Ph.D in Applied Math, Coaching Data/AI

Dr. Sharon-Y Veerayah-McGregor

 Train SQL, PowerBI, Python -Data Science, AI, Claude; Engineering Math and GQE

Python Overview

You will be introduced to Python using Jupyter Notebook (cloud/desktop) even if you have never used it. 

Overview Python using Jupyter Notebook

Stop being the “number explainer.” Build the stories, systems, and credibility to lead with clarity and confidence in the age of AI.

Cohort 10

Data & AI Leader, 19yrs of scaling data

Wayfair

Scotiabank

Wunderflats

ZEAL Network SE

Pier Martin

Data & AI leader with 19+ years scaling global teams, Executive coach 

Cohort 9

Cohort 11

Do you lead a data or analytics team and you keep asking yourself:<ul><li>How do I get executives to act on my insights instead of nodding and moving on?</li><li>How do I influence Product, Marketing, and Finance when none of them report to me?</li><li>How do I build real credibility on AI without just demoing tools?</li><li>How do I get my team seen as a trusted partner, not a ticket shop?</li></ul>If those are your questions, you're in the right place. The work is strong. You are just not in the room yet when the decisions get made.Here you fix that by building your own AI Operating System. A working system that keeps supporting you long after the course ends. It remembers every stakeholder you map, drafts in your voice, pressure-tests your decisions before the room, and gets sharper every time you use it. One person, operating like a team.So you turn data into stories executives repeat. You turn AI pilots into business cases that win support. And the system keeps compounding, carrying more of your load as you take on more. You own it for life.

Get in the room with the decisions - taught by a VP who still runs a data team.

How to Make Executives Act on Your Insights

How to Lead AI Change Without the Fear

How to Run a Data Team That Scales

How to Decide What Matters Under Pressure

Folks who are early in their career, focused only on technical execution, or looking to break into data/analytics should not enroll.

This course is NOT FOR everyone

It is designed for data managers, analytics leads, and heads of data already managing teams and facing leadership challenges.

This is NOT a beginner-level technical course

The Overlooked Data Manager: you ship dashboards but get talked over in strategy rooms, and want to be trusted for impact, not output.

The Stretched Head of Data: you lead 5 to 15 people, firefight all week, and need a system that shows control and builds credibility.

The New Data Leader: just promoted from IC, strong technically but light on storytelling and presence, ready to lead in the AI era.

"Python for Beginners" is a foundational course designed for absolute beginners, to introduce you to programming.

Founder and CEO, ScholarU

Roberto Reif

20+ years of Technology and Data Trainings

Summer 2026

Winter 2027

Why should I learn Python?Python is a versatile, beginner-friendly programming language that holds significant value across numerous industries, including web development, data analysis, artificial intelligence, machine learning, and scientific computing. Its broad applicability has created a strong demand for Python skills, opening doors to excellent job prospects and career advancement.About the CourseThis course is designed to prepare you with the essential skills to use Python effectively. You'll dive into fundamental data types such as integers, strings, floats, booleans, lists, dictionaries, sets, and tuples, while mastering core programming concepts like conditionals, loops, functions, and modules. With hands-on exercises, you'll apply what you learn in real-world scenarios.Who Should Take This Course?This course is intended for individuals starting a career in coding, data analysis or data science, as well as professionals seeking to expand their expertise in the field.Course Duration:The course will be delivered live online through Zoom, offering a total of 10 hours of instruction.

Absolute beginners can confidently learn Python!

Get started with Python basics, including syntax and setup

Use comments, variables, and data types effectively

Apply conditionals and booleans to control program flow

Work with lists, ranges, dictionaries, sets, and tuples to manage data

Implement loops and functions to write efficient, reusable code

Import modules and format strings to extend and refine functionality

Gmail account

We will run our live online sessions with Zoom

Zoom account

A thirst for knowledge

Career Changers - Professionals who are transitioning into tech roles and want to learn Python as a first programming language.

Aspiring Data Analysts or Scientists - Individuals looking to build Python skills in data analysis, machine learning, or data science roles.

Excel Users - Experts aiming to enhance their skill sets by mastering advanced and more efficient analysis techniques.

You finish with a pipeline that runs end to end, and with the evidence to say what it does and does not establish.

Author of Machine Learning for Trading

Stefan Jansen

Founder, Applied AI. Systematic investment partner since 2013. CFA.

Build a complete machine-learning trading strategy from an economic idea to a reproducible implementation. Foundations teaches the decisions in the order they arise: formulate the hypothesis, define performance goals and transaction-cost assumptions, assemble point-in-time market data, create a tradable universe, establish a non-ML baseline, design labels and features, train and validate linear and gradient-boosting models, convert forecasts into positions, test them after trading costs, manage risk, and prepare the strategy for deployment.The course uses a running case study of 100 ETFs across equities, bonds, commodities, currencies, and real estate, with daily data from 2006 to 2025. Across 42 units, you work with a complete, runnable project and develop it through concise lectures, focused notebooks, and practical exercises. By the end, you will understand how the full research pipeline fits together and will have modified, checked, and run every major stage.

Build an ML trading pipeline yourself, and interpret the results it produces

A written strategy specification

A data panel you can trust

A validation protocol fixed before there is anything to evaluate

Labels, features and two models built properly

A backtest that charges for everything

A verdict you can defend

You can read and write Python and NumPy in a notebook. The course uses Polars and introduces the operations it relies on.

Python with pandas/polars and NumPy

You have fit and scored a model with scikit-learn. Gradient boosting is taught from scratch and is not assumed.

Basic machine learning

Markets, costs, backtests and portfolio mechanics are taught from the ground up. Prior finance experience is not needed.

No trading background required

Python developers and data scientists moving into systematic trading. You can fit a model; you have never taken one to a costed backtest.

Self-taught traders and readers of the book who want the reasoning behind each decision, in the order the decisions are actually made.

Anyone heading for Research to Production who wants the pipeline-building grounding that course assumes rather than teaches.

From struggling with credit theory to building production scorecards in 4 days!

Credit Risk Modeling Expert | 15+ Years 

Nitin Kumar

Credit Risk Modeling Expert | 15+ Years Building Scorecards for Major Banks 

Cohort 2

Cohort 3

Are you a risk analyst, data scientist, or banking professional struggling to move from credit scoring theory to actual production models? Most courses teach you the formulas, but leave you lost when it comes to implementing real scorecards with messy data, regulatory requirements, and business constraints.This intensive 4-day hands-on workshop takes you from raw credit data to a fully validated, production-ready scorecard using Python. You'll learn the exact techniques used by major banks and financial institutions to assess credit risk, make lending decisions, and monitor portfolio performance.What makes this different: No theory-only lectures. Every session involves live coding, real datasets, and building actual scorecard components. By day 4, you'll have a complete end-to-end scorecard with all validation metrics, monitoring frameworks, and management reports.

Build production-ready credit scorecards using industry-standard techniques 

From theory to building production scorecards in 4 days.

Mastering modelling techniques

Determine the different cut off

Application scorecard development

Python proficiency: You should be comfortable with basic Python syntax, pandas DataFrames, and data manipulation


1.	Python Programming Fundamentals

Its necessary to understand the credit scorecard on broad level. We will recommend to read credit scorecard book by Naeem Siddique

2.	Credit Scorecard Development Theory

Python 3.8+, Jupyter Notebook, libraries (pandas, numpy, scikit-learn, matplotlib, seaborn)

3.  Development environment

Risk Analysts wanting to build scorecards beyond Excel-based approaches

Data Scientists in fintech, banking, or lending who need specialized credit risk skills

Credit Risk Managers seeking to understand the technical implementation of scorecards

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

Data scientists who apply statistical and ML techniques to extract insights from data.

Building technical literacy — understanding APIs, databases, system architecture, and engineering tradeoffs.

Python Overview

What you'll learn

Why this topic matters

You'll learn from

Dr. Sharon-Y Veerayah-McGregor