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PP5000 Fundamentals for Policy Analysis: Coding, Mathematics, Statistics

Academic year

2026 to 2027 Summer before start of session

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

10

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Limited to students studying the Master of Public Policy.

Planned timetable

Mon - Fri, 9am - 12 noon

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Prof D A Jaeger

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Prof David Jaeger

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module is an intensive preparatory module designed to equip students in the Master of Public Policy programme with a common foundation in programming, mathematical reasoning, probability, and applied econometrics. The module focuses on practical data analysis using Python, core concepts in calculus and probability, statistical inference, and regression analysis through ordinary least squares. It introduces causal reasoning through the potential outcomes framework and the use of simple causal diagrams. Students also develop skills in interpreting and communicating quantitative results and in the responsible use of generative AI as a coding and learning tool. The module prepares students for more advanced quantitative coursework in the programme.

Assessment pattern

Coursework= 100%

Re-assessment

Coursework= 100%

Learning and teaching methods and delivery

Weekly contact

15 hr lecture (x 3 weeks)

Intended learning outcomes

  • Use Python for data loading, cleaning, and basic analysis
  • Understand functions (including graphing), and basic calculus (derivatives, partial derivatives, and integrals)
  • Understand probability, probability distributions and expectations
  • Understand causal questions using the potential outcomes framework and directed acylic graphs (DAGs)
  • Estimate ordinary least squares models in Python and interpret the results
  • Conduct and interpret hypothesis tests and confidence intervals