Managing Director Oleksandr Bilokon (Thalesians Ltd and Thalesians Marine Ltd) is one of the leading experts on continued education in financial markets. Jointly with Neil Fowler’s WBS Training, he has been involved in market-leading quantitative analyst certifications from their inceptions and has an in-depth knowledge of these quantitative finance courses. Here is what he has to say.

Here’s a practical, no-nonsense orientation to the main finance and IT-adjacent certification paths a prospective student usually weighs up—CQF, CFA (incl. ESG), quant MSc programmes—and, especially, how the QDC (Quantitative Developer Certificate) and the MLI (Machine Learning Institute Certificate in Finance) stack up for careers in quantitative finance, data science, and engineering roles.

The quick map (who each one is “for”)

• QDC – Quantitative Developer Certificate: a quantitative analyst certification focused on the engineering of quant—coding models, market data, and infrastructure (Python, C++, kdb+/q). Ideal if you see yourself as a quant developer/strat, sitting between research and production. It’s a highly practical quantitative finance course with strong time-series/kdb+ exposure.
• MLI – Machine Learning Institute (Certificate in Finance): a seven-month, part-time quantitative finance online course specialising in machine learning in finance with an exam + project; good for quants, risk, and model validation aiming to formalise ML for markets.
• CQF – Certificate in Quantitative Finance: a broad, practice-oriented certificate in quantitative finance (six modules, electives, assessments) with strong brand recognition; think pricing, risk, computational finance, data science. It’s a flexible cqf program you can take globally with lifelong learning access.
• CFA – Chartered Financial Analyst + Sustainable Investing Certificate (the ESG track): best for investment management, portfolio management, investment analyst and investment banker paths; the ESG certificate (now “Sustainable Investing Certificate”) formalises sustainable investing/ESG integration skills.
• Quant MSc / Master’s degrees (e.g., master in quantitative finance, computational finance, financial engineering, master’s degree in finance, master’s degree in data science / data analytics): university-based finance programs—deeper theory, academic signalling, research pathways (and visas), but longer and costlier; great if you want a master program credential, campus student resources, and internships (e.g., New York/London ecosystems, technology programs at a business university). Such degrees are offered by Imperial College London and the University of Oxford in the UK, Courant in NYC, Paris Dauphine, etc.

Where QDC and MLI shine (relative advantages)

1) Job alignment and skill emphasis

• QDC is unusually explicit about production-grade quant engineering: Python, C++, and kdb+/q for time-series; hands-on build-outs similar to what investment banks and financial services companies expect from strats/quants. If your daily future includes market data pipelines, liquidity management, analytics services, or credit risk management tooling, QDC maps closely to the real job.
• MLI squarely targets ML for markets—model design, validation, and deployment thinking—making it a good complement for those already in risk management in banking, market risk management, investment research, or quantitative analysis who need structured ML coverage with an exam + final project.

2) Time to impact & format

• Both QDC and MLI run as professional, globally-online programmes—faster than a full master’s degree in finance or data science master program, more focussed than a general investment management course or “finance and investment courses” set. MLI is explicitly seven months part-time; QDC offers tightly scoped, practical modules and assignments you can apply immediately at banks in America, Europe, and Asia.

3) Cost and flexibility

• CQF fees are publicly listed and reflect a premium global brand (split across Level 1/2 or as a full programme). QDC typically positions itself as a more cost-effective route to quantitative analyst courses on the build side; MLI sits in between—specialised ML depth without master’s-level tuition/time. (For CQF’s current fee table, see the official schedule.)

4) Signal vs. specificity

• CQF is a strong, general cqf certificate/cqf qualification signal across quantitative finance; broad, well-known alumni network; quantitative finance course depth across pricing/risk/ML; structured cqf exam points in certain modules; substantial lifelong learning library.
• CFA designation is the gold standard for investment professional tracks (PMs, analysts), with deep coverage of corporate finance, valuation, ethics, financial analysis, and portfolio management. The Sustainable Investing Certificate (formerly ESG) adds formal esg certification for individuals, increasingly relevant for investment management and sustainable investing mandates.
• QDC & MLI give you specificity: QDC = quant dev stack; MLI = ML-for-finance depth. If your target role reads quantitative analyst, quantitative developer, financial technology, or computational finance engineer, these two hit the core skills faster than a broad certificate in finance.

Side-by-side snapshot

Sources: official QDC, MLI, CQF, and CFA Institute pages.

How to choose (by goal and background)

If you’re already coding (Python/C++), like building low-latency analytics, or want to own the bridge from quantitative analysis to production:
→ Choose QDC as your first credential; it’s the tightest fit for strat/quant dev roles and makes you useful on day one (data feeds, market microstructure, order book/liquidity risk management tools). Later you can add CQF for breadth. The Quantitative Developer Certificate

If your gap is ML for finance—feature design, model risk, validation, and explainability—while you stay in quant/risk lines:
→ Choose MLI; the seven-month cadence, exam, and final project give you deliverables you can discuss in interviews. Pairing MLI with a quant MSc or CQF later works well. mlinstitute.org

If you want the broadest quant brand and a full “quantitative finance course” arc (derivatives, risk, ML) in one quantitative finance certificate online:
→ Choose CQF. It’s the “generalist” cqf course with lifelong learning; consider cqf fees and time commitment. cqf.com

If you’re investment-track (buy-side/sell-side research, PM, investment banker):
→ Choose CFA designation (and add the Sustainable Investing Certificate if ESG-heavy). Consider cfa preparation via a cfa online course or the best cfa prep course providers; the ESG certificate exam is 100 MCQs in 2h20. CFA Institute

Typical stacks (what to combine)

• Engineer-heavy quant: QDC → MLI → elective finance certification courses (e.g., options/risk) → later CQF.
• Quant researcher: MLI → CQF (or master in quantitative finance/financial engineering) → specialised seminars.
• Investment analyst / portfolio route: CFA (Levels 1–3) → Sustainable Investing Certificate → selected investment courses (e.g., fixed income, corporate finance). CFA Institute