果冻影院

The module teaches quantitative skills, with an emphasis on the context and use of data. Students learn to focus on datasets which will allow them to explore questions in society 鈥� in arts, humanities, sports, criminal justice, economics, inequality, or policy. The student will be expected to work with Python to carry out data manipulation (cleaning and segmentation), analysis (for example, deriving descriptive statistics) and visualisation (graphing, mapping and other forms of visualisation). They will engage with literatures around a topic and connect their datasets and analyses to explore and decide wider arguments, and link their results to these contextual considerations.

The module is assessed by a group research project, using data analysis and visualisation to explore a 鈥渞eal-world鈥� question. The literature-research question-data-analysis-presentation-conclusion model follows the path of typical data-driven research projects which take place at a postgraduate and postdoctoral level.

Teaching Delivery

The module is taught in 10 weekly lectures and 10 weekly coding workshops.

Indicative Topics

• Introduction to Data Science听听
• Data Structures听听
• Spatial Data听听
• Text Data听听
• Distributions and Basic Statistics听
• Hypothesis Testing听
• 搁别驳谤别蝉蝉颈辞苍听听
• Difference in Differences听听
• Regression Discontinuity听听

Module aims and objectives

The aim of this module is to enable students to learn Python, and deploy these skills in order to quantitatively analyze datasets in a domain of their choosing. Upon completion of the course, students should be profficient in python, and have a solid foundation in hypothesis testing, regression, and basic causal inference methods.听听

Recommended Readings

The Python Data Science Handbook by Jake VanderPlas. 听