ISODS suggests the following online courses to support the exams:

Probability (P)
An Intuitive Introduction to Probability (University of Zurich)
Introduction to Probability and Data with R (Duke University)
Introduction to Statistics (Stanford University) - in combination with STAT exam

Statistics (STAT)
Introduction to Statistics (Stanford University) - in combination with P exam

Linear Algebra (LA)
Essential Linear Algebra for Data Science  (University of Colorado Boulder)
Mathematics for Machine Learning: Linear Algebra (Imperial College London)

Calculus (CAL) 
Single Variable Calculus (University of Pennsylvania)
Calculus: Single Variable Part 1 - Functions (University of Pennsylvania)
Calculus: Single Variable Part 2 - Differentiation (University of Pennsylvania)
Calculus: Single Variable Part 3 - Integration (University of Pennsylvania)
Calculus: Single Variable Part 4 - Applications (University of Pennsylvania)

Predictive Analytics (PA)
Generalized Linear Models and Nonparametric Regression (University of Colorado Boulder)
Modern Regression Analysis in R (University of Colorado Boulder)
ANOVA and Experimental Design (University of Colorado Boulder)
Regression Models

Database Management (DM) 
Database Management Essentials (University of Colorado System)

Big Data Analytics (BDA) 
Big Data Analysis with Scala and Spark (École Polytechnique Fédérale de Lausanne) 
Big Data Analysis with Scala and Spark (Scala 2 version) (École Polytechnique Fédérale de Lausanne)

Object-oriented Programming (PRG)
Python Classes and Inheritance (University of Michigan)
Python 3 Programming Specialization (University of Michigan)

Data Structures and Algorithms (DSA)
Data Science Foundations: Data Structures and Algorithms Specialization (University of Colorado Boulder)
Data Structures and Algorithms Specialization (University of San Diego)

Time Series (TS)
Practical Time Series Analysis (The State University of New York)
Sequences, Time Series and Prediction (DeepLearning.AI)

Machine Learning (ML)
Machine Learning (Stanford University)

Deep Learning 1 (DL1)
Neural Networks and Deep Learning (DeepLearning.AI)
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (DeepLearning.AI)
Convolutional Neural Networks (DeepLearning.AI)

Deep Learning 2 (DL2) 
Sequence Models (DeepLearning.AI)
Natural Language Processing Specialization (DeepLearning.AI)

Reinforcement Learning (RL)
Reinforcement Learning Specialization (University of Alberta)