For data scientists

Skip the intro. Learn from the people who wrote the book.

You're fed up of yet another 'Intro to Pandas' course. Get the cookbooks and deep dives from the authors already on your desk, from $10 a month.

Free for 7 days. Valid card required. Cancel anytime.

Galli
Cover image for Python Feature Engineering Cookbook
Labonne
Cover image for LLM Engineer's Handbook
Raschka
Cover image for Machine Learning with PyTorch and Scikit-Learn
Ayd
Cover image for Pandas Cookbook
McMahon
Cover image for Machine Learning Engineering with Python

Sound familiar?

You've taken this course before

You've done the one with the clean CSV and the fill-in-the-blank cell. Here's the same lesson, the way our authors teach it.

The course you've taken

Intro to Machine Learning · Lesson 4 · recorded 2016

import pandas as pd
from sklearn.cross_validation import train_test_split1
df = pd.read_csv("iris.csv")2
X, y = df.drop(columns="species"), df["species"]
model = ____(n_estimators=100)3
model.fit(X, y)
The same lesson on ExpertEdge

Cookbook recipe · current edition

from sklearn.model_selection import train_test_split1
orders = pd.read_parquet("orders.parquet")
df = orders.merge(customers, on="customer_id")2
df["spend"] = df["spend"].fillna(df["spend"].median())
X_train, X_test, y_train, y_test = train_test_split(
X, y, stratify=y
)
model = RandomForestClassifier(class_weight="balanced")3
model.fit(X_train, y_train)
  1. 1

    A module removed in 2018

    Everybody else

    The video still teaches sklearn.cross_validation, years after it was dropped.

    ExpertEdge

    The library tracks new editions. Every title shows when it was published.

  2. 2

    Data that cleaned itself

    Everybody else

    150 rows, no nulls, no joins. Nothing like the tables you get at work.

    ExpertEdge

    Cookbooks start where real data does, with joins, gaps and types to fix.

  3. 3

    One blank to fill in

    Everybody else

    You type one word, the cell turns green, and you still can't start from an empty notebook.

    ExpertEdge

    Every recipe is written out in full, by someone who does this for a living.

Where does the code run?

No training wheels. You run it on your own machine.

Browser labs set everything up for you, then vanish when the course ends. Here you install, run and version your own work, the way you do at your job.

Everybody else
labs.course-site.com/session/4821

Session ends in 00:59:32

  • pandas 1.1 preinstalled
  • Files reset when you close the tab
  • No git, no terminal
ExpertEdge

~/work/churn · zsh

$ python -m venv .venv && source .venv/bin/activate
$ pip install -r requirements.txt
$ jupyter lab
$ git commit -am "Churn model, v2"

Your laptop, your versions, your git history. Everything you build is still there next week.

Why does this keep happening?

Most courses are made for beginners

Marketplaces earn per enrolment. Beginners are the biggest market, so that's what gets made. Most of the ExpertEdge library is written for people past the basics.

Best data science courses of 2026

Class Central

67%
20%
13%

ExpertEdge data science, ML and AI titles

30%
61%
9%
  • Beginner
  • Intermediate
  • Advanced
Most courses are made for beginners
SourceBeginnerIntermediateAdvanced
Best data science courses of 20261032
ExpertEdge data science, ML and AI titles15030143

Who's teaching?

Learn from the authors already on your desk

Cookbooks and deep dives, written by the people who built the tools or use them every day.

  • Cover image for Machine Learning with PyTorch and Scikit-Learn
    Sebastian Raschka, author of Machine Learning with PyTorch and Scikit-Learn

    Sebastian Raschka

    247k

    The PyTorch and scikit-learn course many data scientists learned from.

  • Cover image for LLM Engineer's Handbook
    Maxime Labonne, author of LLM Engineer's Handbook

    Maxime Labonne

    70k

    How LLMs get built, tuned and shipped, from someone who does it.

    3 titlesProfile
  • Cover image for Polars Cookbook
    Yuki Kakegawa, author of Polars Cookbook

    Yuki Kakegawa

    Polars, the fast new DataFrame library, one recipe at a time.

  • Cover image for Machine Learning Engineering with Python
    Andrew P. McMahon, author of Machine Learning Engineering with Python

    Andrew P. McMahon

    The part most courses skip: getting a model into production.

Where do I go next?

Pick a target and we'll put the skills in order

No more 47 bookmarked tutorials. Set Data Scientist as your target and ExpertEdge lays out the skills it asks for, one at a time.

  1. The basics

    Where you are

  2. Data Analysis Workflows & Tools

  3. Data Ingestion & Transformation (ETL)

  4. Data Visualization Tools & Techniques

  5. Data Warehousing & Big Data

  6. Database Technologies & Administration

  7. Statistical Analysis & Predictive Modeling

  8. Supervised & Unsupervised Learning

  9. Data Scientist

    The goal

What about AI?

The job ad changed. You don't have to.

You've got the stats, the SQL and the models. Now every posting wants RAG and agents too. Learn the new stuff without starting over.

The posting you saw this week

jobs.example.com/senior-data-scientist

Senior Data Scientist

Posted this week

  • Python and SQL
  • Statistics and experiments
  • Machine learning
  • RAG and LLMs1
  • AI agents2
  • Checking AI-written code3

188

AI titles published in 2025 and 2026

  1. 1

    RAG and LLMs

    Everybody else

    A chatbot in ten minutes, until it meets your data.

    ExpertEdge

    RAG from First Principles, for when it has to actually work.

  2. 2

    AI agents

    Everybody else

    Recorded before the framework changed again.

    ExpertEdge

    Building AI Agents with Python and LangChain, published 2026.

  3. 3

    Checking AI-written code

    Everybody else

    Teaches the prompt, then skips the checking.

    ExpertEdge

    Check it with the stats you know. AI-Driven Data Modeling Using ChatGPT and Copilot shows how.

What's the catch?

What ExpertEdge isn't

  • A bootcamp

    No cohort and no live classes. You learn in your own time, at your own pace.

  • A messy-data sandbox

    We don't host datasets to practise on. Bring your own projects for that.

  • Another intro course

    We assume you know the basics. The library starts where most courses stop.

We won't replace real projects. We'll make sure the next thing you learn is worth your time.

And the price?

One price, everything included

Monthly

Billed monthly

$15per month

Everything, month to month. Cancel anytime.

What's included

  • Every data science, ML and AI title
  • The whole 3,400+ course library
  • Skills and a passport that stay yours
Start Free Trial

Annual

Billed yearly ($120)

$10per month

Everything for a third less, paid once a year.

What's included

  • Every data science, ML and AI title
  • The whole 3,400+ course library
  • Skills and a passport that stay yours
Start Free Trial

No setup fees, no minimum seats. Invoicing available on plans of 20+ seats or more.

Comparing options? Read how ExpertEdge stacks up against Udemy Business, LinkedIn Learning, Coursera and edX in our comparisons

Our blog

Built by people who know how great teams run L&D programs that work at scale

Still not convinced ExpertEdge is the right fit for you? Review some of our thinking on enterprise learning, content strategy and more. See if we align via the full blog.

Content Strategy

The B2B learning content stack most enterprises miss

Enterprise L&D teams have approached content procurement the wrong way for a decade. What a mature B2B learning content stack looks like, and the layers most enterprises are missing.

Oli Huggins

Oli Huggins

CEO and Founder

Comparisons

ExpertEdge vs Coursera and edX for B2B learning

A side-by-side of Coursera, edX and ExpertEdge for enterprise learning content: what each does well, where each falls short, and how to choose.

Oli Huggins

Oli Huggins

CEO and Founder