IB9CU-15 Data Analytics
Introductory description
This module aims to equip students with the necessary knowledge and skills to understand, critique, and conduct quantitative financial accounting research.
Module aims
By the end of the module, students should be able to design and execute valid and reliable quantitative financial accounting research projects, using critical, analytical, and technical skills acquired during the term. The priority of the module would be the ability to implement these methods. This involves writing code in STATA to perform these methods so the students understand what they are doing and how to do it.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Introduction to Data Analytics
Data Types and Structures
Importing and Cleaning Data
Exploratory Data Analysis
Regression Analysis and Basic Econometrics
Text Data and Textual Analysis
Web Analytics and Data Scraping
Causal Inference: To Answer Quenstions in an Rigorous Way
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a general understanding of data analytics concepts and how data can be used to explore questions, identify patterns, and support decision-making.
- Demonstrate understanding of the role of programming languages such as R in facilitating data-driven analysis
- Approach problems analytically, interpret data outputs, and reflect on the strengths and limitations of different analytical approaches.
Indicative reading list
Reading lists can be found in Talis
Research element
This module will equip students with techniques for addressing real-world questions. It will not only expose them to how data can be used to answer such questions, but also provide them with theoretical frameworks to approach these questions in a rigorous and structured manner.
Interdisciplinary
The module will incorporate some interdisciplinary elements, from management and finance
Subject specific skills
Work with data in R, including basic techniques for importing, preparing, analyzing, and presenting data in a structured and reproducible manner.
Transferable skills
Communicate findings clearly through written and visual formats
Study time
| Type | Required |
|---|---|
| Lectures | 9 sessions of 1 hour (6%) |
| Seminars | 9 sessions of 1 hour (6%) |
| Online learning (independent) | 9 sessions of 1 hour (6%) |
| Private study | 49 hours (33%) |
| Assessment | 74 hours (49%) |
| Total | 150 hours |
Private study description
Self study to include preparation for assessment and pre-reading for lectures and seminars
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Individual Assignment 1 | 30% | 22 hours | Yes (extension) |
Reassessment component is the same |
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Assessment component |
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| Individual Assignment 2 | 60% | 44 hours | Yes (extension) |
|
2000 word essay |
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Reassessment component is the same |
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Assessment component |
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| Participation | 10% | 8 hours | No |
Reassessment component is the same |
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Feedback on assessment
Advice and feedback on project work will be given during seminars. Written feedback will be given on the submitted coursework.
There is currently no information about the courses for which this module is core or optional.