00-10:30 Lecture: Social Impact in online media with regression and bootstrapping. [Slides]
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10:30:11:00 Break
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11:00-12:30 Tutorial: Reddit API, loading and dumping JSON, and linear regression basics in Python. [Notebook 2.1]
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12:30-13:30 Lunch
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13:30-14:30 Supervised Learning: Testing the division of impact hypothesis on Reddit. [Exercise 2]
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09:00-10:30 Lecture: Computational Affective Science: supervised and unsupervised sentiment analysis. [Slides]
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10:30:11:00 Break
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11:00-12:30 Tutorial: Off-the-self sentiment analysis (VADER and BERT) and supervised analysis with scikit-learn. [Notebook 3.1]
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12:30-13:30 Lunch
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13:30-14:30 Supervised Learning: Evaluation of sentiment analysis methods. [Exercise 3]
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09:00-10:30 Lecture: Online Social networks: concepts and node-level analysis. [Slides]
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10:30:11:00 Break
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11:00-12:30 Tutorial: Handling network data with NetworkX. [Notebook 4.1]
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12:30-13:30 Lunch
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13:30-14:30 Supervised Learning: Reading and visualizing Swiss politicians on Twitter. [Exercise 4]
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09:00-10:30 Lecture: Network-level metrics and analysis - Social resilience and communities. [Slides]
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10:30:11:00 Break
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11:00-12:30 Tutorial: Network analysis with NetworkX and advanced visualization with Gephi. [Notebook 5.1]
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12:30-13:30 Lunch
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13:30-14:30 Supervised Learning: Politician assortativity on Twitter + community detection. [Exercise 5]