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[dhruvk]

Hi,

I need some honest advice regarding my application for masters in Financial Engineering.
I'm a 2016 computer science graduate. I worked at a top Indian government organization for 1 year. My work there includes lots of python programming. After that, I did 3 internships in Derivatives and Trading to gain some work exp in finance. I'm not currently working and focusing on making a stronger base in programming, math, and statistics by doing below things:-

1. Currently enrolled in Stanford's Graduate certification in Quantitative Methods in Finance. Completed autumn semester with A-Excellent Grade.
2. Completed Machine Learning Nanodegree from Udacity.
3. Incoming summer school 2019 student at London School of Economics & Political Science(LSE) for Introduction to Financial Mathematics course.
4. Completed University of California, Berkeley Math for Financial Engineering pre-MFE certification.
5. Completed Harvard Business School "Leading with Finance" certification.
6. Attended Asia Investment & Banking Conference (AIBC-2018). Participated in Merger and Acquisition competition and secured position in top 10 teams.
7. Completed various financial market courses from Coursera and scored above 90% in all of them.

My query is, will it be a negative impact on my profile to be not working and focusing on above things? I'm applying for MFE fall'20.

Thanks in advance!

 

[Onegin]

Knowledge is a good thing. Keep at it. I did find most programs weighted accredited knowledge and discounted udemy / Coursera type knowledge. If you can do a year of math or comp sci in India, it might have better returns. That’s at least what worked for me.

 

[Nth Root of N]

Knowledge is a good thing. Keep at it. I did find most programs weighted accredited knowledge and discounted udemy / Coursera type knowledge. If you can do a year of math or comp sci in India, it might have better returns. That’s at least what worked for me.

Within non-accredited, my thinking anecdotally is that a strong course of independent reading and implementation is better than Coursera, from the point of view of both industry and academia.

Industry sees that 1) you actually know what is relevant and 2) are willing to go directly for it, and academia sees that you are thinking in terms of accredited or nothing.

These are broad strokes, though.