Duration: 6 hours ​

Target Audience: Python developers with basic knowledge of Python and Pandas but new to machine learning ​

Pre-requisite: Familiarity with NumPy and Pandas ​

Learning Outcomes: ​

By the end of the workshop, participants will be able to: ​

1. Understand and explain key ML concepts and workflows ​
2. Prepare and preprocess real-world data for ML tasks ​
3. Train and evaluate basic ML models using scikit-learn ​
4. Understand the fundamentals of deploying ML models ​

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Course Outline: ​

​Module 1: Introduction to Machine Learning ​

Module 2: Data Preprocessing with Pandas ​

Module 3: Classic ML Algorithms ​

Module 4: Model Evaluation ​

Module 5: Deployment Basics ​

Duration: 3 hours ​

Target Audience: FASS postgraduate students ​

​Pre-requisite: Familiarity with SPSS ​

Learning Outcomes: ​

By the end of the workshop, participants will be able to: ​

1. Differentiate between types of quantitative methods relevant to digital media studies ​
2. Formulate and test hypotheses based on real-world research questions. ​
3. Apply correlation and regression techniques to analyse relationships between media variables.  ​
4. Interpret statistical findings in the context of visual culture, fan studies, and social media research.​

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Course Outline: ​

1. Introduction to Quantitative Methods in Visual and Digital Research ​
2. Formulating Research Question and Hypotheses ​
3. Introduction to Data Analysis Using Correlation and Regression ​