# Essential Math for Data Science

- 37 Courses | 45h 38m 18s
- 1 Lab | 4h

**Track 1: Introduction to Math**

In this track of the Essential Math for Data Science Skillsoft Aspire journey, you will focus on the fundamentals of linear algebra and calculus. This includes discrete math concepts and their implementations, theoretical and practical guide to calculus, exploring linear algebra, and matrix operations.

- 11 Courses | 12h 45m 5s
- 1 Lab | 4h

**Track 2: Statistics and Probability**

In this track of the Essential Math for Data Science Skillsoft Aspire journey, you will acquire a deeper understanding of probability and statistical concepts including probability distributions, various types of statistical tests, and hypothesis testing. You will deep dive into understanding conditional probability concepts that forms the crux of naïve Bayes classification algorithms.

- 13 Courses | 17h 23m 44s

**Track 3: Math Behind ML Algorithms**

In this track of the Essential Math for Data Science Skillsoft Aspire journey, the focus will be on math applied in various machine learning algorithms. You will understand the intuition behind these algorithms along with math used in their optimization/loss/cost functions. You will understand the math behind regression algorithms, decision trees, distance-based models, kernel methods and SVM and neural networks.

- 10 Courses | 12h 50m 7s

**Track 4: Advanced Math**

In this track of the Essential Math for Data Science Skillsoft Aspire journey, the focus will be on math behind advanced concepts such as principal component analysis, recommendation systems, and gradient descent.

- 3 Courses | 2h 39m 22s

**COURSES INCLUDED**

**Math & Optimizations: Introducing Sets & Set Operations**

**Math & Optimizations: Introducing Graphs & Graph Operations**

**Math & Optimizations: Solving Optimization Problems Using Linear Programming**

**Math & Optimizations: Solving Optimization Problems Using Integer Programming**

**Calculus: Getting Started with Derivatives**

**Calculus: Derivatives with Linear and Quadratic Functions & Partial Derivatives**

**Calculus: Understanding Integration**

**Essential Maths: Exploring Linear Algebra**

**Matrix Decomposition: Getting Started with Matrix Decomposition**

**Matrix Decomposition: Using Eigendecomposition & Singular Value Decomposition**

**Final Exam: Introduction to Math**

**COURSES INCLUDED**

**Core Statistical Concepts: An Overview of Statistics & Sampling**

**Core Statistical Concepts: Statistics & Sampling with Python**

**Probability Theory: Getting Started with Probability**

**Probability Theory: Understanding Joint, Marginal, & Conditional Probability**

**Probability Theory: Creating Bayesian Models**

**Probability Distributions: Getting Started with Probability Distributions**

**Probability Distributions: Uniform, Binomial, & Poisson Distributions**

**Probability Distributions: Understanding Normal Distributions**

**Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing**

**Statistical & Hypothesis Tests: Using the One-sample T-test**

**Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests**

**Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis**

**Final Exam: Statistics and Probability**

**COURSES INCLUDED**

**Regression Math: Getting Started with Linear Regression**

**Regression Math: Using Gradient Descent & Logistic Regression**

**The Math Behind Decision Trees: An Exploration of Decision Trees**

**Distance-based Models: Overview of Distance-based Metrics & Algorithms**

**Distance-based Models: Implementing Distance-based Algorithms**

**Support Vector Machine (SVM) Math: A Conceptual Look at Support Vector Machines**

**Support Vector Machine (SVM) Math: Building & Applying SVM Models in Python**

**Neural Network Mathematics: Understanding the Mathematics of a Neuron**

**Neural Network Mathematics: Exploring the Math behind Gradient Descent**

**Final Exam: Math Behind ML Algorithms**

**COURSES INCLUDED**

**ML & Dimensionality Reduction: Performing Principal Component Analysis**

**Recommender Systems: Under the Hood of Recommendation Systems**

**Final Exam: Advanced Math**

**EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE TRACKS**

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