Evidence-Based Data Science

🔥 Enrollment Now Open — Limited Spots for Cohort 1

This is the most rigorous, zero-fluff data science course available—designed for real-world performance, not leaderboard optics. It’s built to help you avoid costly ML failures and deliver projects that are validated, trusted, and decision-ready.

Enrollment for Cohort 1 is now open.

Spots are limited and pricing may increase without notice. We cap enrollment intentionally to keep this a focused, high-impact experience—not a mass-market funnel.

🔒 Secure your seat and build the skillset top ML teams use to prevent failure and ship models that actually work.

🧠 Built for Real-World Data Scientists, Technical and Business Leaders

✅ Tired of models that look great in notebooks but fail in production?

✅ Want to avoid being the next Zillow or AI ethics headline?

✅ Need to build systems that decision-makers trust—without overselling?

This course isn’t about trendy frameworks or flashy benchmarks. It’s about mastering the core principles of evidence-based modeling—rigor, reproducibility, and relevance—so your models deliver value, not damage.

The real world isn’t Kaggle. Mistakes here don’t just fail—they mislead, waste budgets, and erode trust.

💡 You'll Learn How To:

💎 Audit your ML pipelines for hidden risks like leakage and metric misuse

💎 Design projects with testable hypotheses and stakeholder alignment

💎 Evaluate models with calibration, prediction intervals, and external validation

💎 Communicate uncertainty without undercutting credibility

💎 Ship reproducible workflows that survive peer review and executive scrutiny

Each session blends the theory that matters with practical tools you can deploy immediately—whether you're building models in production or reviewing them at the leadership table.

By the end, you won’t just understand what went wrong in failed projects.

You’ll know how to build ones that don’t.

👨‍🏫 Meet Your Instructor: Dr. Valeriy Manokhin

Dr. Valeriy Manokhin is an internationally recognized expert in machine learning, forecasting, and uncertainty quantification. He’s known for bridging academic rigor with real-world impact—turning research into results that scale in production.

✅ Designed evidence-based ML systems for Fortune 500 companies and high-growth startups

✅ Authored 4 bestselling books on forecasting, uncertainty, and applied machine learning

✅ Published peer-reviewed research in top ML journals on Machine Learning, Forecasting and Conformal Prediction

✅ Outperformed leading consulting firms in competitive AI tenders

✅ Delivered mission-critical DS/ML systems for global enterprises

✅ Designed industry-first training on Conformal Prediction and Modern Forecasting

✅ Helped teams avoid multi-million dollar mistakes by upgrading their modeling rigor

His evidence-based teaching is trusted by senior data scientists, ML engineers, researchers, and leaders across industries.

🌍 Trusted by Professionals From:

Amazon, Meta, Google, Morgan Stanley, Bayer, NTT Data, Spotify, Capgemini, Daybreak AI, and more—plus PhD researchers and faculty from top institutions like UCL, UBC, TU Munich, and KTH.

Students range from principal data scientists and ML engineers to analytics directors, researchers, and technical founders.

❌ What This Course Is Not

🚫 Not a Python 101 or sklearn tutorial

🚫 Not a repackaged blog post or buzzword bingo

🚫 Not a black-box shortcut or AI hype machine

This is a practical, strategic, zero-hype program for those who want to lead with rigor, credibility, and clarity in data science.

✅ Final Call

If you're ready to stop shipping untrustworthy models and start building AI and ML systems that withstand scrutiny, this is your moment.

Cohort 1 is now open.

Seats are limited. Price may rise without notice. 📌 Enroll now to lock in your spot.