Module ECON1181: Mastering Data and Computation

Department: Economics

ECON1181: Mastering Data and Computation

Tied To

• CFC0: Natural Sciences (Psychology pathway)
• CFG0: Natural Sciences
• FGC0: Natural Sciences
• L125: Economics and Mathematics
• L130: Economics
• L133: Economics with Management
• L134: Economics with Management with Placement Year
• L135: Economics with Management with Study Abroad
• L136: (programme not found)
• L139: (programme not found)
• LA01: Liberal Arts
• LA02: Liberal Arts (with Year Abroad)
• LL01: Economics and Politics with Study Abroad
• LL02: Economics and Politics with Placement Year
• LL12: Economics and Politics
• LMV0: Combined Honours in Social Sciences

Pre-requisites

Corequisites

Excluded Combinations

Aims

• 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.

Content

• 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

• 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.

• 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.

• 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, private study and formative assessment. Summative assessment is by means of written assignments as follows:
• Group assignment Term 1: 500 words = 10%
• Group assignment Term 2: 1000 words = 10%
• Final Individual Assignment: 2000 words = 80%

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment

Continuous assessment in the form of quizzes.