Course Description
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Course Name
Time Dependent Data from Financial Analytics to Large Language Models
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Host University
Queen Mary, University of London
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Location
London, England
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Area of Study
Computer Science, Engineering Science and Math, Mathematics, Statistics
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Language Level
Taught In English
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Course Level Recommendations
Upper
ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.
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UK Credits
15 -
Recommended U.S. Semester Credits4
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Recommended U.S. Quarter Units6
Hours & Credits
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Overview
Course description:
This course is a basic introduction to the dynamics of time dependent data. We will start by discussing the type of data to be analysed. Apart from typical single number time series such as temperatures or stock prices, we will also consider the evolution of geospatial variables, 3D and text data.
This will be followed by some basic Exploratory Data Analysis in the context of time dependent data. The course will then provide insights on how time dependent data can be analysed based on real world examples and applications. Areas of applications that might be considered are speech, stock market evolution, music, geospatial data such as MRI scans, and medical time series data used in diagnostics.
This module aims to:
1. Introduce students to the fundamental concepts of time-dependent data analysis in financial markets, medicine, social studies and other domains.
2. Develop a basic understanding of the methodologies and tools used to analyse time series data.
3. Provide hands-on experience with real-world datasets to apply theoretical concepts.
4. Explore the application of time-dependent data analysis in the context of large language models and cognitive sciences.
5. Equip students with the skills to critically evaluate and interpret dynamic data trends in various fields.
Learning outcomes:
You will learn/develop:
• To understand fundamental concepts of time series analysis
• Apply predictive modelling techniques
• Explore large language models and their applications
• To analyse and interpret time dependent data Analysis
• To consider and evaluate models used in the analysis of time dependent data
• To utilise existing and new analytical and computational techniques in the resolution of real-life problems related to the field
• Critical Thinking and Problem-Solving
• Technical Proficiency
• Interdisciplinary Insight