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Mathematics courses 2,879

Learn Mathematics online: university courses, full YouTube courses, and courses with free certificates, from the IITs, MIT, Harvard, Google, Microsoft and more.

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Showing 2,879 courses

Harvard University

Data Science: R Basics

4.4273 ratings

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

  • edX
  • 8 weeks, 2 - 3 hours per week
  • Self-paced
  • Free to audit
  • Paid certificate
  • Beginner

Stanford Online

Cryptography I

4.84,483 ratings

Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared…

Partner
  • Coursera
  • 23 hours
  • Self-paced
  • Paid certificate

Harvard University

Data Science: Capstone

4.575 ratings

Show what you've learned from the Professional Certificate Program in Data Science.

  • edX
  • 2 weeks, 2 - 3 hours per week
  • Self-paced
  • Free to audit
  • Paid certificate
  • Beginner

Stanford Online

Divide and Conquer, Sorting and Searching, and Randomized Algorithms

4.85,339 ratings

The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).

Partner
  • Coursera
  • 4 weeks of study, 4-8 hours/week
  • Self-paced
  • Paid certificate

Google

Google AI for Anyone

4.6111 ratings

A course for anyone to learn what AI is and how it works.

  • edX
  • 4 weeks, 3 - 4 hours per week
  • Self-paced
  • Free to audit
  • Paid certificate
  • Beginner

Microsoft

Add decision logic to your code in C#

Learn to branch your code's execution path by evaluating Boolean expressions.

  • Microsoft Learn
  • 45 minutes
  • Self-paced
  • Free course
  • Free badge
  • Beginner

upGrad

Linear Algebra for Analysis

Free Linear Algebra Course will help you learn analytics concepts along with vectors, linear transformations, matrices, eigenvalues and eigenvectors through linear algebra classes

Partner
  • upGrad
  • 5 hours
  • Self-paced
  • Free course

Harvard University

Data Science: Inference and Modeling

4.454 ratings

Learn inference and modeling, two of the most widely used statistical tools in data analysis.

  • edX
  • 8 weeks, 2 - 3 hours per week
  • Self-paced
  • Free to audit
  • Paid certificate
  • Beginner

MIT OpenCourseWare

Prediction: Machine Learning and Statistics

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific wor…

  • MIT
  • Self-paced
  • Free course
  • Advanced

Stanford Online

Graph Search, Shortest Paths, and Data Structures

4.82,001 ratings

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social…

Partner
  • Coursera
  • 4 weeks of study, 4-8 hours/week
  • Self-paced
  • Paid certificate

The Georgia Institute of Technology

Computing in Python I: Fundamentals and Procedural Programming

4.7103 ratings

Learn the fundamentals of computing in Python, including variables, operators, and writing and debugging your own programs.

  • edX
  • 5 weeks, 9 - 10 hours per week
  • Self-paced
  • Free to audit
  • Paid certificate
  • Beginner

MIT OpenCourseWare

Advanced Natural Language Processing

This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of th…

  • MIT
  • Self-paced
  • Free course
  • Advanced

Udacity

Eigenvectors and Eigenvalues Free Course

4.86 ratings

Learn how to calculate eigenvalues and eigenvectors and why they are important for AI applications.

Partner
  • Udacity
  • 40 minutes
  • Self-paced
  • Free course

MIT OpenCourseWare

Geometric Folding Algorithms: Linkages, Origami, Polyhedra

This course focuses on the algorithms for analyzing and designing geometric foldings. Topics include reconfiguration of foldable structures, linkages made from one-dimensional rods connected by hinges, folding two-dimensional paper (origami), and unfolding and folding three-dimensional polyhedra. A…

  • MIT
  • Self-paced
  • Free course
  • Advanced

Stanford Online

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

4.81,278 ratings

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Partner
  • Coursera
  • 4 weeks of study, 4-8 hours/week
  • Self-paced
  • Paid certificate

The University of Michigan

Programming for Everybody (Getting Started with Python)

4.7182 ratings

This course is a "no prerequisite" introduction to Python Programming. You will learn about variables, conditional execution, repeated execution and how we use functions. The homework is done in a web browser so you can do all of the programming assignments on a phone or public computer.

  • edX
  • Self-paced
  • Free to audit
  • Paid certificate
  • Beginner

Stanford Online

Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

4.8832 ratings

The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).

Partner
  • Coursera
  • 4 weeks of study, 4-8 hours/week
  • Self-paced
  • Paid certificate

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