Applied Statistics and Financial Modelling vs Quantitative Risk Management with Machine Learning (QRM with ML)

Hi All,
Considering that I want to become a quantitative researcher, which of these 2 MSc programs would be more suited to my career goal? Keep in mind that I am doing CFA Level 1 in August and a lot of the introductory finance modules in QRM with ML is already covered in CFA, and if I do end up doing the applied statistics and financial modelling MSc, my dissertation will be ML focused.

i) Applied statistics and financial modelling

CORE MODULES
• Probability and Stochastic Modelling
• Statistical Analysis

COMPULSORY MODULES
• Continuous Time Stochastic Processes
• Stochastic Processes and Financial Applications

INDICATIVE OPTION MODULES
• Bayesian Methods
• Stochastic models and forecasting

Dissertation:
• Ai (Machine Learning) in finance

ii) Quantitative risk management with machine learning (QRM with ML)

Entrance module:
• Quantitative techniques (Maths, Statistics, Finance)

CORE MODULES
• Credit Risk Management
• Market Risk Management

COMPULSORY MODULES
• Financial Data Science with Python
• Mathematics of Financial Derivatives
• Portfolio Theory
• Statistical Analysis
• Statistical Learning

Dissertation MSc
• Quantitative Risk Management with Machine Learning

I look forward to hearing from you.

Kind regards,
Moein
 
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