10-step roadmap

Data Scientist / ML

You go further than describing data. You build models that predict things, like which customers might cancel their subscription.

Data Scientist / ML roadmap

Follow the line, from first step to goal.

Python
Start
Python is the main language for data science. It's easy to read, beginner-friendly, and packed with tools built for this work.
Math foundations
Learn the basics of statistics, probability, and a bit of algebra. This is the logic behind how models learn.
Working with data (pandas)
Loading, cleaning, and reshaping datasets so they're ready to analyze or feed into a model.
Data visualization
Charting your data to spot patterns and check your assumptions before building anything complex.
Machine learning fundamentals
Learn how a computer 'learns' patterns from past examples. It uses those patterns to predict new data, instead of following fixed rules.
Key algorithms
Learn a handful of common techniques, like decision trees and regression. They solve most real-world problems.
Feature engineering
Choose and shape the right inputs, so your model has the best clues to learn from. You usually do this as you build the model.
Model evaluation
Checking whether your model is actually good, and not just memorizing the examples you gave it.
Deep learning basics
Get an introduction to neural networks. This technique powers image recognition and modern AI chatbots.
Deploying a model
Goal
Turning your model from a notebook experiment into something a real app can actually use.