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)