Module ECON41G15: Data Analytics

Department: Economics

ECON41G15: Data Analytics

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• The module aims to equip students with fundamental coding skills in Python and teach them how to apply these skills to processing, visualising, and analysing economic data. Students will work with real-world economic data sets, using key Python libraries such as Pandas, NumPy, and Matplotlib to develop hands-on experience in data manipulation and visualisation. The module also introduces essential statistical methods and basic machine learning techniques relevant to economic analysis. These skills are particularly valuable for students pursuing careers as professional economists working in private and public sectors or in the broader field of data analytics applied to economic contexts, where data-driven decision-making is becoming increasingly essential.

Content

• Topics covered in this module may include:
• Introduction to Python basics and essential Python libraries for data analysis
• Handling and cleaning data with Pandas
• Data visualisation with Matplotlib library
• Implementation of regression analysis in Python
• Implementation of supervised and unsupervised learning techniques in Python

Learning Outcomes

• Subject-specific Knowledge:
• be familiar with Python environments, key features, and the main libraries used for data analysis;
• understand data cleaning and preprocessing techniques;
• develop skills in data visualization, using charts and graphs to explore and communicate insights from real-world datasets;
• understand data analysis techniques commonly used to deal with real-world problems.

• Subject-specific Skills:
• be proficient in basic Python programming for data analysis;
• be able to import, clean, and manipulate datasets effectively;
• develop skills in data visualisation to explore and present insights;
• apply learned techniques to analyse datasets and process raw data efficiently.

• Written Communication
• Verbal Communication
• Problem-Solving and Analysis.
• Initiative
• Numeracy

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

• Teaching is delivered through two-hour workshops, combining interactive discussions and hands-on exercises. Learning occurs through active participation in workshops, preparation for sessions, engagement in individual assignments related to workshop topics, and collaboration on a group project.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

None

■ 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