Skip to content

Finance Programming in Python and Databases

Overview

The modern financial world is driven by evidence-based financial monitoring and decision-making. The role of data in informing financial insights is expanding, requiring a more advanced understanding of data, along with the ability to process and communicate it effectively.

Broadly speaking, Python for Finance sits at an important crossroads between business intelligence and scientific methods and is considered one of the most relevant skills for various stages of careers in finance.

The course provides an introduction to software-based routines for data and financial analysis using Python.

Python is a general-purpose, open-source computing engine that enables the implementation of various data processing, computation, and communication tasks within a wide range of business and scientific contexts.

The course is organized into ten weekly sessions, held on Thursdays from 12:00 PM to 2:00 PM. Sessions are conducted online via Microsoft Teams, with session details provided below.

While the series is not part of the formal postgraduate assessment (0 credits), the insights and techniques covered are highly relevant to several coursework assignments and the dissertation throughout the year, especially when substantial data collection, data processing, and data analysis are required.

Course Contents

The course is organised according to theories and empirical facts related to financial markets and institutions. Both aspects are essential in terms of understanding the course material and examinations but also in terms of their importance towards developing a foundation for future careers in finance within or outside academia. Financial markets and financial institutions is delivered across the following main units:

  • Develop foundational Python programming skills.
  • Learn numerical computing techniques using NumPy.
  • Manage and manipulate datasets using Pandas.
  • Create effective data visualizations using Matplotlib.
  • Apply Python-based workflows to finance and business analytics problems.
  • Build skills directly applicable to coursework, research projects, and dissertations.

Course Timetable

The course is delivered via weekly sessions and six tutorial workshops. There are practice problem sets with solutions to further illustrate theories and implementations. There are three assessment assignments through the semester timetable below. The timetable below is subject to change, please review this timetable on weekly basis:

Course Schedule

Office Hours

Friday 10-11 am, Gilbert Scott Building

Course Tutorials and GTA Support

You are expected to have covered the material ahead of the tutorials. Tongtong Wang holds weekly office hours, starting in week 1. The schedule will be posted on MyGlasgow.

Financial Datasets and Empirical Exercises

The course contents, practice problem sets and assessment components are based on real-world financial data. Therefore, it is a requirement that all class participants set up their accounts with the data platforms described below:

  • Register your accounts on Financial Analysis Made Easy (FAME) via the university library and additionally Wharton Research Data Services directly on their platform using the university email address.
  • This registration is then activated by the business database administration within one week. Please initiate the registration in the first week of the course before we progress towards further course contents and assignments.
  • Key statistics and learning outcomes arising from the activities related to the data will be part of the exam. Treat the empirical exercises as an essential part of the learning experience
  • As a financial analyst or a research financial economist, you will work with the very same data providers repeatedly. Developing an understanding of the empirical counterparts of theories will be an important takeaway for future careers in finance.

Assessments

  • Assessment is based on a portfolio, comprising numerical and computational results submitted as subcomponent 1, in additional to a written report to explain derivations and economic interpretations.