BE343-6-AU-CO:
Python for Quantitative Analysis
2024/25
Essex Business School
Colchester Campus
Autumn
Undergraduate: Level 6
Future
Thursday 03 October 2024
Friday 13 December 2024
15
08 December 2023
Requisites for this module
(none)
(none)
(none)
(none)
(none)
The purpose of the module is to introduce students to the principles of programming with applications in quantitative analysis. Via Python programming language, the module provides a detailed basic coverage of programming including procedural programming, objects, data structures, input, and output procedures, data processing and visualization.
Students who complete this module should be able to write simple programs in Python, input data from external files and databases, perform quantitate analysis, export output and save them in the desired file location.
The aims of this module are:
- To develop students’ programming skills including object-oriented programming (IDE)
- To provide an overview of Python, Python Packages and Python Integrated Development Environment
- To enable students to design and write Python functions and create scripts to solve specific tasks
- To teach students how to manage and visualise data in Python
- To provide an overview of program specific statistical and financial analysis in Python, including descriptive statistics, regression analysis, time series analysis and forecasting
By the end of this module, students will be expected to be able to:
- Demonstrate the fundamental principles of programming
- Demonstrate a clear and comprehensive understanding of the Python programming language
- Process and critically analyse large datasets
- Connect to and manipulate databases from Python
- Design and organize Python code to answer complex problems associated with quantitative analysis in finance and business
Indicative Syllabus
- Introduction to Python: IDE, Python Basic Data Types, Basic Syntax
- Operators, Expressions, Control flow
- Functions, modules, data structure
- Object oriented Programming
- Numerical Computing with NumPy
- Data Visualization and Data analysis with Pandas
- Modelling Time Series
- Input/Output operations
- Case Study
This module will be delivered via:
- One 2-hour lecture per week
- One 1-hour lab per week
Students are expected to undertake the reading before classes and be prepared to engage in discussion.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Christos Argyropoulos
Yes
No
No
No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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