This workshop is designed for educators (e.g., faculty, trainers) who have an interest in teaching and delivering data science courses/programs. Through a careful selection of case studies, datasets, and Python notebooks, this workshop will provide a pedagogical journey of teaching data science at scale. Based on experiences with teaching data science at LUMS, this workshop aims to share a rich set of perspectives about developing data science courses grounded in sound data design, exploratory data science and visualization, causal inference, statistical inference, machine learning, big data analysis, and data ethics.

The workshop will build on the conceptualization of data science as creative problem solving, with a focus on project-based learning. Through examples and case studies, the workshop aims to highlight the benefits of incorporating societally relevant and practical applications of data science in courses to cater to diverse audiences as well as to prepare students for impactful interdisciplinary work. Finally, the ways to scale data science courses while ensuring sound hands-on and classroom learning will be explored in this workshop.

Learning Outcomes:

By the end of this workshop, the participants should be able to:

• Conduct and teach sound data analysis using public datasets and appropriate examples and activities
• Develop strategies for integrating data science into existing curricula across different subject areas
• Describe a given dataset and assess its quality
• Build data pipelines (collection, cleaning, EDA, modelling, evaluation, results) for ''repeatable'' work
• Use Python and popular data science libraries such as Pandas, Numpy, Matplotlib, and Scikit-learn to analyze and visualize data.
• Understand the theory behind drawing inferences from data
• Communicate results effectively