Best Quant / Data Science Courses

To get back into coding and data science for research/trading, here are some of the best resources and strategies based on the most helpful WSO content:

Recommended Resources:

1. Python for Finance:

   • Datacamp: Offers a "Quantitative Analyst with R" career track and Python tutorials tailored for finance.
   • Sentdex: Free videos covering Python for finance, including Monte Carlo simulations and machine learning for trading.
   • Coursera: University-taught courses, such as Andrew Ng's Machine Learning course, which is beginner-friendly and can be applied to finance.
   • O’Reilly: Python for Finance: A book that focuses on Python applications in finance, ideal for building indices and backtesting strategies.

2. R for Finance:

   • R is highly regarded for statistical analysis and has a vast library of packages for financial modeling. R Studio provides a beginner-friendly environment.

3. SQL and Data Manipulation:

   • Learn SQL for data sourcing and manipulation. Advanced SQL skills can be invaluable for querying large datasets efficiently.

4. Linux Environment:

   • Set up a Linux environment (e.g., Ubuntu) to streamline your coding workflow. Learn basic command-line skills to enhance productivity.

5. QuantNet:

   • As you mentioned, QuantNet is a structured and reasonably priced option. It’s a great choice for someone looking to relearn coding with a focus on finance.

6. Additional Tools:

   • Pandas (Python): Essential for data manipulation and analysis.
   • Numpy and Seaborn: For numerical computations and data visualization.
   • Tableau/Power BI: For creating visualizations of your findings.

Day-to-Day in Systematic Trading and Fixed Income:

• Systematic Trading:

  • Heavy focus on data analysis, backtesting strategies, and coding algorithms.
  • Monitoring and optimizing trading models.
  • Collaborating with quants and developers to refine strategies.

• Fixed Income Seats:

  • Analyzing market trends and sector performance.
  • Building and maintaining models to assess risk and return.
  • Using tools like Python or R to evaluate cheapness/richness of sectors and names relative to historical data.

Suggested Approach:

1. Start with Python or R, as both are widely used in finance. Python is more versatile, while R excels in statistical analysis.
2. Focus on building small projects, such as custom indices or backtesting strategies, to apply your learning.
3. Gradually incorporate advanced tools like machine learning or API integrations for market data.

These resources and strategies should help you rebuild your coding and data science skills effectively without breaking the bank.

Sources: Programming/Technical Skills for Finance: SQL and Python, Programming/Technical Skills for Finance: SQL and Python, Best Way To Start Learning Quant Finance?, New breed of Finance and Capital Markets Professionals, 0 to pseudo quant real quick - analytical skills for juniors with finance background