Profile Review MFE 2025

[altopt]

Interested in MFE/computational finance, data science. Any inputs are much much appreciated.

Top UK university (think LSE/imperial)
Major: Economics, a social science subject
Grade: Expected first class (my first year was bad due to mental health issues and family bereavement so my math grades were in the low 60s, I've improved considerably in 2nd year despite a second bereavement: 75/100 top 15% of the class in econometrics, top decile of the class in research with other grades pending)
Relevant modules (taken or will take): quant methods (math), stats, micro I, micro II, macro I, macro II, econometrics I (STATA), econometrics II, further econometrics (year-long), intro to programming (python), research (R), further quant methods (linear algebra), statistical models and data analysis (R)
Internships: 2 summer internships in asset management (one at a top British firm with £400+bn AUM), 2 spring weeks
Research: Research assistant for a politics professor but involving statistics; research lead at undergrad journal (using R to analyse time series data on a socioeconomic topic)
GRE: expecting a good score, almost done with prep
Fluent in R, Python
Will take C++ course

I know my application isn't exactly fitting of the traditional profile. Should I bother with MFE at all or just stick with data science?

Thank you

 

[latus_rectum]

I see maths lacking from your profile. What subjects do quant methods (math) includes ?
At the very least, You would need a solid background in calc I, II, III, Probability Theory and Linear Algebra.

 

[altopt]

I see maths lacking from your profile. What subjects do quant methods (math) includes ?
At the very least, You would need a solid background in calc I, II, III, Probability Theory and Linear Algebra.

thank you for responding

between quant methods and further quant methods I cover:

sets, functions, equations, graphs. Difference equations, sequences, limits. Differentiation, inverse functions, exponential and logarithmic functions. Partial differentiation, chain rule, homogeneous functions. Optimisation in two variables: unconstrained and constrained. Lagrange multipliers. Vector notation and convexity. Matrix notation, systems of linear equations, inverse matrices. Integration. Differential equations.

Matrix methods in portfolio analysis. Linear independence. Rank of a matrix. Eigenvalues and eigenvectors. Diagonalisation. Linear systems of recurrence equations. Markov process. Second-order recurrence equations. Macroeconomic models. Vector geometry. Gradient and directional derivative. Tangent hyperplanes and the optimal bundle. Resource allocation and Pareto efficiency. Orthogonal matrices and quadratic forms. Critical points of quadratic functions. Taylor's approximation. Optimisation of functions of two or more variables.