Ph.D in Applied Math, Coaching Data/AI

Dr. Sharon-Y Veerayah-McGregor

 Train AI, SQL, PowerBI, Python; SSRS; Data Sc., AI, Claude; Eng. Adv. Math 

Numerical Method Finite Difference

Students will learn notations and understand how to translate Derivatives into FDD

Forward Finite Difference

Backward Finite Difference

Learn and develope expertise how to write Numerically and solve using Central Difference

Central Finite DIfference

Master forecasting with hands-on, real-world training. No fluff—just insights that drive ROI and advance your career.


Forecasting That Works in Production

H-E-B

Royal Holloway

Stanley Black & Decker

Dr Valeriy Manokhin, MBA, CQF

ML expert and 7× book author building real-world forecasting systems

Cohort 4

Cohort 5

🔥 Enrollment Open — Limited SpotsThis is a deep, zero-fluff forecasting program built for real business impact, not academic perfection.We cap attendance to keep the experience focused and high-signal (not mass-market). Pricing increases for future cohorts.Built for real-world forecasters<ul><li>Models work in notebooks but fail in production?</li><li>Tutorials ignore operational constraints?</li><li>Want to lead forecasting with clarity and credibility?</li></ul>This course focuses on what works under pressure, at scale, and when decisions carry weight. Forecasting mistakes don’t just fail—they compound and get expensive.You’ll learn to:<ul><li>Build production-grade forecasting systems</li><li>Apply modern Machine Learning where it truly helps</li><li>Quantify uncertainty and communicate risk</li><li>Align forecasts with business outcomes</li></ul>Instructor: Dr. Valeriy Manokhin, MBA, CQF — ML/forecasting expert, 7× book author, with experience building forecasting systems for large multinationals and startups.Not: a pandas crash course, blog rehash, or black-box shortcut.✅ If you’re ready to ship forecasting that earns trust and drives results—join the next Cohort.

Master proven forecasting methods and build systems that drive real results.


Design end-to-end forecasting systems that work reliably in production.

Apply the right methods

Evaluate forecasts correctly.

Fix production failure modes.

Communicate forecasts clearly.

Lead forecasting initiatives.

Comfort with high-school level math, including basic algebra, probability, and statistics. No advanced math or calculus required.

High-school level math

Basic proficiency in Python. You should be able to read and write simple code using tools like pandas, numpy, and scikit-learn.

🐍 Python

No prior forecasting or ML experience needed. All key concepts are introduced from scratch—but taught quickly and practically.

No prior forecasting knowledge needed

For Data Scientists aiming to master real-world forecasting and build robust, scalable models that deliver accurate, actionable busines

For Machine Learning Engineers focused on building reliable, scalable forecasting systems that perform in real-world.

For Researchers, students, and Professors seeking to apply advanced forecasting techniques and contribute to cutting-edge research

Everything I Wish I Knew Before Becoming a Fractional Operator

Helping operators go fractional

Walmart

Saks Fifth Avenue

Ralph Lauren

Gilt

Bellamy Grindl

Fashion Executive | Ex-Walmart, Gap, Gilt | Advisor to 8–9 figure brands

Founding workshop for operators exploring the fractional path.

Founding Cohort

Cohort 2

If you've ever wondered whether you could make the transition from employee to consultant, you're not alone.I've spent the last few years talking with operators, planners, marketers, finance leaders, and executives who are curious about going fractional but aren't sure where to start.For some, it's about creating more flexibility and control over their career. And for many, it's because they enjoy solving problems more than managing teams and corporate politics.The challenge usually isn't expertise. Most people considering this path already have valuable experience.The challenge is confidence.How do you know if you're ready? Where do clients come from? How do relationships become opportunities? What does a sustainable pipeline actually look like?In this workshop, I'll share the lessons, mistakes, and realities I've experienced building Retailytics after a 15+ year career in retail and e-commerce leadership.My goal isn't to convince you to go fractional. It's to help you determine whether it's the right path for you and leave with greater clarity and a roadmap for what comes next.

Gain clarity, confidence, and a practical roadmap for evaluating and building a fractional career.

Determine whether the fractional path is right for your goals and lifestyle

Understand how successful fractional consultants create opportunities

Define your expertise and position yourself effectively

Build confidence pricing your services and communicating your value

Create a practical plan for building a pipeline

Shortcut the learning curve through lessons learned

Experienced operators who want more flexibility, autonomy, and control over their career.

Leaders exploring fractional work after layoffs, restructuring, or career transitions.

Professionals with deep expertise who want to turn experience into consulting income.

The #1 FDE Skill Isn't AI or Coding. It's Storytelling!

Top 1% AI Expert, Ex-@Google, Microsoft

Google

Microsoft

The Wharton School

Intel

Priyanka Vergadia

AI & Cloud leader @Microsoft @Google | Visual Storyteller | TED Speaker

Cohort 1

I've built and hired FDE teams, DevRel and Solutions Engineers for 10+ Years. This workshop teaches what hiring managers look for beyond coding!As a Software Engineer or AI Engineer, you can build agentic workflows and ship AI apps in days. That's not what makes a good FDE.What kills FDE careers early: a CTO wants faster deployments, you jump to CI/CD tooling. The engineering team is uncomfortable you're there, you don't notice. Someone hands you a pre-scoped project, you build without asking if it's the right problem.FDE roles are everywhere: Google, OpenAI, Anthropic are all building this function. But engineers getting those offers aren't winning on technical chops. They ask questions, keep going past the first answer, figure out who does the work, who feels threatened, what's politically doable. Not just tech. People. Org charts. They leave with scoped projects the customer actually wants built.In this workshop you will:<ul><li>Run real discovery conversations with enterprise customers</li><li>Navigate stakeholders who don't all agree and some who don't want you there</li><li>Turn a business goal into a scoped project you can deliver</li></ul>AI and code got you this far. This gets you to FDE.

Go from engineer to FDE with the SCOPE Method: uncover problems, manage politics, define outcomes, influence without authority.

STUDY & CONNECT: Run executive discovery that uncovers the real business problem

OBSERVE: Double-click past surface-level asks to find the real problem

PEOPLE: Navigate stakeholder politics and influence without authority

EXECUTE: Scope projects tied to outcomes and present with confidence

Software Engineers: You ship production systems but have never run customer discovery or scoped a project from a vague exec ask.

Solutions Architects Consultants: You have stakeholder skills but need the framework to own outcomes and shape projects, not just sell them.

Technical Product Managers: You define what gets built but have never embedded with a customer's engineering team to ship it yourself.

Go from strong engineer who freezes in the case round to candidate who scopes, ships and defends a real deployment under pressure.

FDE at Notion, Ex-Google, OReilly Author

Notion

Bloomberg

Scale AI

Accenture

Paden Gayle

Founding FDE @ Notion | O'Reilly Author | Ex-Google, Bloomberg 

You can build. That was never the problem.The problem is the round nobody prepared you for. A vague enterprise problem, no spec, 45 minutes, and a stranger watching you think. You proposed an architecture in the first three minutes because that felt like competence. You never heard back.FDE postings went from 643 to over 5,300 in a year. The role moved faster than the prep did. That round has the lowest pass rate in the loop, around 40%, and at OpenAI case studies and business judgment are half the evaluation. Almost nothing you studied touched it.Most courses would hand you more curriculum. That isn't the gap. You cannot rehearse scoping against a blog post, or prove judgment on a resume.So we do reps. A real customer, broken data, stakeholders who contradict each other, and an ask that's partly impossible. You scope it, build a thin slice, and defend it while I play the VP who wants the wrong thing.Twice, on camera, scored against a published rubric.I wrote the handbook on this role. Check it out in the free resources

Ship Enterprise AI in 14 Days

Scope a vague enterprise ask before you propose a single piece of architecture

Ship a thin slice a customer would actually approve

Prove it works with an eval a customer will believe

Defend your architecture to a hostile stakeholder

Read each company's loop and prep for the one you're interviewing at

Walk into the loop with evidence instead of a resume

That's the bar. You don't need AI experience, a vector database, or a shipped agent. 

You can write Python.

You can be two years in or ten, career-switching or already an FDE. What matters is that you'd rather build something graded 

You want reps, not lectures.

Your scoping gets reviewed and discussed. The people who improve most are the ones who get it wrong in week one and fix it in week two.

You're ready to work in the open.

<blockquote>Engineers targeting FDE roles You can build. Now prove the judgment half of the loop nobody trained you for, on a real deployment, scored.</blockquote>

Solution engineers moving into FDE You have the customer instincts already. Get the technical half graded by someone who runs the deployment

FDEs in their first year You got the job and scoping still feels improvised. Get reps on broken data before you do it in front of a customer.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

AI for GTM

Sales

Customer Success

GTM Engineering

Everyone

Founders

Operators

Data Scientists

Business Analysts

User Researchers

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Numerical Method Finite Difference

What you'll learn

Why this topic matters

You'll learn from

Dr. Sharon-Y Veerayah-McGregor