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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?

{"text":"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:\n1. 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.\n2. Finance: You should have a deep understanding of finance concepts such as financial instruments and markets, trading strategies, portfolio optimization, and risk management.\n3. Data Analysis: You should have experience in data analysis and statistical modeling, including time-series analysis, regression analysis, and other machine learning techniques.\n4. 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.\n5. Communication: You will need to be able to communicate complex quantitative concepts to both technical and non-technical audiences.\n\nYou 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.","dsl":{}}

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.

{"text":"Sure, I'd be happy to ask you some specific questions to gauge your expertise in these areas:\n\nMathematics:\n- What is the difference between a derivative and an integral?\n- Can you explain eigenvalues and eigenvectors?\n- What is the purpose of Lagrange multipliers?\n- What is the difference between a linear and nonlinear optimization problem?\n\nFinance:\n- What is a stock option, and how does it work?\n- What is the difference between a stock and a bond?\n- Can you explain the efficient market hypothesis?\n- How does diversification help to mitigate risk in a portfolio?\n\nData Analysis:\n- What is the difference between supervised and unsupervised learning?\n- Can you explain the concept of overfitting?\n- What is regularization, and why is it important?\n- How do you evaluate the performance of a regression model?\n\nProgramming:\n- Can you explain the difference between a stack and a queue?\n- How do you handle missing data in a dataset?\n- What is the purpose of a try-except block in Python?\n- What is the difference between a deep copy and a shallow copy in Python?\n\nBased on the deficiencies identified from your responses to these questions, a learning plan could include:\n\nMathematics:\n- Review foundational calculus concepts and work through practice problems\n- Study linear algebra and practice solving problems involving matrices and eigenvectors\n- Work through optimization problems using both analytical and numerical techniques\n\nFinance:\n- Study financial markets and instruments, including options, futures, and other derivatives\n- Gain an understanding of portfolio theory and risk management techniques\n- Read up on the efficient market hypothesis and other theories of asset pricing\n\nData Analysis:\n- Work on building proficiency in machine learning algorithms, including both supervised and unsupervised learning techniques\n- Gain experience in data cleaning and preparation, including handling missing data and outliers\n- Learn about best practices in model selection, hyperparameter tuning, and model evaluation\n\nProgramming:\n- Expand your knowledge of Python libraries, including pandas, numpy, and scikit-learn\n- Practice coding problems involving data structures such as stacks and queues\n- Gain experience working with APIs and web scraping tools for obtaining data","dsl":{}}