Hi everyone 
,
As the title suggests, I am a pure mathematician trying to get into quant. I wanted to ask what you think of my current plan and how good my chances are. Here are the relevant details:
- BSc in Maths at Warwick, 1st Class, mostly focused on pure maths, but a couple courses on Python, R and numerical methods
- MSc in Pure Maths at Imperial College, 1st Class, mostly focused on pure maths (topology, geometry) but I realised I was interested in ML, so I also took Scientific Computing (Python) and Methods for Data Science (building classic ML methods, e.g. kNN, random forest, CNN, from scratch in Python). I also did my thesis in Topological Data Analysis(TDA), a new technique which uses algebraic topology to analyse data (basically extremely fancy feature extraction).
- Python for data science online long course (the basics, scikitlearn, tensorflow)
- Work experience: have been working for 3 months as a Geospatial Data Engineer (making a LightGBM model to estimate height of trees in the tropics using satellite data as predictors). Learnt about GBM + Version Control and practiced my Python.
My plan was to go through Elements of Statistical Learning (refresh and deepen stats + ML), study data structures + grind Leetcode (Python), MIT's youtube series on Topics in Mathematics with Applications in Finance (portfolio, stochastic, time series, etc.) as a start.
Then complement all this with important papers/papers on applying TDA (to maybe make myself stand out?), more in depth time series analysis, and making Linkedin posts about the topics I'm learning about to attract attention. In the future some small projects too, but I don't know what those would be because I haven't learnt the theory yet.
Is this reasonable? Or is there something huge I am missing? Or wasting my time with something? And do you think chances are good? For reference, I am thinking about London (but also open to other places in Europe).
Thank you!
,As the title suggests, I am a pure mathematician trying to get into quant. I wanted to ask what you think of my current plan and how good my chances are. Here are the relevant details:
- BSc in Maths at Warwick, 1st Class, mostly focused on pure maths, but a couple courses on Python, R and numerical methods
- MSc in Pure Maths at Imperial College, 1st Class, mostly focused on pure maths (topology, geometry) but I realised I was interested in ML, so I also took Scientific Computing (Python) and Methods for Data Science (building classic ML methods, e.g. kNN, random forest, CNN, from scratch in Python). I also did my thesis in Topological Data Analysis(TDA), a new technique which uses algebraic topology to analyse data (basically extremely fancy feature extraction).
- Python for data science online long course (the basics, scikitlearn, tensorflow)
- Work experience: have been working for 3 months as a Geospatial Data Engineer (making a LightGBM model to estimate height of trees in the tropics using satellite data as predictors). Learnt about GBM + Version Control and practiced my Python.
My plan was to go through Elements of Statistical Learning (refresh and deepen stats + ML), study data structures + grind Leetcode (Python), MIT's youtube series on Topics in Mathematics with Applications in Finance (portfolio, stochastic, time series, etc.) as a start.
Then complement all this with important papers/papers on applying TDA (to maybe make myself stand out?), more in depth time series analysis, and making Linkedin posts about the topics I'm learning about to attract attention. In the future some small projects too, but I don't know what those would be because I haven't learnt the theory yet.
Is this reasonable? Or is there something huge I am missing? Or wasting my time with something? And do you think chances are good? For reference, I am thinking about London (but also open to other places in Europe).
Thank you!
