prompter: I have completed a Computer Science degree at a university and worked as a Data Scientists for two years at a start-up. I can code in C, C++, and Python. The latter is the one I use at work. I want to work at a big fund like Square Point as a quant researcher. What additional skills do I need to gain in order to qualify for such position?
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