AI and Data Scientist Roadmap

Step by step guide to becoming a modern AI and Data Scientist in 2026

Step 1

Mathematics

Algebra

Calculus

Linear Algebra

Probability

Statistics

Discrete Mathematics

Mathematics for Machine Learning

Step 2

Statistics

Descriptive Statistics

Inferential Statistics

Statistical Inference

Bayesian Statistics

Experimental Design

Statistical Thinking in Python

Statistical Modeling

Step 3

Coding

Python

Java

JavaScript

R

C/C++

SQL

NoSQL

Step 4

Exploratory Data Analysis (EDA)

Data Cleaning

Data Visualization

Statistical Analysis

Hypothesis Testing

Feature Engineering

Data Wrangling

Interactive Data Visualization

Step 5

Machine Learning

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Neural Networks

Model Deployment

Advanced Machine Learning

Machine Learning Projects

Step 6

Deep Learning

Convolutional Neural Networks

Recurrent Neural Networks

Generative Adversarial Networks

Autoencoders

Transfer Learning

AI Ethics

Deep Reinforcement Learning

Step 7

MLOps

Model Versioning

Data Versioning

Model Monitoring

Continuous Integration

Continuous Deployment

MLOps Pipelines

MLOps for Machine Learning Operations