assistant: To become a quant researcher at a big fund like Square Point, in addition to your existing skills, you may need to gain expertise in the following areas:
1. Mathematics: You should have a strong understanding of advanced mathematics such as calculus, probability theory, linear algebra, and numerical optimization. A strong foundation in these areas is essential for understanding the quantitative models that drive the financial industry.
2. Finance: You should have a deep understanding of finance concepts such as financial instruments and markets, trading strategies, portfolio optimization, and risk management.
3. Data Analysis: You should have experience in data analysis and statistical modeling, including time-series analysis, regression analysis, and other machine learning techniques.
4. Programming: While Python is widely used in the industry, knowledge of other languages such as R and MATLAB can also be useful, particularly for running complex simulations. Experience with data visualization tools such as Tableau or Power BI can also be beneficial.
5. Communication: You will need to be able to communicate complex quantitative concepts to both technical and non-technical audiences.

You may also want to consider obtaining a master's degree in a related field such as financial engineering or computational finance to strengthen your credentials and increase your chances of landing a job at a big fund. Additionally, gaining experience working with financial data, such as through internships or personal projects, can help demonstrate your expertise and enthusiasm for the field.

prompter: I want you to ask me specific questions to judge my expertise in these areas, and then create a learning plan for me to become proficient in them at a high level based on the deficiencies that you've identified.

assistant: Sure, I'd be happy to ask you some specific questions to gauge your expertise in these areas:

Mathematics:
- What is the difference between a derivative and an integral?
- Can you explain eigenvalues and eigenvectors?
- What is the purpose of Lagrange multipliers?
- What is the difference between a linear and nonlinear optimization problem?

Finance:
- What is a stock option, and how does it work?
- What is the difference between a stock and a bond?
- Can you explain the efficient market hypothesis?
- How does diversification help to mitigate risk in a portfolio?

Data Analysis:
- What is the difference between supervised and unsupervised learning?
- Can you explain the concept of overfitting?
- What is regularization, and why is it important?
- How do you evaluate the performance of a regression model?

Programming:
- Can you explain the difference between a stack and a queue?
- How do you handle missing data in a dataset?
- What is the purpose of a try-except block in Python?
- What is the difference between a deep copy and a shallow copy in Python?

Based on the deficiencies identified from your responses to these questions, a learning plan could include:

Mathematics:
- Review foundational calculus concepts and work through practice problems
- Study linear algebra and practice solving problems involving matrices and eigenvectors
- Work through optimization problems using both analytical and numerical techniques

Finance:
- Study financial markets and instruments, including options, futures, and other derivatives
- Gain an understanding of portfolio theory and risk management techniques
- Read up on the efficient market hypothesis and other theories of asset pricing

Data Analysis:
- Work on building proficiency in machine learning algorithms, including both supervised and unsupervised learning techniques
- Gain experience in data cleaning and preparation, including handling missing data and outliers
- Learn about best practices in model selection, hyperparameter tuning, and model evaluation

Programming:
- Expand your knowledge of Python libraries, including pandas, numpy, and scikit-learn
- Practice coding problems involving data structures such as stacks and queues
- Gain experience working with APIs and web scraping tools for obtaining data

assistant: Alright, here are some questions:
1. Mathematics:
    a) Suppose x and y satisfy the differential equation dy=0.01y(100-y)dx. Solve for y as a function of x if y is 20 when x is 0, then use the answer to find y when x = 3 and x when y = 80.
    b) Prove that AB and BA have the same eigenvalues if A and B are square matrices.
2. Finance:
    a) Explain the reasoning behind the popular 'rule of 72'.
    b) Explain the concept of an indifference curve.
3. Data Analysis:
    a) If a normally distributed variable has a mean of 90 and a standard deviation of 10, what is the probability a randomly chosen value is between 100 and 110?
    b) Two dice with the same number of sides are thrown several times. The products of their spins are 10, 28, 27, 50, 18, 8, 4, 1, 80, and 14. How many sides do they most likely have?
4. Programming:
    a) Write a MATLAB program to simulate a group of particles acting under the influence of each other's gravity.
    b) Write an R program to solve for the roots of any polynomial.
5. Communication:
    a) Explain the motivations for diversifying one's portfolio.
    b) Explain why actively managed funds are unlikely to perform better than passively managed ones.
I hope these questions are a helpful start for identifying your deficiencies. I can generate more if you like.