Financial Computing and Data Science

Course Modules

Each PhD student follows a personally tailored programme of postgraduate courses. The course modules cover: financial IT, networks & communications, HCI, computational finance, financial engineering, mathematics, statistics and business, financial mathematics and are supplemented by lectures from our industry partners.

Choices are agreed between the student, their supervisor and the programme director.

The courses undertaken address issues such as technical, analytical and financial knowledge, and international cultural awareness, with the aim of improving students' skills and supporting application to projects within the financial services industry.

Course Modules

Each PhD student follows a personally tailored programme of postgraduate courses. The course modules cover: financial IT, networks & communications, HCI, computational finance, financial engineering, mathematics, statistics and business, financial mathematics and are supplemented by lectures from our industry partners.

Choices are agreed between the student, their supervisor and the programme director.

The courses undertaken address issues such as technical, analytical and financial knowledge, and international cultural awareness, with the aim of improving students' skills and supporting application to projects within the financial services industry.

Courses Available

ELECTIVE MODULES:

Choose 45 credits from these elective modules.

All choices are subject to timetabling constraints and the approval of the relevant Module Tutors (i.e. to ensure any prerequisites are satisfied) and the Programme Director.

UCL COURSES:

Old module code

New module code

Module Title Term

COMPG001

COMP0040

Financial Data and Statistics

2

COMPG009

COMP0046

Networks and Systemic Risk

2

COMPG011

COMP0047

Data Analytics

2

COMPGA12

COMP0058

Applied Cryptography

2

COMPGC03

COMP0068

Architecture and Hardware

1

COMPGC04

COMP0069

Systems Infrastructure

1

COMPGC26

COMP0024

Artificial Intelligence and Neural Computing

2

COMPGI01

COMP0078

Supervised Learning

1

COMPGI08

COMP0080

Graphical Models

1

COMPGI09

COMP0081

Applied Machine Learning

2

COMPGI13

COMP0083

Advanced Topics in Machine Learning

1

COMPGI15

COMP0084

Information Retrieval and Data Mining

2

COMPGI16

COMP0085

Approximate Inference and Learning in Probabilistic Models

1

COMPGI17

COMP0053

Affective Computing and Human-Robot Interaction

2

COMPGI18

COMP0086

Probabilistic and Unsupervised Learning

1

COMPGI19

COMP0087

Statistical Natural Language Processing

2

COMPGS03

COMP0103

Validation & Verification

2

COMPGW01

COMP0123

Complex Networks and Web

1

COMPGW02

COMP0124

Multi-agent Artificial Intelligence

2

ECONG008

ECON0059

Advanced Microeconomic Theory

2

ECONG021

ECON0065

Microeconomics

1

MATHGM21

MATH0088

Quantitative and Computational Finance

1

MPHYG002

MPHY0022

Research Computing with C++

2

STATG001

STAT0028

Statistical Models and Data Analysis

1

STATG004

STAT0031

Applied Bayesian Methods

2

STATG011

STAT0010

Forecasting

2

STATG012

STAT0008

Statistical Inference

1

STATG017

STAT0013

Stochastic Methods in Finance

1

STATG019

STAT0017

Selected Topics in Statistics

2

 

OPTIONAL MODULES 

Module

Title

Term

Credits

COMPGR18

COMP0039

Entrepreneurship Theory and Practice

2

15

-

Foreign Language

 

15

EDUCGE01

Investigating Research

2

15

MPHYG001

MPHY0001

Research Software Engineering with Python

1

15

EDUCGE02

Researcher Professional Development

1

15

OPTIONAL MODULES RULES: 

Choose 30 credits from these optional modules.

All choices are subject to timetabling constraints and the approval of the relevant Module Tutor (i.e. to ensure any prerequisites are satisfied) and the Programme Director.

The level of the Foreign Language module is to be determined by the UCL Centre for Languages and International Education (CLIE), which determines the module code (www.ucl.ac.uk/clie/CourseUnits).

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