Cluster Analysis in Data Mining (Coursera)

Coursera

Northeastern University

Data Visualization with Python & R for Engineers (Coursera)

Statistics & Data Analysis Data Science

The primary objective of this course is to offer students an opportunity to learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, manufacturing, and related applications. This course covers basics of data mining and visualization, and Python. It also introduces students to static visualization charts and techniques that reveal information, patterns, interactions.

Aug 3rd 2026

4 Weeks

Python Data Mining Data Visualization

Details

Coursera

University of Minnesota

Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)

Data Science

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Jul 20th 2026

4 Weeks

Statistics Machine Learning Data Mining

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Coursera

University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining (Coursera)

Statistics & Data Analysis Data Science

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Jul 20th 2026

4 Weeks

Algorithms Data Mining Data Analysis

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Coursera

EIT Digital

Approximation Algorithms (Coursera)

Computer Science

Many real-world algorithmic problems cannot be solved efficiently using traditional algorithmic tools, for example because the problems are NP-hard. The goal of this course is to become familiar with important algorithmic concepts and techniques needed to effectively deal with such problems. These techniques apply when we don't require the optimal solution to certain problems, but an approximation that is close to the optimal solution. We will see how to efficiently find such approximations.

Jul 24th 2026

4 Weeks

Algorithms Approximation Algorithms LP Relaxation

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Coursera

Universitat Autònoma de Barcelona

Sistemas Digitales: De las puertas lógicas al procesador (Coursera)

CS: Theory Engineering

En este curso aprenderemos los fundamentos del diseño de los circuitos digitales actuales, siguiendo una orientación eminentemente práctica. A diferencia de otros cursos más "clásicos" de Circuitos Digitales, nuestro interés se centrará más en el Sistema que en la Electrónica que lo sustenta. Este enfoque nos permitirá sentar las bases del diseño de Sistemas Digitales complejos.

Aug 3rd 2026

5-12 Weeks

Algorithms Engineering Digital Systems

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Coursera

National Taiwan University

Operations Research (2): Optimization Algorithms (Coursera)

Computer Science

Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc. The series of courses consists of three parts, we focus on deterministic optimization techniques, which is a major part of the field of OR. As the second part of the series, we study some efficient algorithms for solving linear programs, integer programs, and nonlinear programs.

Aug 3rd 2026

5-12 Weeks

Algorithms Optimization Business Analytics

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Coursera

Sungkyunkwan University - SKKU

Machine Learning Algorithms (Coursera)

CS: Software Engineering Data Science

In this course you will: understand the naïve Bayesian algorithm; understand the Support Vector Machine algorithm; understand the Decision Tree algorithm; understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.

Aug 3rd 2026

4 Weeks

Machine Learning Clustering Support Vector Machines

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Coursera

University of California, San Diego

Finding Hidden Messages in DNA (Bioinformatics I) (Coursera)

Sci: Biology & Life Sciences Health & Society

This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat. In the first half of the course, we investigate DNA replication, and ask the question, where in the genome does DNA replication begin? We will see that we can answer this question for many bacteria using only some straightforward algorithms to look for hidden messages in the genome.

Jul 20th 2026

5-12 Weeks

Biology Algorithms DNA

Details

Coursera

Johns Hopkins University

Algorithms for DNA Sequencing (Coursera)

Statistics & Data Analysis Data Science

We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.

Jul 20th 2026

4 Weeks

Python Algorithms DNA

Details

Coursera

The University of Sydney

Blockchain Scalability and its Foundations in Distributed Systems (Coursera)

CS: Information & Technology

Blockchain promises to disrupt industries once it will be efficient at large scale. In this course, you will learn how to make blockchain scale. You will learn about the foundational problem of distributed computing, consensus, that is key to create blocks securely. By illustrating limitations of mainstream blockchains, this course will indicate how to improve the technology in terms of security and efficiency. In particular, this course will help you: understand security vulnerabilities of mainstream blockchains; design consensus algorithms that tolerate attacks, and; design scalable blockchain systems.

Aug 3rd 2026

5-12 Weeks

Algorithms Networks Computer Science

Details

Coursera

École normale supérieure

Approximation Algorithms Part II (Coursera)

CS: Software Engineering CS: Programming

This is the continuation of Approximation algorithms, Part 1. Here you will learn linear programming duality applied to the design of some approximation algorithms, and semidefinite programming applied to Maxcut. By taking the two parts of this course, you will be exposed to a range of problems at the foundations of theoretical computer science, and to powerful design and analysis techniques.

Aug 3rd 2026

4 Weeks

Algorithms Computer Science Linear Programming

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Coursera

Columbia University

Decision Making and Reinforcement Learning (Coursera)

Computer Science

This course is an introduction to sequential decision making and reinforcement learning. We start with a discussion of utility theory to learn how preferences can be represented and modeled for decision making. We first model simple decision problems as multi-armed bandit problems in and discuss several approaches to evaluate feedback. We will then model decision problems as finite Markov decision processes (MDPs), and discuss their solutions via dynamic programming algorithms. We touch on the notion of partial observability in real problems, modeled by POMDPs and then solved by online planning methods.

Aug 3rd 2026

5-12 Weeks

Algorithms Machine Learning Monte Carlo Method

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