Durham University
Programme and Module Handbook

Undergraduate Programme and Module Handbook 2023-2024


Department: Economics


Type Tied Level 1 Credits 20 Availability Available in 2023/24 Module Cap None. Location Durham
Tied to L100
Tied to L106
Tied to L109
Tied to L1R1
Tied to L103
Tied to L104
Tied to L105
Tied to LL12
Tied to LL02
Tied to LL01
Tied to VL52
Tied to VLL6
Tied to VLLA
Tied to CFG0
Tied to FGC0
Tied to FGC1
Tied to CFG1
Tied to CFG2
Tied to LA01
Tied to LA02
Tied to LMVA
Tied to LMV0


  • A level Maths


  • Principles of Economics (ECON1011) and Economic Methods (ECON 1021)

Excluded Combination of Modules

  • Programming (MATH1587)


  • To allow students to acquire, query, and understand the basic properties of data analysis and how to extract insights from data and report the results.
  • To provide an overview of the computational methods and tools which can be used in understanding data as well as theoretical questions.


  • This module will introduce handling of data on contemporary Economics and related topics which may include, Climate change, Hunger, Inequality, Poverty, Public Goods, Literacy, Taxation, Pricing of Goods and Services and others, in each week. The topics might have two to three subtopics, each containing multiple questions on the specific topic.
  • Students will be introduced to the topic and in the context of the topics and otherwise, various tools will be introduced provide hands-on experience, using real-world data, to investigate important policy problems. Step-by-step walk-throughs for conceptually difficult or challenging tasks would be done using the tools.
  • The tools may include:
  • Understanding a scripting language like R or Python
  • Understanding objects, commands and functions
  • Understanding functions and data frames
  • Using vector operations
  • Numerical solutions of linear and non-linear equations
  • Numerical solutions of optimisation problems
  • Accessing public and proprietary databases
  • Transformation and data loading methods
  • Data visualization tools and methods
  • Computing and interpreting descriptive statistics and plotting
  • Survey Designs
  • Sampling Design
  • Multivariate Data handling and plotting

Learning Outcomes

Subject-specific Knowledge:
  • By the end of the module, students should be able to:
  • demonstrate an understanding of a programming language such as R and its use in economics;
  • develop a knowledge of the central issues in data analysis;
  • understanding of computational algorithms and its use in economics;
  • Implementing numerical computations of economic models
  • use real world data sets using modern libraries of chosen languages and their ecosystems;
  • perform an exploratory data analysis including a variety of visualizations;
  • gain extensive first-hand experience of carrying out typical workflows of data analytics.
Subject-specific Skills:
  • demonstrate foundational skills in data science;
  • become skilled at advanced usage of economic data sources such as World Bank Open Data;
  • the ability to evaluate the usage of computational and empirical techniques;
  • acquire foundational skills in computer programming in data-analytics context and numerical computation.
Key Skills:
  • Written Communication – through the summative assessment;
  • Planning and Organising - observing the strict assignment deadlines; revising relevant material in preparation for assignments;
  • Problem Solving – e.g., by applying appropriate analytical and quantitative skills to evaluate theoretical concepts using real data;
  • Initiative – e.g., by identifying relevant tools and techniques for numerical analysis and data analysis;
  • Numeracy – e.g., by analysing data and carrying out computations;
  • Computer literacy – e.g., by using programming languages and other tools.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Teaching is by workshops. Learning takes place through attendance at workshops, and private study. Formative assessment is continuous in the form of quizzes. Summative assessment is by means of written assignments.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Workshops 16 8 in each semester 2 hours 32
Preparation & Reading 168
Total 200

Summative Assessment

Component: Assignment Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Group assignment Term 1 500 words 10% Same
Group assignment Term 2 1000 words 10% Same
Final Individual Assignment 2000 words 80% Same

Formative Assessment:

Continuous assessment in the form of quizzes

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University