Not Getting into CS Changed My Career (For the Better)

When I started at UofT, I was admitted into CMP1, the computer science stream, with plans to pursue a data science specialist program. I had mapped out my academic journey: ace the required math and CS courses, get CS POSt, and dive into the world of AI and big data.

But things didn’t go as planned.

First-year hit me hard - MAT137 didn’t go as well as I had hoped. When the time came to apply for the CS POSt, my grades weren’t enough to get in. I was devastated. It felt like everything I had worked toward was slipping away.

At that moment, I had two choices: dwell on the setback or pivot and make the most of my opportunities. I chose the latter.

Today, I’m doing a double major in economics and statistics with a minor in CS, and I’ve had incredible opportunities in tech consulting, commodities, and finance—most recently working at TD Bank’s AI2 Finance Analytics and Data Science team. I’ve learned that not getting into a CS program isn’t the end of the world. In fact, the ability to adapt, network effectively, and apply technical skills across domains matters far more than any single program of study.

This blog is for those who want to break into finance and analytics—fields where a CS minor can be a major advantage over those without programming experience. And more importantly, for those who mistakenly believe in Cali or Bust (the false belief that if one doesn’t secure an internship in Silicon Valley/FAANG company, that represents a sign of failure).

Why LeetCode Isn’t Enough

Many CS students hoping to land quant or risk-related roles grind LeetCode, believing it’s the golden ticket to a career in finance. But quantitative finance is not your typical software engineering job—it demands much more than just coding skills.

Beyond DSA (data structures & algorithms), a strong foundation in probability, statistics, finance, and optimization is crucial. Here are the key areas to focus on:

1. Probability & Stochastic Processes: The Core of Risk & Trading

  • Why it matters: Markets are probabilistic, and risk models rely on randomness.
  • Key concepts: Monte Carlo simulations, Brownian motion, martingales.
  • How to learn: Read “Introduction to Probability” by Bertsekas & Tsitsiklis and implement probability models in Python.

2. Statistics & Econometrics: Understanding Market Data

  • Why it matters: Financial markets are noisy, and you need statistics to extract meaningful insights.
  • Key concepts: Time series analysis (ARIMA, GARCH), regression (OLS, Ridge/Lasso), causal inference.
  • How to learn: Work with financial datasets in Python using statsmodels and pandas.

3. Financial Derivatives & Risk Metrics: The Finance Side

  • Why it matters: Many roles involve options pricing, bond valuation, and portfolio risk metrics.
  • Key concepts: Black-Scholes model, Greeks (Delta, Vega), Value at Risk (VaR), Expected Shortfall.
  • How to learn: Read Hull’s Options, Futures, and Other Derivatives or take an online financial modeling course.

4. Optimization & Numerical Methods: The Math Behind Trading

  • Why it matters: Optimization plays a role in portfolio allocation, pricing models, and algorithmic trading.
  • Key concepts: Convex optimization, Lagrange multipliers, reinforcement learning.
  • How to learn: Experiment with scipy.optimize and cvxpy on real-world datasets.

5. SQL, Python, and C++: Practical Programming for Finance

  • Why it matters: Finance deals with massive datasets that require efficient querying and execution.
  • Key concepts: SQL for database queries, Python for data analysis (pandas, NumPy), C++ for high-frequency trading.
  • How to learn: Practice SQL on LeetCode and explore Python quant libraries like QuantLib.

6. Professional Certifications in Finance: Getting Your Foot In The Door

  • Why it matters: Certifications add credibility and signal expertise to employers.
  • Key certifications: CFA (Chartered Financial Analyst) for investment management, FRM (Financial Risk Manager) for risk management, and CQF (Certificate in Quantitative Finance) for specialized finance roles.
  • How to learn: Consider enrolling in certification programs or using free resources like Investopedia and Coursera.

Final Thoughts: Your Program Doesn’t Define You—Your Skills and Network Do

Looking back, not getting into the CS Major POSt was one of the best things that happened to me. It forced me to explore new fields, develop a broader set of skills, and build meaningful professional connections. If I had only focused on grinding LeetCode, I might have missed out on the incredible opportunities I’ve had in tech, commodities, and finance.

If you’re in a similar situation—whether you didn’t get into CS or feel like you’re missing key finance skills—remember: a CS minor is more than enough if you focus on the right areas. Exploring economics and statistics can give you a strong foundation for finance, so consider taking an introductory course like ECO105 and looking into an Economics program to complement your CS program.

Instead of just coding interview prep, try:

  • Analyzing financial data with Python
  • Building a simple Monte Carlo simulation
  • Networking with professionals in the field

At the end of the day, adaptability and resourcefulness will take you further than any single program of study ever could.

“Why do we fall, Bruce? So that we can learn to pick ourselves up.”
— Batman Begins (2005)

Now go crush it! 🚀

Resources

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