AI-Assisted Coding for Data Scientists
Gabriela de Queiroz
Director of AI | Ex-Microsoft, IBM
Catherine Nelson
2x O'Reilly Author | Former Principal DS
Use AI to move faster without sacrificing rigor, reproducibility, or judgment.
Founding cohort pricing: $1,295
Special inaugural pricing for our first cohort. Future cohorts will be priced higher.
___
AI can write data science code in minutes. But how do you know the results are right?
As a data scientist, you're still accountable for the analysis. You need to know the data is reliable, the methods are appropriate, and the results can be reproduced.
In this course, you will learn how to use AI coding tools to produce more trustworthy analysis, faster.
You’ll learn how to validate AI-generated code, document your decisions, build reproducible workflows, and create evidence you can show to your stakeholders.
Instead of using AI simply to generate more code, you'll learn how to prove your work while still using AI to accelerate your workflow. You'll be able to use AI coding tools with the judgment, rigor, and accountability that professional data science requires.
What you’ll learn
Use AI to accelerate data science work while producing analysis you can validate, explain, reproduce, and defend.
Build a practical mental model of how LLMs work, hallucinate, and respond to context.
Recognize plausible but incorrect output and identify when additional scrutiny is required.
Use AI across exploratory analysis, data cleaning, modeling, visualization, and reporting.
Break projects into reviewable, verifiable steps instead of asking AI to complete everything at once.
Write assertions and validation functions for joins, cleaning, features, and model outputs.
Turn verification into evidence you can show to a manager, stakeholder, or regulator.
Record assumptions, alternatives, limitations, and the reasoning behind key choices.
Separate AI-generated suggestions from the decisions you make and own.
Guide AI to create functions and scripts that can be rerun, reviewed, and adapted.
Make every chart and result traceable to its source data and transformation steps.
Develop reusable prompts, validation checks, and review patterns you can apply to future projects.
Establish practical guidelines for privacy, security, reliability, and when not to use AI.
Learn directly from Gabriela & Catherine
Gabriela de Queiroz
Director of AI | Ex-Microsoft, IBM
Catherine Nelson
Author, "Software Engineering for Data Scientists" | Former Principal DS @SAP
Who this course is for
Data scientists who want to use AI coding agents without sacrificing rigor or professional judgment.
Data analysts and ML practitioners expected to use AI at work who need to validate, document, and explain their results.
Data professionals who learned before AI coding agents and want to modernize their analysis and reporting workflows.
Prerequisites
Access to an AI-powered coding agent such as Claude Code, Codex, or Copilot.
You’ll use the coding agent throughout the course to build, analyze, and iterate on hands-on projects.
Working knowledge of statistics and ML fundamentals
We’ll build on core statistics and ML concepts so we can focus on practical AI-assisted data science workflows.
Comfortable working with Python, pandas, Git, and Github
You should be able to read and modify Python code, work with pandas, and use basic Git/GitHub workflows.
What's included
Live sessions
Learn directly from Gabriela de Queiroz & Catherine Nelson in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Showcase your completion with your employer, network, or on LinkedIn.
Reusable course resources
Leave with validation checks, workflow patterns, and a practical playbook you can reuse on future projects.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
8 live sessions • 4 lessons • 4 projects
Week 1
Oct
13
Lecture 1: Understanding AI Coding Tools
Tue 10/136:00 PM—7:00 PM (UTC)Oct
16
Office Hours with Gabriela and Catherine
Fri 10/165:00 PM—6:00 PM (UTC)
L1 - Understanding AI coding tools
Week 2
Oct
20
Lecture 2: Validating AI-Generated Code
Tue 10/206:00 PM—7:00 PM (UTC)Oct
23
Optional: Office Hours with Gabriela and Catherine
Fri 10/235:00 PM—6:00 PM (UTC)Optional
L2 - Validating AI-Generated Code: EDA and Data Cleaning
Schedule
Live sessions
1-3 hrs / week
Tue, Oct 13
6:00 PM—7:00 PM (UTC)
Fri, Oct 16
5:00 PM—6:00 PM (UTC)
Tue, Oct 20
6:00 PM—7:00 PM (UTC)
Projects
1-4 hrs / week
Async content
1-2 hrs / week
You get lifetime access to the recorded content, so that you can learn at your own pace.
Books by Catherine Nelson
Frequently asked questions
Maven for Teams
Reimbursement
Get your company to pay
Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedTeam discount
Learn with your teammates
Save 20%+ when 2 or more teammates enroll in the same cohort.
Save 20%+ with a teamPrivate cohort
Run a cohort for your org
A dedicated cohort with a custom schedule and curriculum, tailored to your team.
Book a private cohort$1,295
USD